sha
stringlengths 40
40
| text
stringlengths 1
13.4M
| id
stringlengths 2
117
| tags
sequencelengths 1
7.91k
| created_at
stringlengths 25
25
| metadata
stringlengths 2
875k
| last_modified
stringlengths 25
25
| arxiv
sequencelengths 0
25
| languages
sequencelengths 0
7.91k
| tags_str
stringlengths 17
159k
| text_str
stringlengths 1
447k
| text_lists
sequencelengths 0
352
| processed_texts
sequencelengths 1
353
|
---|---|---|---|---|---|---|---|---|---|---|---|---|
e160738e18e8afad80ac61f72d18099aec1029e6 |
# Dataset of soleil (Fire Emblem)
This is the dataset of soleil (Fire Emblem), containing 156 images and their tags.
The core tags of this character are `pink_hair, long_hair, hairband, breasts, pink_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 156 | 137.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/soleil_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 156 | 86.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/soleil_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 334 | 174.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/soleil_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 156 | 124.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/soleil_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 334 | 231.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/soleil_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/soleil_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, hetero, penis, 1boy, solo_focus, nipples, sex, blush, vaginal, spread_legs, censored, cum_in_pussy, navel, smile, medium_breasts, open_mouth, completely_nude, large_breasts |
| 1 | 29 |  |  |  |  |  | 1girl, solo, smile, simple_background, gloves, white_background, armor, looking_at_viewer, open_mouth, sword |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hetero | penis | 1boy | solo_focus | nipples | sex | blush | vaginal | spread_legs | censored | cum_in_pussy | navel | smile | medium_breasts | open_mouth | completely_nude | large_breasts | solo | simple_background | gloves | white_background | armor | looking_at_viewer | sword |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------|:-------|:-------------|:----------|:------|:--------|:----------|:--------------|:-----------|:---------------|:--------|:--------|:-----------------|:-------------|:------------------|:----------------|:-------|:--------------------|:---------|:-------------------|:--------|:--------------------|:--------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 29 |  |  |  |  |  | X | | | | | | | | | | | | | X | | X | | | X | X | X | X | X | X | X |
| CyberHarem/soleil_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T17:37:01+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T18:15:31+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of soleil (Fire Emblem)
===============================
This is the dataset of soleil (Fire Emblem), containing 156 images and their tags.
The core tags of this character are 'pink\_hair, long\_hair, hairband, breasts, pink\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
5ab65bb356fe464051d4558ff94b656d9fc747fe |
# Dataset of ivy (Fire Emblem)
This is the dataset of ivy (Fire Emblem), containing 309 images and their tags.
The core tags of this character are `long_hair, breasts, purple_hair, large_breasts, purple_eyes, bangs, mole`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 309 | 529.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ivy_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 309 | 270.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ivy_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 768 | 591.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ivy_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 309 | 454.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ivy_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 768 | 892.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ivy_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ivy_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, cleavage, dress, mole_under_mouth, solo, bare_shoulders, elbow_gloves, looking_at_viewer, smile, white_gloves, rose, red_flower |
| 1 | 7 |  |  |  |  |  | 1girl, cleavage, elbow_gloves, holding_book, looking_at_viewer, solo, white_gloves, dress, fishnet_thighhighs, rose, bare_shoulders, simple_background |
| 2 | 6 |  |  |  |  |  | 1girl, choker, cleavage, looking_at_viewer, necklace, solo, mole_under_mouth, closed_mouth, collarbone, purple_dress, veil, very_long_hair, grey_background, long_sleeves, red_rose, upper_body |
| 3 | 22 |  |  |  |  |  | 1girl, cleavage, navel, solo, looking_at_viewer, hat_flower, one-piece_swimsuit, bracelet, cup, holding, smile, bare_shoulders, sarong, simple_background |
| 4 | 6 |  |  |  |  |  | 1girl, bare_shoulders, bracelet, cleavage, drinking_glass, full_body, hibiscus, holding_cup, looking_at_viewer, sarong, simple_background, solo, sun_hat, white_background, hat_flower, navel, pink_hair, sandals, smile, white_footwear, bare_legs, black_headwear, black_one-piece_swimsuit, drinking_straw, frills, high_heels, pink_eyes, standing, stomach, thighs, closed_mouth, red_flower, see-through, toes |
| 5 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, open_mouth, navel, nipples, sex, solo_focus, vaginal, completely_nude, cum_in_pussy, heart-shaped_pupils, missionary, on_back, penis |
| 6 | 6 |  |  |  |  |  | 1girl, nipples, pussy, solo, navel, smile, female_pubic_hair, looking_at_viewer, blush, completely_nude, mole_under_mouth, very_long_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | dress | mole_under_mouth | solo | bare_shoulders | elbow_gloves | looking_at_viewer | smile | white_gloves | rose | red_flower | holding_book | fishnet_thighhighs | simple_background | choker | necklace | closed_mouth | collarbone | purple_dress | veil | very_long_hair | grey_background | long_sleeves | red_rose | upper_body | navel | hat_flower | one-piece_swimsuit | bracelet | cup | holding | sarong | drinking_glass | full_body | hibiscus | holding_cup | sun_hat | white_background | pink_hair | sandals | white_footwear | bare_legs | black_headwear | black_one-piece_swimsuit | drinking_straw | frills | high_heels | pink_eyes | standing | stomach | thighs | see-through | toes | 1boy | blush | hetero | open_mouth | nipples | sex | solo_focus | vaginal | completely_nude | cum_in_pussy | heart-shaped_pupils | missionary | on_back | penis | pussy | female_pubic_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------|:-------------------|:-------|:-----------------|:---------------|:--------------------|:--------|:---------------|:-------|:-------------|:---------------|:---------------------|:--------------------|:---------|:-----------|:---------------|:-------------|:---------------|:-------|:-----------------|:------------------|:---------------|:-----------|:-------------|:--------|:-------------|:---------------------|:-----------|:------|:----------|:---------|:-----------------|:------------|:-----------|:--------------|:----------|:-------------------|:------------|:----------|:-----------------|:------------|:-----------------|:---------------------------|:-----------------|:---------|:-------------|:------------|:-----------|:----------|:---------|:--------------|:-------|:-------|:--------|:---------|:-------------|:----------|:------|:-------------|:----------|:------------------|:---------------|:----------------------|:-------------|:----------|:--------|:--------|:--------------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | | X | X | X | X | | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | | X | X | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 22 |  |  |  |  |  | X | X | | | X | X | | X | X | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | | | X | X | | X | X | | | X | | | X | | | X | | | | | | | | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | |
| 6 | 6 |  |  |  |  |  | X | | | X | X | | | X | X | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | X | | | | | | X | X |
| CyberHarem/ivy_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T17:37:44+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T18:47:33+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of ivy (Fire Emblem)
============================
This is the dataset of ivy (Fire Emblem), containing 309 images and their tags.
The core tags of this character are 'long\_hair, breasts, purple\_hair, large\_breasts, purple\_eyes, bangs, mole', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1fd2972c55adf99501cf9a1a3aff9e1dcb857123 | # Dataset Card for "financial_sentiment_analysis_train_compilation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | RobertoMCA97/financial_sentiment_analysis_train_compilation | [
"region:us"
] | 2024-01-17T17:38:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "fiqa_2018", "path": "data/fiqa_2018-*"}, {"split": "financial_phrasebank", "path": "data/financial_phrasebank-*"}, {"split": "twitter_financial_news_sentiment", "path": "data/twitter_financial_news_sentiment-*"}, {"split": "auditor_sentiment", "path": "data/auditor_sentiment-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "fiqa_2018", "num_bytes": 86800, "num_examples": 961}, {"name": "financial_phrasebank", "num_bytes": 601485, "num_examples": 4217}, {"name": "twitter_financial_news_sentiment", "num_bytes": 971346, "num_examples": 9543}, {"name": "auditor_sentiment", "num_bytes": 555930, "num_examples": 3877}], "download_size": 58269, "dataset_size": 2215561}} | 2024-01-24T11:51:26+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "financial_sentiment_analysis_train_compilation"
More Information needed | [
"# Dataset Card for \"financial_sentiment_analysis_train_compilation\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"financial_sentiment_analysis_train_compilation\"\n\nMore Information needed"
] |
a6e8a23c1557fbc1a88ac500e6a08a311d01927f |
# Dataset of sonya (Fire Emblem)
This is the dataset of sonya (Fire Emblem), containing 250 images and their tags.
The core tags of this character are `purple_hair, long_hair, breasts, large_breasts, earrings, purple_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 250 | 341.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonya_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 250 | 182.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonya_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 590 | 366.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonya_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 250 | 296.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonya_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 590 | 535.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonya_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sonya_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, jewelry, nipples, penis, blush, cum_in_pussy, open_mouth, sex, vaginal, navel, thighhighs, ahegao, heart, spread_legs, sweat, cowgirl_position, tongue_out, brown_eyes, completely_nude, girl_on_top, saliva, uncensored |
| 1 | 12 |  |  |  |  |  | 1girl, 1boy, blush, hetero, mosaic_censoring, penis, solo_focus, fellatio, cum_in_mouth, heart, jewelry, dark-skinned_male, interracial, nipples, nude, circlet, gloves, thighhighs |
| 2 | 5 |  |  |  |  |  | 1girl, fellatio, hetero, multiple_penises, solo_focus, 2boys, double_penetration, jewelry, mmf_threesome, nipples, uncensored, blush, completely_nude, navel, spitroast, spread_legs, testicles, vaginal, black_gloves, dark-skinned_male, gangbang, gloved_handjob, interracial, pregnant, pussy_juice, thighhighs |
| 3 | 38 |  |  |  |  |  | jewelry, 1girl, cleavage, solo, cape, circlet, looking_at_viewer, smile, simple_background, black_gloves, dress, thighhighs, white_background |
| 4 | 6 |  |  |  |  |  | 1girl, fake_animal_ears, looking_at_viewer, pantyhose, rabbit_ears, solo, cleavage, gloves, jewelry, playboy_bunny, smile, leotard, official_alternate_costume, cape, circlet, easter_egg, open_mouth, thighs |
| 5 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, navel, smile, bare_shoulders, blue_sky, choker, cloud, collarbone, day, ocean, outdoors, purple_bikini, thighs, water, alternate_costume, bikini_pull, closed_mouth, jewelry, thigh_strap, tongue_out, twitter_username, wading |
| 6 | 5 |  |  |  |  |  | 2girls, yuri, closed_eyes, french_kiss, nail_polish, nude, blush, short_hair, black_nails, jewelry, nipples, saliva, sweat, tongue_out |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | solo_focus | jewelry | nipples | penis | blush | cum_in_pussy | open_mouth | sex | vaginal | navel | thighhighs | ahegao | heart | spread_legs | sweat | cowgirl_position | tongue_out | brown_eyes | completely_nude | girl_on_top | saliva | uncensored | mosaic_censoring | fellatio | cum_in_mouth | dark-skinned_male | interracial | nude | circlet | gloves | multiple_penises | 2boys | double_penetration | mmf_threesome | spitroast | testicles | black_gloves | gangbang | gloved_handjob | pregnant | pussy_juice | cleavage | solo | cape | looking_at_viewer | smile | simple_background | dress | white_background | fake_animal_ears | pantyhose | rabbit_ears | playboy_bunny | leotard | official_alternate_costume | easter_egg | thighs | bare_shoulders | blue_sky | choker | cloud | collarbone | day | ocean | outdoors | purple_bikini | water | alternate_costume | bikini_pull | closed_mouth | thigh_strap | twitter_username | wading | 2girls | yuri | closed_eyes | french_kiss | nail_polish | short_hair | black_nails |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:-------------|:----------|:----------|:--------|:--------|:---------------|:-------------|:------|:----------|:--------|:-------------|:---------|:--------|:--------------|:--------|:-------------------|:-------------|:-------------|:------------------|:--------------|:---------|:-------------|:-------------------|:-----------|:---------------|:--------------------|:--------------|:-------|:----------|:---------|:-------------------|:--------|:---------------------|:----------------|:------------|:------------|:---------------|:-----------|:-----------------|:-----------|:--------------|:-----------|:-------|:-------|:--------------------|:--------|:--------------------|:--------|:-------------------|:-------------------|:------------|:--------------|:----------------|:----------|:-----------------------------|:-------------|:---------|:-----------------|:-----------|:---------|:--------|:-------------|:------|:--------|:-----------|:----------------|:--------|:--------------------|:--------------|:---------------|:--------------|:-------------------|:---------|:---------|:-------|:--------------|:--------------|:--------------|:-------------|:--------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | | X | X | X | X | X | | X | | | | X | X | X | | | X | | | | | X | | | X | | X | | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 38 |  |  |  |  |  | | X | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | | X | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | X | X | X | X | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | | X | | | X | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 6 | 5 |  |  |  |  |  | | | | | X | X | | X | | | | | | | | | | X | | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X |
| CyberHarem/sonya_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T17:49:23+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T18:47:20+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of sonya (Fire Emblem)
==============================
This is the dataset of sonya (Fire Emblem), containing 250 images and their tags.
The core tags of this character are 'purple\_hair, long\_hair, breasts, large\_breasts, earrings, purple\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
ba032b6347159a0ca9069f2c1cd30411e88d5903 |
# Dataset of smia (Fire Emblem)
This is the dataset of smia (Fire Emblem), containing 111 images and their tags.
The core tags of this character are `long_hair, brown_hair, breasts, brown_eyes, large_breasts, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 111 | 125.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 111 | 74.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 258 | 156.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 111 | 110.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 258 | 214.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/smia_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/smia_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | hetero, blush, nipples, 1boy, 1girl, penis, sex, solo_focus, vaginal, open_mouth, cum_in_pussy, navel, completely_nude, straddling, sweat, girl_on_top, spread_legs, uncensored |
| 1 | 6 |  |  |  |  |  | 1girl, navel, nipples, solo, female_pubic_hair, looking_at_viewer, armpits, smile, blush, completely_nude, open_mouth, pussy |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | hetero | blush | nipples | 1boy | 1girl | penis | sex | solo_focus | vaginal | open_mouth | cum_in_pussy | navel | completely_nude | straddling | sweat | girl_on_top | spread_legs | uncensored | solo | female_pubic_hair | looking_at_viewer | armpits | smile | pussy |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------|:--------|:----------|:-------|:--------|:--------|:------|:-------------|:----------|:-------------|:---------------|:--------|:------------------|:-------------|:--------|:--------------|:--------------|:-------------|:-------|:--------------------|:--------------------|:----------|:--------|:--------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 1 | 6 |  |  |  |  |  | | X | X | | X | | | | | X | | X | X | | | | | | X | X | X | X | X | X |
| CyberHarem/smia_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T17:49:55+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T18:29:40+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of smia (Fire Emblem)
=============================
This is the dataset of smia (Fire Emblem), containing 111 images and their tags.
The core tags of this character are 'long\_hair, brown\_hair, breasts, brown\_eyes, large\_breasts, hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
423ff46a756fd429fde335ced68ac8be34c36136 |
# Dataset of laegjarn (Fire Emblem)
This is the dataset of laegjarn (Fire Emblem), containing 244 images and their tags.
The core tags of this character are `green_hair, dark_skin, multicolored_hair, short_hair, breasts, red_eyes, dark-skinned_female, gradient_hair, orange_hair, large_breasts, hair_ornament, horns, earrings, hair_flower`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 244 | 333.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laegjarn_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 244 | 179.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laegjarn_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 609 | 388.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laegjarn_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 244 | 291.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laegjarn_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 609 | 558.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laegjarn_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/laegjarn_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, black_one-piece_swimsuit, cleavage, solo, flower, simple_background, upper_body, arms_up, parted_lips |
| 1 | 13 |  |  |  |  |  | 1girl, simple_background, solo, upper_body, cape, jewelry, smile, white_background, breastplate, closed_mouth, feather_trim, looking_at_viewer |
| 2 | 6 |  |  |  |  |  | 1girl, breastplate, cape, holding_sword, solo, feather_trim, jewelry, lipstick, simple_background, full_body, two-tone_hair, armored_boots, bangs, gauntlets, pantyhose |
| 3 | 5 |  |  |  |  |  | 1girl, armored_boots, bangs, feather_trim, fire, flaming_eye, jewelry, long_hair, solo, two-tone_hair, arrow_(projectile), cleavage, full_body, hat, holding_bow_(weapon), lipstick, parted_lips, purple_lips, shiny_clothes, white_background, arm_up, bare_shoulders, elbow_gloves, gold_trim, gradient_clothes, high_heels, looking_at_viewer, pantyhose, purple_bodysuit, simple_background, arm_behind_head, clothing_cutout, feathers, leg_up, looking_away, pelvic_curtain, shoulder_armor, sleeveless, standing, teeth, transparent_background, turtleneck |
| 4 | 8 |  |  |  |  |  | 1girl, solo, alternate_costume, fur_trim, oil-paper_umbrella, holding, obi, wide_sleeves, choker, floral_print, full_body, jewelry, looking_at_viewer, red_kimono, bangs, closed_mouth, flower, sandals, simple_background, smile, tabi, white_background, lipstick |
| 5 | 25 |  |  |  |  |  | 1girl, hetero, solo_focus, 1boy, nipples, penis, flower, jewelry, blush, open_mouth, black_one-piece_swimsuit, cum_on_breasts, facial, sex, huge_breasts, mosaic_censoring, pussy, vaginal |
| 6 | 5 |  |  |  |  |  | 1girl, hetero, multiple_penises, nipples, solo_focus, blush, navel, 2boys, completely_nude, cum_in_pussy, gangbang, mmf_threesome, spread_legs, uncensored, vaginal, 3boys, anal, cum_in_ass, fellatio, lying, open_mouth, simple_background, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_one-piece_swimsuit | cleavage | solo | flower | simple_background | upper_body | arms_up | parted_lips | cape | jewelry | smile | white_background | breastplate | closed_mouth | feather_trim | looking_at_viewer | holding_sword | lipstick | full_body | two-tone_hair | armored_boots | bangs | gauntlets | pantyhose | fire | flaming_eye | long_hair | arrow_(projectile) | hat | holding_bow_(weapon) | purple_lips | shiny_clothes | arm_up | bare_shoulders | elbow_gloves | gold_trim | gradient_clothes | high_heels | purple_bodysuit | arm_behind_head | clothing_cutout | feathers | leg_up | looking_away | pelvic_curtain | shoulder_armor | sleeveless | standing | teeth | transparent_background | turtleneck | alternate_costume | fur_trim | oil-paper_umbrella | holding | obi | wide_sleeves | choker | floral_print | red_kimono | sandals | tabi | hetero | solo_focus | 1boy | nipples | penis | blush | open_mouth | cum_on_breasts | facial | sex | huge_breasts | mosaic_censoring | pussy | vaginal | multiple_penises | navel | 2boys | completely_nude | cum_in_pussy | gangbang | mmf_threesome | spread_legs | uncensored | 3boys | anal | cum_in_ass | fellatio | lying | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------------|:-----------|:-------|:---------|:--------------------|:-------------|:----------|:--------------|:-------|:----------|:--------|:-------------------|:--------------|:---------------|:---------------|:--------------------|:----------------|:-----------|:------------|:----------------|:----------------|:--------|:------------|:------------|:-------|:--------------|:------------|:---------------------|:------|:-----------------------|:--------------|:----------------|:---------|:-----------------|:---------------|:------------|:-------------------|:-------------|:------------------|:------------------|:------------------|:-----------|:---------|:---------------|:-----------------|:-----------------|:-------------|:-----------|:--------|:-------------------------|:-------------|:--------------------|:-----------|:---------------------|:----------|:------|:---------------|:---------|:---------------|:-------------|:----------|:-------|:---------|:-------------|:-------|:----------|:--------|:--------|:-------------|:-----------------|:---------|:------|:---------------|:-------------------|:--------|:----------|:-------------------|:--------|:--------|:------------------|:---------------|:-----------|:----------------|:--------------|:-------------|:--------|:-------|:-------------|:-----------|:--------|:--------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | | | X | | X | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | | X | | X | | | | X | X | | | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | X | | X | | | X | | X | | X | | | X | X | | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | | | X | X | X | | | | | X | X | X | | X | | X | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 25 |  |  |  |  |  | X | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | | X | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/laegjarn_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T17:50:19+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T18:54:11+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of laegjarn (Fire Emblem)
=================================
This is the dataset of laegjarn (Fire Emblem), containing 244 images and their tags.
The core tags of this character are 'green\_hair, dark\_skin, multicolored\_hair, short\_hair, breasts, red\_eyes, dark-skinned\_female, gradient\_hair, orange\_hair, large\_breasts, hair\_ornament, horns, earrings, hair\_flower', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
a4c2e74883b38cc6a0217d30722b18193fe50fbf |
# Dataset of eponine (Fire Emblem)
This is the dataset of eponine (Fire Emblem), containing 366 images and their tags.
The core tags of this character are `braid, ahoge, twin_braids, bangs, long_hair, blue_eyes, breasts, twintails, parted_bangs, low_twintails, white_hair, hairband, medium_breasts, white_hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 366 | 422.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eponine_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 366 | 238.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eponine_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 769 | 475.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eponine_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 366 | 368.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eponine_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 769 | 689.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eponine_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/eponine_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, solo, looking_at_viewer, low_twin_braids, hair_flower, wings, official_alternate_costume, open_mouth, upper_body, cleavage, smile |
| 1 | 35 |  |  |  |  |  | 1girl, hooded_capelet, upper_body, choker, chest_harness, solo, buttons, turtleneck, hood_down, closed_mouth, looking_at_viewer, red_hood, cleavage, leather, smile, red_capelet |
| 2 | 22 |  |  |  |  |  | 1girl, hooded_capelet, belt, chest_harness, solo, buttons, choker, holding_bow_(weapon), hood_down, leather_boots, looking_at_viewer, arrow_(projectile), red_hood, key, low_twin_braids, red_capelet, full_body |
| 3 | 7 |  |  |  |  |  | 1girl, cleavage, earrings, hood_up, hooded_cape, looking_at_viewer, official_alternate_costume, turtleneck, mask, open_mouth, solo, upper_body, black_cape, black_gloves, blue_bodysuit, feathers, o-ring, smile, upper_teeth_only, holding, one_eye_closed, chest_harness, covered_navel, rose, skin_tight |
| 4 | 7 |  |  |  |  |  | 1girl, black_footwear, blue_bodysuit, earrings, holding, looking_at_viewer, o-ring, official_alternate_costume, thigh_boots, turtleneck, black_cape, black_gloves, chest_harness, cleavage, hooded_cape, leather_belt, mask, skin_tight, solo, latex_bodysuit, blue_ribbon, covered_navel, hair_ribbon, key, hood_up, red_cape, satchel, tongue_out |
| 5 | 5 |  |  |  |  |  | 1girl, alternate_costume, navel, solo, bikini, looking_at_viewer, cleavage, hair_flower, jewelry, holding, midriff |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | low_twin_braids | hair_flower | wings | official_alternate_costume | open_mouth | upper_body | cleavage | smile | hooded_capelet | choker | chest_harness | buttons | turtleneck | hood_down | closed_mouth | red_hood | leather | red_capelet | belt | holding_bow_(weapon) | leather_boots | arrow_(projectile) | key | full_body | earrings | hood_up | hooded_cape | mask | black_cape | black_gloves | blue_bodysuit | feathers | o-ring | upper_teeth_only | holding | one_eye_closed | covered_navel | rose | skin_tight | black_footwear | thigh_boots | leather_belt | latex_bodysuit | blue_ribbon | hair_ribbon | red_cape | satchel | tongue_out | alternate_costume | navel | bikini | jewelry | midriff |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:------------------|:--------------|:--------|:-----------------------------|:-------------|:-------------|:-----------|:--------|:-----------------|:---------|:----------------|:----------|:-------------|:------------|:---------------|:-----------|:----------|:--------------|:-------|:-----------------------|:----------------|:---------------------|:------|:------------|:-----------|:----------|:--------------|:-------|:-------------|:---------------|:----------------|:-----------|:---------|:-------------------|:----------|:-----------------|:----------------|:-------|:-------------|:-----------------|:--------------|:---------------|:-----------------|:--------------|:--------------|:-----------|:----------|:-------------|:--------------------|:--------|:---------|:----------|:----------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 35 |  |  |  |  |  | X | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 22 |  |  |  |  |  | X | X | X | X | | | | | | | | X | X | X | X | | X | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | X | | | | X | X | X | X | X | | | X | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | X | X | | | | X | | | X | | | | X | | X | | | | | | | | | | X | | X | X | X | X | X | X | X | | X | | X | | X | | X | X | X | X | X | X | X | X | X | X | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X |
| CyberHarem/eponine_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T17:50:39+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:05:22+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of eponine (Fire Emblem)
================================
This is the dataset of eponine (Fire Emblem), containing 366 images and their tags.
The core tags of this character are 'braid, ahoge, twin\_braids, bangs, long\_hair, blue\_eyes, breasts, twintails, parted\_bangs, low\_twintails, white\_hair, hairband, medium\_breasts, white\_hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
567743682926ea12fc0c06d65bef01846c993fbe |
# Civitai-2.5M
This dataset contains around 2.5M ai-generated images from civitai.
Metadata from https://huggingface.co/datasets/hanruijiang/civitai-stable-diffusion-2.5m
I use img2dataset to create this dataset:
img2dataset --url_list civitai-2023-11-14_img2datasets.parquet --input_format "parquet" --url_col "url" --caption_col "prompt" --output_format files --output_folder civitai25m --processes_count 16 --thread_count 64 --resize_mode="no" --save_additional_columns '["'id'","hash","'negativePrompt'"]'
| nebula/civitai-2.5m | [
"license:cc-by-nc-4.0",
"region:us"
] | 2024-01-17T17:59:19+00:00 | {"license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "__key__", "dtype": "string"}, {"name": "jpg", "dtype": "image"}, {"name": "json", "struct": [{"name": "caption", "dtype": "string"}, {"name": "error_message", "dtype": "null"}, {"name": "exif", "dtype": "string"}, {"name": "hash", "dtype": "string"}, {"name": "height", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "key", "dtype": "string"}, {"name": "negativePrompt", "dtype": "string"}, {"name": "original_height", "dtype": "int64"}, {"name": "original_width", "dtype": "int64"}, {"name": "sha256", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "width", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 757285374527.0, "num_examples": 2388378}], "download_size": 754294698643, "dataset_size": 757285374527.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-19T14:03:07+00:00 | [] | [] | TAGS
#license-cc-by-nc-4.0 #region-us
|
# Civitai-2.5M
This dataset contains around 2.5M ai-generated images from civitai.
Metadata from URL
I use img2dataset to create this dataset:
img2dataset --url_list civitai-2023-11-14_img2datasets.parquet --input_format "parquet" --url_col "url" --caption_col "prompt" --output_format files --output_folder civitai25m --processes_count 16 --thread_count 64 --resize_mode="no" --save_additional_columns '["'id'","hash","'negativePrompt'"]'
| [
"# Civitai-2.5M\n\nThis dataset contains around 2.5M ai-generated images from civitai.\n\nMetadata from URL\n\nI use img2dataset to create this dataset:\n\nimg2dataset --url_list civitai-2023-11-14_img2datasets.parquet --input_format \"parquet\" --url_col \"url\" --caption_col \"prompt\" --output_format files --output_folder civitai25m --processes_count 16 --thread_count 64 --resize_mode=\"no\" --save_additional_columns '[\"'id'\",\"hash\",\"'negativePrompt'\"]'"
] | [
"TAGS\n#license-cc-by-nc-4.0 #region-us \n",
"# Civitai-2.5M\n\nThis dataset contains around 2.5M ai-generated images from civitai.\n\nMetadata from URL\n\nI use img2dataset to create this dataset:\n\nimg2dataset --url_list civitai-2023-11-14_img2datasets.parquet --input_format \"parquet\" --url_col \"url\" --caption_col \"prompt\" --output_format files --output_folder civitai25m --processes_count 16 --thread_count 64 --resize_mode=\"no\" --save_additional_columns '[\"'id'\",\"hash\",\"'negativePrompt'\"]'"
] |
590ac621e14b8e2b9210102ede3ba20ae6f86549 |
# Dataset of ishtar (Fire Emblem)
This is the dataset of ishtar (Fire Emblem), containing 251 images and their tags.
The core tags of this character are `long_hair, breasts, purple_eyes, ponytail, purple_hair, large_breasts, earrings, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 251 | 375.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ishtar_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 251 | 198.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ishtar_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 615 | 424.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ishtar_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 251 | 322.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ishtar_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 615 | 607.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ishtar_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ishtar_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, alternate_costume, cape, cleavage, dress, hair_flower, necklace, solo, thighs, braid, magic, medium_breasts, electricity, full_body, holding_book, lightning, looking_away, open_book, armpits, bangs, high_heels, shiny_hair, simple_background, white_background, elbow_gloves, gold_trim, pauldrons |
| 1 | 7 |  |  |  |  |  | 1girl, cape, cleavage, dress, elbow_gloves, solo, choker, pauldrons, thighhighs, white_gloves, belt, necklace, side_slit, thighs, grey_hair, looking_at_viewer, sidelocks, simple_background, cross_earrings, lightning, thigh_boots, white_background |
| 2 | 14 |  |  |  |  |  | 1girl, cape, cleavage, dress, elbow_gloves, solo, pauldrons, thighhighs, looking_at_viewer, simple_background, side_slit, side_ponytail, bracelet, bridal_gauntlets, full_body, high_heels, thigh_boots, thighs, choker, white_background, white_gloves |
| 3 | 5 |  |  |  |  |  | 1girl, book, cape, cleavage, dress, elbow_gloves, electricity, lightning, looking_at_viewer, solo, magic, belt, bracelet, side_slit, thighhighs, choker, pauldrons |
| 4 | 10 |  |  |  |  |  | 1girl, choker, looking_at_viewer, smile, solo, alternate_costume, bracelet, cleavage, purple_dress, cup, holding |
| 5 | 16 |  |  |  |  |  | 1girl, solo, looking_at_viewer, nipples, blush, navel, completely_nude, jewelry, pussy, choker, smile |
| 6 | 6 |  |  |  |  |  | 1girl, hetero, jewelry, open_mouth, 1boy, blush, sex_from_behind, solo_focus, nipples, nude, choker, doggystyle, grey_hair |
| 7 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, penis, mosaic_censoring, nude, sex, solo_focus, open_mouth, vaginal, blush, pussy, female_pubic_hair, grey_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | alternate_costume | cape | cleavage | dress | hair_flower | necklace | solo | thighs | braid | magic | medium_breasts | electricity | full_body | holding_book | lightning | looking_away | open_book | armpits | bangs | high_heels | shiny_hair | simple_background | white_background | elbow_gloves | gold_trim | pauldrons | choker | thighhighs | white_gloves | belt | side_slit | grey_hair | looking_at_viewer | sidelocks | cross_earrings | thigh_boots | side_ponytail | bracelet | bridal_gauntlets | book | smile | purple_dress | cup | holding | nipples | blush | navel | completely_nude | jewelry | pussy | hetero | open_mouth | 1boy | sex_from_behind | solo_focus | nude | doggystyle | penis | mosaic_censoring | sex | vaginal | female_pubic_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:--------|:--------------|:-----------|:-------|:---------|:--------|:--------|:-----------------|:--------------|:------------|:---------------|:------------|:---------------|:------------|:----------|:--------|:-------------|:-------------|:--------------------|:-------------------|:---------------|:------------|:------------|:---------|:-------------|:---------------|:-------|:------------|:------------|:--------------------|:------------|:-----------------|:--------------|:----------------|:-----------|:-------------------|:-------|:--------|:---------------|:------|:----------|:----------|:--------|:--------|:------------------|:----------|:--------|:---------|:-------------|:-------|:------------------|:-------------|:-------|:-------------|:--------|:-------------------|:------|:----------|:--------------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | X | X | X | | X | X | X | | | | | | | X | | | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | | X | X | X | | | X | X | | | | | X | | | | | | | X | | X | X | X | | X | X | X | X | | X | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | X | X | | | X | | | X | | X | | | X | | | | | | | | | X | | X | X | X | | X | X | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 5 | 16 |  |  |  |  |  | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | X | X | | | X | | X | X | X | X | X | X | X | | | | | |
| 7 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | | | | X | X | X | X | | X | X | | X | X | X | X | X |
| CyberHarem/ishtar_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T18:04:19+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:08:53+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of ishtar (Fire Emblem)
===============================
This is the dataset of ishtar (Fire Emblem), containing 251 images and their tags.
The core tags of this character are 'long\_hair, breasts, purple\_eyes, ponytail, purple\_hair, large\_breasts, earrings, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
2fd9e1f7e7a2c6e22c5df446ceb6d5f86ab27c57 |
# Dataset of lakche (Fire Emblem)
This is the dataset of lakche (Fire Emblem), containing 185 images and their tags.
The core tags of this character are `black_hair, breasts, short_hair, sidelocks`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 185 | 232.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lakche_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 185 | 130.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lakche_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 433 | 267.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lakche_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 185 | 206.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lakche_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 433 | 378.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lakche_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/lakche_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, belt, breastplate, solo, sword, looking_at_viewer, purple_eyes, shoulder_armor, simple_background, white_background, smile, white_gloves, blush, earrings, purple_dress |
| 1 | 7 |  |  |  |  |  | 1girl, breastplate, holding_sword, solo, bangs, belt, short_dress, shoulder_armor, white_gloves, boots, open_mouth, simple_background, full_body, white_background, earrings, looking_at_viewer, sleeveless, smile, standing, white_footwear |
| 2 | 8 |  |  |  |  |  | 1girl, solo, bangs, breastplate, looking_at_viewer, short_hair_with_long_locks, pauldrons, purple_eyes, simple_background, upper_body, earrings, hair_between_eyes, smile, closed_mouth, white_background, cropped_torso |
| 3 | 9 |  |  |  |  |  | 1girl, nipples, blush, navel, solo, completely_nude, female_pubic_hair, looking_at_viewer, smile, barefoot, medium_breasts, uncensored, after_sex, cum_in_pussy, cum_on_body, cumdrip, lying |
| 4 | 11 |  |  |  |  |  | 1boy, hetero, nipples, 1girl, penis, blush, open_mouth, sex, female_pubic_hair, navel, smile, vaginal, clothed_male_nude_female, medium_breasts, solo_focus, uncensored, completely_nude, cowgirl_position, cum_in_pussy, girl_on_top, large_breasts, spread_legs, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | belt | breastplate | solo | sword | looking_at_viewer | purple_eyes | shoulder_armor | simple_background | white_background | smile | white_gloves | blush | earrings | purple_dress | holding_sword | bangs | short_dress | boots | open_mouth | full_body | sleeveless | standing | white_footwear | short_hair_with_long_locks | pauldrons | upper_body | hair_between_eyes | closed_mouth | cropped_torso | nipples | navel | completely_nude | female_pubic_hair | barefoot | medium_breasts | uncensored | after_sex | cum_in_pussy | cum_on_body | cumdrip | lying | 1boy | hetero | penis | sex | vaginal | clothed_male_nude_female | solo_focus | cowgirl_position | girl_on_top | large_breasts | spread_legs | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------|:-------|:--------|:--------------------|:--------------|:-----------------|:--------------------|:-------------------|:--------|:---------------|:--------|:-----------|:---------------|:----------------|:--------|:--------------|:--------|:-------------|:------------|:-------------|:-----------|:-----------------|:-----------------------------|:------------|:-------------|:--------------------|:---------------|:----------------|:----------|:--------|:------------------|:--------------------|:-----------|:-----------------|:-------------|:------------|:---------------|:--------------|:----------|:--------|:-------|:---------|:--------|:------|:----------|:---------------------------|:-------------|:-------------------|:--------------|:----------------|:--------------|:--------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | | X | | X | X | X | X | X | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | X | | X | X | | X | X | X | | | X | | | X | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | | | X | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | X | | | | | | | | | | X | | X | | | | | | | X | | | | | | | | | | | X | X | X | X | | X | X | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/lakche_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T18:04:51+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T18:58:34+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of lakche (Fire Emblem)
===============================
This is the dataset of lakche (Fire Emblem), containing 185 images and their tags.
The core tags of this character are 'black\_hair, breasts, short\_hair, sidelocks', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
c92a2507bdcab7270cc9232c42e34e259b6f62cf |
# Dataset Card for Evaluation run of lodrick-the-lafted/Winged-Lagomorph-2x13B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [lodrick-the-lafted/Winged-Lagomorph-2x13B](https://huggingface.co/lodrick-the-lafted/Winged-Lagomorph-2x13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lodrick-the-lafted__Winged-Lagomorph-2x13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-17T18:07:01.785781](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Winged-Lagomorph-2x13B/blob/main/results_2024-01-17T18-07-01.785781.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.44701093892342236,
"acc_stderr": 0.03445607638165826,
"acc_norm": 0.44980402546373816,
"acc_norm_stderr": 0.03519159952391745,
"mc1": 0.2802937576499388,
"mc1_stderr": 0.015723139524608763,
"mc2": 0.4453666576267616,
"mc2_stderr": 0.015036118833065276
},
"harness|arc:challenge|25": {
"acc": 0.44795221843003413,
"acc_stderr": 0.01453201149821167,
"acc_norm": 0.47952218430034127,
"acc_norm_stderr": 0.014599131353035005
},
"harness|hellaswag|10": {
"acc": 0.5243975303724357,
"acc_stderr": 0.004983837641502894,
"acc_norm": 0.6938856801433977,
"acc_norm_stderr": 0.004599358920909553
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.04135176749720386,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.04135176749720386
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.040179012759817494,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.040179012759817494
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.44150943396226416,
"acc_stderr": 0.030561590426731837,
"acc_norm": 0.44150943396226416,
"acc_norm_stderr": 0.030561590426731837
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.375,
"acc_stderr": 0.04048439222695598,
"acc_norm": 0.375,
"acc_norm_stderr": 0.04048439222695598
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3988439306358382,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.3988439306358382,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.30392156862745096,
"acc_stderr": 0.045766654032077636,
"acc_norm": 0.30392156862745096,
"acc_norm_stderr": 0.045766654032077636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.425531914893617,
"acc_stderr": 0.032321469162244675,
"acc_norm": 0.425531914893617,
"acc_norm_stderr": 0.032321469162244675
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.04227054451232199,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.04227054451232199
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4689655172413793,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.4689655172413793,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.29894179894179895,
"acc_stderr": 0.023577604791655802,
"acc_norm": 0.29894179894179895,
"acc_norm_stderr": 0.023577604791655802
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.04006168083848878,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.04006168083848878
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110175,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110175
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.4096774193548387,
"acc_stderr": 0.027976054915347364,
"acc_norm": 0.4096774193548387,
"acc_norm_stderr": 0.027976054915347364
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.32019704433497537,
"acc_stderr": 0.032826493853041504,
"acc_norm": 0.32019704433497537,
"acc_norm_stderr": 0.032826493853041504
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.5696969696969697,
"acc_stderr": 0.03866225962879077,
"acc_norm": 0.5696969696969697,
"acc_norm_stderr": 0.03866225962879077
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5606060606060606,
"acc_stderr": 0.035360859475294805,
"acc_norm": 0.5606060606060606,
"acc_norm_stderr": 0.035360859475294805
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.5440414507772021,
"acc_stderr": 0.03594413711272438,
"acc_norm": 0.5440414507772021,
"acc_norm_stderr": 0.03594413711272438
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.023901157979402538,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.023901157979402538
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.027309140588230186,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.027309140588230186
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.3949579831932773,
"acc_stderr": 0.03175367846096624,
"acc_norm": 0.3949579831932773,
"acc_norm_stderr": 0.03175367846096624
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.03802039760107903,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.03802039760107903
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5651376146788991,
"acc_stderr": 0.021254631465609283,
"acc_norm": 0.5651376146788991,
"acc_norm_stderr": 0.021254631465609283
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3194444444444444,
"acc_stderr": 0.031798763421768524,
"acc_norm": 0.3194444444444444,
"acc_norm_stderr": 0.031798763421768524
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5980392156862745,
"acc_stderr": 0.034411900234824655,
"acc_norm": 0.5980392156862745,
"acc_norm_stderr": 0.034411900234824655
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6244725738396625,
"acc_stderr": 0.03152256243091156,
"acc_norm": 0.6244725738396625,
"acc_norm_stderr": 0.03152256243091156
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5515695067264574,
"acc_stderr": 0.03337883736255097,
"acc_norm": 0.5515695067264574,
"acc_norm_stderr": 0.03337883736255097
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.4122137404580153,
"acc_stderr": 0.04317171194870255,
"acc_norm": 0.4122137404580153,
"acc_norm_stderr": 0.04317171194870255
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6611570247933884,
"acc_stderr": 0.04320767807536671,
"acc_norm": 0.6611570247933884,
"acc_norm_stderr": 0.04320767807536671
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04803752235190193,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04803752235190193
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5337423312883436,
"acc_stderr": 0.039194155450484096,
"acc_norm": 0.5337423312883436,
"acc_norm_stderr": 0.039194155450484096
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.375,
"acc_stderr": 0.04595091388086298,
"acc_norm": 0.375,
"acc_norm_stderr": 0.04595091388086298
},
"harness|hendrycksTest-management|5": {
"acc": 0.5631067961165048,
"acc_stderr": 0.04911147107365777,
"acc_norm": 0.5631067961165048,
"acc_norm_stderr": 0.04911147107365777
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7008547008547008,
"acc_stderr": 0.029996951858349476,
"acc_norm": 0.7008547008547008,
"acc_norm_stderr": 0.029996951858349476
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5721583652618135,
"acc_stderr": 0.017692787927803728,
"acc_norm": 0.5721583652618135,
"acc_norm_stderr": 0.017692787927803728
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.4913294797687861,
"acc_stderr": 0.0269150473553698,
"acc_norm": 0.4913294797687861,
"acc_norm_stderr": 0.0269150473553698
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.32513966480446926,
"acc_stderr": 0.01566654278505354,
"acc_norm": 0.32513966480446926,
"acc_norm_stderr": 0.01566654278505354
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.4084967320261438,
"acc_stderr": 0.02814640599309636,
"acc_norm": 0.4084967320261438,
"acc_norm_stderr": 0.02814640599309636
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5016077170418006,
"acc_stderr": 0.02839794490780661,
"acc_norm": 0.5016077170418006,
"acc_norm_stderr": 0.02839794490780661
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.49382716049382713,
"acc_stderr": 0.027818623962583295,
"acc_norm": 0.49382716049382713,
"acc_norm_stderr": 0.027818623962583295
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.35815602836879434,
"acc_stderr": 0.028602085862759415,
"acc_norm": 0.35815602836879434,
"acc_norm_stderr": 0.028602085862759415
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3396349413298566,
"acc_stderr": 0.012095592506931976,
"acc_norm": 0.3396349413298566,
"acc_norm_stderr": 0.012095592506931976
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.27941176470588236,
"acc_stderr": 0.027257202606114944,
"acc_norm": 0.27941176470588236,
"acc_norm_stderr": 0.027257202606114944
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.019910377463105932,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.019910377463105932
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5363636363636364,
"acc_stderr": 0.04776449162396197,
"acc_norm": 0.5363636363636364,
"acc_norm_stderr": 0.04776449162396197
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5755102040816327,
"acc_stderr": 0.031642094879429414,
"acc_norm": 0.5755102040816327,
"acc_norm_stderr": 0.031642094879429414
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.527363184079602,
"acc_stderr": 0.035302355173346824,
"acc_norm": 0.527363184079602,
"acc_norm_stderr": 0.035302355173346824
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-virology|5": {
"acc": 0.41566265060240964,
"acc_stderr": 0.03836722176598052,
"acc_norm": 0.41566265060240964,
"acc_norm_stderr": 0.03836722176598052
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6023391812865497,
"acc_stderr": 0.0375363895576169,
"acc_norm": 0.6023391812865497,
"acc_norm_stderr": 0.0375363895576169
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2802937576499388,
"mc1_stderr": 0.015723139524608763,
"mc2": 0.4453666576267616,
"mc2_stderr": 0.015036118833065276
},
"harness|winogrande|5": {
"acc": 0.6740331491712708,
"acc_stderr": 0.01317378263692219
},
"harness|gsm8k|5": {
"acc": 0.2562547384382108,
"acc_stderr": 0.012025145867332842
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_lodrick-the-lafted__Winged-Lagomorph-2x13B | [
"region:us"
] | 2024-01-17T18:09:23+00:00 | {"pretty_name": "Evaluation run of lodrick-the-lafted/Winged-Lagomorph-2x13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [lodrick-the-lafted/Winged-Lagomorph-2x13B](https://huggingface.co/lodrick-the-lafted/Winged-Lagomorph-2x13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lodrick-the-lafted__Winged-Lagomorph-2x13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T18:07:01.785781](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Winged-Lagomorph-2x13B/blob/main/results_2024-01-17T18-07-01.785781.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.44701093892342236,\n \"acc_stderr\": 0.03445607638165826,\n \"acc_norm\": 0.44980402546373816,\n \"acc_norm_stderr\": 0.03519159952391745,\n \"mc1\": 0.2802937576499388,\n \"mc1_stderr\": 0.015723139524608763,\n \"mc2\": 0.4453666576267616,\n \"mc2_stderr\": 0.015036118833065276\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.44795221843003413,\n \"acc_stderr\": 0.01453201149821167,\n \"acc_norm\": 0.47952218430034127,\n \"acc_norm_stderr\": 0.014599131353035005\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5243975303724357,\n \"acc_stderr\": 0.004983837641502894,\n \"acc_norm\": 0.6938856801433977,\n \"acc_norm_stderr\": 0.004599358920909553\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.040179012759817494,\n \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.040179012759817494\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.44150943396226416,\n \"acc_stderr\": 0.030561590426731837,\n \"acc_norm\": 0.44150943396226416,\n \"acc_norm_stderr\": 0.030561590426731837\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.425531914893617,\n \"acc_stderr\": 0.032321469162244675,\n \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.032321469162244675\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.04227054451232199,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.04227054451232199\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.29894179894179895,\n \"acc_stderr\": 0.023577604791655802,\n \"acc_norm\": 0.29894179894179895,\n \"acc_norm_stderr\": 0.023577604791655802\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.04006168083848878,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.04006168083848878\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110175,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110175\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4096774193548387,\n \"acc_stderr\": 0.027976054915347364,\n \"acc_norm\": 0.4096774193548387,\n \"acc_norm_stderr\": 0.027976054915347364\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.32019704433497537,\n \"acc_stderr\": 0.032826493853041504,\n \"acc_norm\": 0.32019704433497537,\n \"acc_norm_stderr\": 0.032826493853041504\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.5696969696969697,\n \"acc_stderr\": 0.03866225962879077,\n \"acc_norm\": 0.5696969696969697,\n \"acc_norm_stderr\": 0.03866225962879077\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5606060606060606,\n \"acc_stderr\": 0.035360859475294805,\n \"acc_norm\": 0.5606060606060606,\n \"acc_norm_stderr\": 0.035360859475294805\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.5440414507772021,\n \"acc_stderr\": 0.03594413711272438,\n \"acc_norm\": 0.5440414507772021,\n \"acc_norm_stderr\": 0.03594413711272438\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.023901157979402538,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.023901157979402538\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.027309140588230186,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.027309140588230186\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.3949579831932773,\n \"acc_stderr\": 0.03175367846096624,\n \"acc_norm\": 0.3949579831932773,\n \"acc_norm_stderr\": 0.03175367846096624\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5651376146788991,\n \"acc_stderr\": 0.021254631465609283,\n \"acc_norm\": 0.5651376146788991,\n \"acc_norm_stderr\": 0.021254631465609283\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3194444444444444,\n \"acc_stderr\": 0.031798763421768524,\n \"acc_norm\": 0.3194444444444444,\n \"acc_norm_stderr\": 0.031798763421768524\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5980392156862745,\n \"acc_stderr\": 0.034411900234824655,\n \"acc_norm\": 0.5980392156862745,\n \"acc_norm_stderr\": 0.034411900234824655\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6244725738396625,\n \"acc_stderr\": 0.03152256243091156,\n \"acc_norm\": 0.6244725738396625,\n \"acc_norm_stderr\": 0.03152256243091156\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5515695067264574,\n \"acc_stderr\": 0.03337883736255097,\n \"acc_norm\": 0.5515695067264574,\n \"acc_norm_stderr\": 0.03337883736255097\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.4122137404580153,\n \"acc_stderr\": 0.04317171194870255,\n \"acc_norm\": 0.4122137404580153,\n \"acc_norm_stderr\": 0.04317171194870255\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6611570247933884,\n \"acc_stderr\": 0.04320767807536671,\n \"acc_norm\": 0.6611570247933884,\n \"acc_norm_stderr\": 0.04320767807536671\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04803752235190193,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04803752235190193\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.5337423312883436,\n \"acc_stderr\": 0.039194155450484096,\n \"acc_norm\": 0.5337423312883436,\n \"acc_norm_stderr\": 0.039194155450484096\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.5631067961165048,\n \"acc_stderr\": 0.04911147107365777,\n \"acc_norm\": 0.5631067961165048,\n \"acc_norm_stderr\": 0.04911147107365777\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7008547008547008,\n \"acc_stderr\": 0.029996951858349476,\n \"acc_norm\": 0.7008547008547008,\n \"acc_norm_stderr\": 0.029996951858349476\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5721583652618135,\n \"acc_stderr\": 0.017692787927803728,\n \"acc_norm\": 0.5721583652618135,\n \"acc_norm_stderr\": 0.017692787927803728\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.4913294797687861,\n \"acc_stderr\": 0.0269150473553698,\n \"acc_norm\": 0.4913294797687861,\n \"acc_norm_stderr\": 0.0269150473553698\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32513966480446926,\n \"acc_stderr\": 0.01566654278505354,\n \"acc_norm\": 0.32513966480446926,\n \"acc_norm_stderr\": 0.01566654278505354\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4084967320261438,\n \"acc_stderr\": 0.02814640599309636,\n \"acc_norm\": 0.4084967320261438,\n \"acc_norm_stderr\": 0.02814640599309636\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5016077170418006,\n \"acc_stderr\": 0.02839794490780661,\n \"acc_norm\": 0.5016077170418006,\n \"acc_norm_stderr\": 0.02839794490780661\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.49382716049382713,\n \"acc_stderr\": 0.027818623962583295,\n \"acc_norm\": 0.49382716049382713,\n \"acc_norm_stderr\": 0.027818623962583295\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.35815602836879434,\n \"acc_stderr\": 0.028602085862759415,\n \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.028602085862759415\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3396349413298566,\n \"acc_stderr\": 0.012095592506931976,\n \"acc_norm\": 0.3396349413298566,\n \"acc_norm_stderr\": 0.012095592506931976\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.027257202606114944,\n \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.027257202606114944\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.019910377463105932,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.019910377463105932\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5363636363636364,\n \"acc_stderr\": 0.04776449162396197,\n \"acc_norm\": 0.5363636363636364,\n \"acc_norm_stderr\": 0.04776449162396197\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5755102040816327,\n \"acc_stderr\": 0.031642094879429414,\n \"acc_norm\": 0.5755102040816327,\n \"acc_norm_stderr\": 0.031642094879429414\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.527363184079602,\n \"acc_stderr\": 0.035302355173346824,\n \"acc_norm\": 0.527363184079602,\n \"acc_norm_stderr\": 0.035302355173346824\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.41566265060240964,\n \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.41566265060240964,\n \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.6023391812865497,\n \"acc_stderr\": 0.0375363895576169,\n \"acc_norm\": 0.6023391812865497,\n \"acc_norm_stderr\": 0.0375363895576169\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2802937576499388,\n \"mc1_stderr\": 0.015723139524608763,\n \"mc2\": 0.4453666576267616,\n \"mc2_stderr\": 0.015036118833065276\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6740331491712708,\n \"acc_stderr\": 0.01317378263692219\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2562547384382108,\n \"acc_stderr\": 0.012025145867332842\n }\n}\n```", "repo_url": "https://huggingface.co/lodrick-the-lafted/Winged-Lagomorph-2x13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|arc:challenge|25_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|gsm8k|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hellaswag|10_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T18-07-01.785781.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_abstract_algebra_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_anatomy_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_astronomy_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_business_ethics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_computer_science_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_mathematics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_medicine_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_physics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_computer_security_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_conceptual_physics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_econometrics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_electrical_engineering_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_elementary_mathematics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_formal_logic_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_global_facts_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_european_history_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_geography_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_government_and_politics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_macroeconomics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_mathematics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_microeconomics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_physics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_psychology_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_statistics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_us_history_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["**/details_harness|winogrande|5_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-17T18-07-01.785781.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_17T18_07_01.785781", "path": ["results_2024-01-17T18-07-01.785781.parquet"]}, {"split": "latest", "path": ["results_2024-01-17T18-07-01.785781.parquet"]}]}]} | 2024-01-17T18:09:48+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of lodrick-the-lafted/Winged-Lagomorph-2x13B
Dataset automatically created during the evaluation run of model lodrick-the-lafted/Winged-Lagomorph-2x13B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-17T18:07:01.785781(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of lodrick-the-lafted/Winged-Lagomorph-2x13B\n\n\n\nDataset automatically created during the evaluation run of model lodrick-the-lafted/Winged-Lagomorph-2x13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T18:07:01.785781(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of lodrick-the-lafted/Winged-Lagomorph-2x13B\n\n\n\nDataset automatically created during the evaluation run of model lodrick-the-lafted/Winged-Lagomorph-2x13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T18:07:01.785781(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
7fb4035fe9909e60771998b87de2f82cea357eac |
# Dataset Card for Evaluation run of KnutJaegersberg/Deita-1_8B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deita-1_8B](https://huggingface.co/KnutJaegersberg/Deita-1_8B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Deita-1_8B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-17T18:22:52.012956](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-1_8B/blob/main/results_2024-01-17T18-22-52.012956.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.4513695685616451,
"acc_stderr": 0.03473174413713779,
"acc_norm": 0.4572369573723266,
"acc_norm_stderr": 0.035511285827617124,
"mc1": 0.2521419828641371,
"mc1_stderr": 0.015201522246299979,
"mc2": 0.4002214148044727,
"mc2_stderr": 0.014908452990717655
},
"harness|arc:challenge|25": {
"acc": 0.32081911262798635,
"acc_stderr": 0.013640943091946522,
"acc_norm": 0.3651877133105802,
"acc_norm_stderr": 0.014070265519268802
},
"harness|hellaswag|10": {
"acc": 0.4574785899223262,
"acc_stderr": 0.004971704917267752,
"acc_norm": 0.6062537343158734,
"acc_norm_stderr": 0.004875812021461993
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.42962962962962964,
"acc_stderr": 0.04276349494376599,
"acc_norm": 0.42962962962962964,
"acc_norm_stderr": 0.04276349494376599
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5131578947368421,
"acc_stderr": 0.04067533136309173,
"acc_norm": 0.5131578947368421,
"acc_norm_stderr": 0.04067533136309173
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4830188679245283,
"acc_stderr": 0.030755120364119898,
"acc_norm": 0.4830188679245283,
"acc_norm_stderr": 0.030755120364119898
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.375,
"acc_stderr": 0.04048439222695598,
"acc_norm": 0.375,
"acc_norm_stderr": 0.04048439222695598
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4393063583815029,
"acc_stderr": 0.03784271932887467,
"acc_norm": 0.4393063583815029,
"acc_norm_stderr": 0.03784271932887467
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3431372549019608,
"acc_stderr": 0.04724007352383889,
"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.04724007352383889
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3829787234042553,
"acc_stderr": 0.03177821250236922,
"acc_norm": 0.3829787234042553,
"acc_norm_stderr": 0.03177821250236922
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.04303684033537315,
"acc_norm": 0.2982456140350877,
"acc_norm_stderr": 0.04303684033537315
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.42758620689655175,
"acc_stderr": 0.041227371113703316,
"acc_norm": 0.42758620689655175,
"acc_norm_stderr": 0.041227371113703316
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.023919984164047732,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.023919984164047732
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.04285714285714281,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.04285714285714281
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5225806451612903,
"acc_stderr": 0.02841498501970786,
"acc_norm": 0.5225806451612903,
"acc_norm_stderr": 0.02841498501970786
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4088669950738916,
"acc_stderr": 0.034590588158832314,
"acc_norm": 0.4088669950738916,
"acc_norm_stderr": 0.034590588158832314
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.5333333333333333,
"acc_stderr": 0.038956580652718446,
"acc_norm": 0.5333333333333333,
"acc_norm_stderr": 0.038956580652718446
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5303030303030303,
"acc_stderr": 0.03555804051763929,
"acc_norm": 0.5303030303030303,
"acc_norm_stderr": 0.03555804051763929
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6010362694300518,
"acc_stderr": 0.03533999094065696,
"acc_norm": 0.6010362694300518,
"acc_norm_stderr": 0.03533999094065696
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.43333333333333335,
"acc_stderr": 0.02512465352588513,
"acc_norm": 0.43333333333333335,
"acc_norm_stderr": 0.02512465352588513
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.25555555555555554,
"acc_stderr": 0.02659393910184405,
"acc_norm": 0.25555555555555554,
"acc_norm_stderr": 0.02659393910184405
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.4495798319327731,
"acc_stderr": 0.03231293497137707,
"acc_norm": 0.4495798319327731,
"acc_norm_stderr": 0.03231293497137707
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.0386155754625517,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.0386155754625517
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5614678899082569,
"acc_stderr": 0.021274713073954572,
"acc_norm": 0.5614678899082569,
"acc_norm_stderr": 0.021274713073954572
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4398148148148148,
"acc_stderr": 0.03385177976044811,
"acc_norm": 0.4398148148148148,
"acc_norm_stderr": 0.03385177976044811
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5147058823529411,
"acc_stderr": 0.03507793834791324,
"acc_norm": 0.5147058823529411,
"acc_norm_stderr": 0.03507793834791324
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6033755274261603,
"acc_stderr": 0.03184399873811224,
"acc_norm": 0.6033755274261603,
"acc_norm_stderr": 0.03184399873811224
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.4977578475336323,
"acc_stderr": 0.033557465352232634,
"acc_norm": 0.4977578475336323,
"acc_norm_stderr": 0.033557465352232634
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5114503816793893,
"acc_stderr": 0.043841400240780176,
"acc_norm": 0.5114503816793893,
"acc_norm_stderr": 0.043841400240780176
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6033057851239669,
"acc_stderr": 0.044658697805310094,
"acc_norm": 0.6033057851239669,
"acc_norm_stderr": 0.044658697805310094
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.048262172941398944,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.048262172941398944
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.44171779141104295,
"acc_stderr": 0.03901591825836184,
"acc_norm": 0.44171779141104295,
"acc_norm_stderr": 0.03901591825836184
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.26785714285714285,
"acc_stderr": 0.04203277291467762,
"acc_norm": 0.26785714285714285,
"acc_norm_stderr": 0.04203277291467762
},
"harness|hendrycksTest-management|5": {
"acc": 0.6699029126213593,
"acc_stderr": 0.046561471100123514,
"acc_norm": 0.6699029126213593,
"acc_norm_stderr": 0.046561471100123514
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.6709401709401709,
"acc_stderr": 0.03078232157768817,
"acc_norm": 0.6709401709401709,
"acc_norm_stderr": 0.03078232157768817
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5504469987228607,
"acc_stderr": 0.017788725283507337,
"acc_norm": 0.5504469987228607,
"acc_norm_stderr": 0.017788725283507337
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.48265895953757226,
"acc_stderr": 0.026902900458666647,
"acc_norm": 0.48265895953757226,
"acc_norm_stderr": 0.026902900458666647
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24804469273743016,
"acc_stderr": 0.01444415780826144,
"acc_norm": 0.24804469273743016,
"acc_norm_stderr": 0.01444415780826144
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5620915032679739,
"acc_stderr": 0.02840830202033269,
"acc_norm": 0.5620915032679739,
"acc_norm_stderr": 0.02840830202033269
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5048231511254019,
"acc_stderr": 0.028396770444111298,
"acc_norm": 0.5048231511254019,
"acc_norm_stderr": 0.028396770444111298
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.45987654320987653,
"acc_stderr": 0.02773102275353928,
"acc_norm": 0.45987654320987653,
"acc_norm_stderr": 0.02773102275353928
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.37943262411347517,
"acc_stderr": 0.028947338851614105,
"acc_norm": 0.37943262411347517,
"acc_norm_stderr": 0.028947338851614105
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3324641460234681,
"acc_stderr": 0.012032022332260507,
"acc_norm": 0.3324641460234681,
"acc_norm_stderr": 0.012032022332260507
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4007352941176471,
"acc_stderr": 0.029768263528933102,
"acc_norm": 0.4007352941176471,
"acc_norm_stderr": 0.029768263528933102
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.019835176484375376,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.019835176484375376
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5363636363636364,
"acc_stderr": 0.04776449162396197,
"acc_norm": 0.5363636363636364,
"acc_norm_stderr": 0.04776449162396197
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5387755102040817,
"acc_stderr": 0.031912820526692774,
"acc_norm": 0.5387755102040817,
"acc_norm_stderr": 0.031912820526692774
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.582089552238806,
"acc_stderr": 0.034875586404620636,
"acc_norm": 0.582089552238806,
"acc_norm_stderr": 0.034875586404620636
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-virology|5": {
"acc": 0.40963855421686746,
"acc_stderr": 0.03828401115079023,
"acc_norm": 0.40963855421686746,
"acc_norm_stderr": 0.03828401115079023
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5497076023391813,
"acc_stderr": 0.038158273659132366,
"acc_norm": 0.5497076023391813,
"acc_norm_stderr": 0.038158273659132366
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2521419828641371,
"mc1_stderr": 0.015201522246299979,
"mc2": 0.4002214148044727,
"mc2_stderr": 0.014908452990717655
},
"harness|winogrande|5": {
"acc": 0.5935280189423836,
"acc_stderr": 0.013804448697753376
},
"harness|gsm8k|5": {
"acc": 0.1561789234268385,
"acc_stderr": 0.00999950936975745
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_KnutJaegersberg__Deita-1_8B | [
"region:us"
] | 2024-01-17T18:24:58+00:00 | {"pretty_name": "Evaluation run of KnutJaegersberg/Deita-1_8B", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deita-1_8B](https://huggingface.co/KnutJaegersberg/Deita-1_8B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Deita-1_8B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T18:22:52.012956](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-1_8B/blob/main/results_2024-01-17T18-22-52.012956.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4513695685616451,\n \"acc_stderr\": 0.03473174413713779,\n \"acc_norm\": 0.4572369573723266,\n \"acc_norm_stderr\": 0.035511285827617124,\n \"mc1\": 0.2521419828641371,\n \"mc1_stderr\": 0.015201522246299979,\n \"mc2\": 0.4002214148044727,\n \"mc2_stderr\": 0.014908452990717655\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.32081911262798635,\n \"acc_stderr\": 0.013640943091946522,\n \"acc_norm\": 0.3651877133105802,\n \"acc_norm_stderr\": 0.014070265519268802\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4574785899223262,\n \"acc_stderr\": 0.004971704917267752,\n \"acc_norm\": 0.6062537343158734,\n \"acc_norm_stderr\": 0.004875812021461993\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.42962962962962964,\n \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309173,\n \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309173\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.4830188679245283,\n \"acc_stderr\": 0.030755120364119898,\n \"acc_norm\": 0.4830188679245283,\n \"acc_norm_stderr\": 0.030755120364119898\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4393063583815029,\n \"acc_stderr\": 0.03784271932887467,\n \"acc_norm\": 0.4393063583815029,\n \"acc_norm_stderr\": 0.03784271932887467\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383889,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383889\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3829787234042553,\n \"acc_stderr\": 0.03177821250236922,\n \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.03177821250236922\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.023919984164047732,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.023919984164047732\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.04285714285714281\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5225806451612903,\n \"acc_stderr\": 0.02841498501970786,\n \"acc_norm\": 0.5225806451612903,\n \"acc_norm_stderr\": 0.02841498501970786\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4088669950738916,\n \"acc_stderr\": 0.034590588158832314,\n \"acc_norm\": 0.4088669950738916,\n \"acc_norm_stderr\": 0.034590588158832314\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.038956580652718446,\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.038956580652718446\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5303030303030303,\n \"acc_stderr\": 0.03555804051763929,\n \"acc_norm\": 0.5303030303030303,\n \"acc_norm_stderr\": 0.03555804051763929\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6010362694300518,\n \"acc_stderr\": 0.03533999094065696,\n \"acc_norm\": 0.6010362694300518,\n \"acc_norm_stderr\": 0.03533999094065696\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.43333333333333335,\n \"acc_stderr\": 0.02512465352588513,\n \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.02512465352588513\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.25555555555555554,\n \"acc_stderr\": 0.02659393910184405,\n \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.02659393910184405\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.4495798319327731,\n \"acc_stderr\": 0.03231293497137707,\n \"acc_norm\": 0.4495798319327731,\n \"acc_norm_stderr\": 0.03231293497137707\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5614678899082569,\n \"acc_stderr\": 0.021274713073954572,\n \"acc_norm\": 0.5614678899082569,\n \"acc_norm_stderr\": 0.021274713073954572\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4398148148148148,\n \"acc_stderr\": 0.03385177976044811,\n \"acc_norm\": 0.4398148148148148,\n \"acc_norm_stderr\": 0.03385177976044811\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03507793834791324,\n \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03507793834791324\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6033755274261603,\n \"acc_stderr\": 0.03184399873811224,\n \"acc_norm\": 0.6033755274261603,\n \"acc_norm_stderr\": 0.03184399873811224\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4977578475336323,\n \"acc_stderr\": 0.033557465352232634,\n \"acc_norm\": 0.4977578475336323,\n \"acc_norm_stderr\": 0.033557465352232634\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5114503816793893,\n \"acc_stderr\": 0.043841400240780176,\n \"acc_norm\": 0.5114503816793893,\n \"acc_norm_stderr\": 0.043841400240780176\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.048262172941398944,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.048262172941398944\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.44171779141104295,\n \"acc_stderr\": 0.03901591825836184,\n \"acc_norm\": 0.44171779141104295,\n \"acc_norm_stderr\": 0.03901591825836184\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.046561471100123514,\n \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.046561471100123514\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6709401709401709,\n \"acc_stderr\": 0.03078232157768817,\n \"acc_norm\": 0.6709401709401709,\n \"acc_norm_stderr\": 0.03078232157768817\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5504469987228607,\n \"acc_stderr\": 0.017788725283507337,\n \"acc_norm\": 0.5504469987228607,\n \"acc_norm_stderr\": 0.017788725283507337\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.48265895953757226,\n \"acc_stderr\": 0.026902900458666647,\n \"acc_norm\": 0.48265895953757226,\n \"acc_norm_stderr\": 0.026902900458666647\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24804469273743016,\n \"acc_stderr\": 0.01444415780826144,\n \"acc_norm\": 0.24804469273743016,\n \"acc_norm_stderr\": 0.01444415780826144\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5620915032679739,\n \"acc_stderr\": 0.02840830202033269,\n \"acc_norm\": 0.5620915032679739,\n \"acc_norm_stderr\": 0.02840830202033269\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5048231511254019,\n \"acc_stderr\": 0.028396770444111298,\n \"acc_norm\": 0.5048231511254019,\n \"acc_norm_stderr\": 0.028396770444111298\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.45987654320987653,\n \"acc_stderr\": 0.02773102275353928,\n \"acc_norm\": 0.45987654320987653,\n \"acc_norm_stderr\": 0.02773102275353928\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.37943262411347517,\n \"acc_stderr\": 0.028947338851614105,\n \"acc_norm\": 0.37943262411347517,\n \"acc_norm_stderr\": 0.028947338851614105\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3324641460234681,\n \"acc_stderr\": 0.012032022332260507,\n \"acc_norm\": 0.3324641460234681,\n \"acc_norm_stderr\": 0.012032022332260507\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4007352941176471,\n \"acc_stderr\": 0.029768263528933102,\n \"acc_norm\": 0.4007352941176471,\n \"acc_norm_stderr\": 0.029768263528933102\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.019835176484375376,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.019835176484375376\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5363636363636364,\n \"acc_stderr\": 0.04776449162396197,\n \"acc_norm\": 0.5363636363636364,\n \"acc_norm_stderr\": 0.04776449162396197\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5387755102040817,\n \"acc_stderr\": 0.031912820526692774,\n \"acc_norm\": 0.5387755102040817,\n \"acc_norm_stderr\": 0.031912820526692774\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.582089552238806,\n \"acc_stderr\": 0.034875586404620636,\n \"acc_norm\": 0.582089552238806,\n \"acc_norm_stderr\": 0.034875586404620636\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n \"acc_stderr\": 0.03828401115079023,\n \"acc_norm\": 0.40963855421686746,\n \"acc_norm_stderr\": 0.03828401115079023\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.5497076023391813,\n \"acc_stderr\": 0.038158273659132366,\n \"acc_norm\": 0.5497076023391813,\n \"acc_norm_stderr\": 0.038158273659132366\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2521419828641371,\n \"mc1_stderr\": 0.015201522246299979,\n \"mc2\": 0.4002214148044727,\n \"mc2_stderr\": 0.014908452990717655\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5935280189423836,\n \"acc_stderr\": 0.013804448697753376\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1561789234268385,\n \"acc_stderr\": 0.00999950936975745\n }\n}\n```", "repo_url": "https://huggingface.co/KnutJaegersberg/Deita-1_8B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|arc:challenge|25_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|gsm8k|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hellaswag|10_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T18-22-52.012956.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_abstract_algebra_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_anatomy_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_astronomy_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_business_ethics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_computer_science_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_mathematics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_medicine_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_physics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_computer_security_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_conceptual_physics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_econometrics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_electrical_engineering_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_elementary_mathematics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_formal_logic_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_global_facts_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_european_history_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_geography_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_government_and_politics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_macroeconomics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_mathematics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_microeconomics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_physics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_psychology_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_statistics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_us_history_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["**/details_harness|winogrande|5_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-17T18-22-52.012956.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_17T18_22_52.012956", "path": ["results_2024-01-17T18-22-52.012956.parquet"]}, {"split": "latest", "path": ["results_2024-01-17T18-22-52.012956.parquet"]}]}]} | 2024-01-17T18:25:21+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of KnutJaegersberg/Deita-1_8B
Dataset automatically created during the evaluation run of model KnutJaegersberg/Deita-1_8B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-17T18:22:52.012956(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of KnutJaegersberg/Deita-1_8B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-1_8B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T18:22:52.012956(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of KnutJaegersberg/Deita-1_8B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-1_8B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T18:22:52.012956(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
81806b77c79d0a34088d236ddf155c158d58abe5 |
# Dataset of chiki (Fire Emblem)
This is the dataset of chiki (Fire Emblem), containing 500 images and their tags.
The core tags of this character are `green_hair, long_hair, pointy_ears, green_eyes, ponytail, breasts, large_breasts, hair_ornament, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 710.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiki_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 386.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiki_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1218 | 824.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiki_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 619.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiki_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1218 | 1.16 GiB | [Download](https://huggingface.co/datasets/CyberHarem/chiki_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/chiki_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 32 |  |  |  |  |  | 1girl, solo, short_dress, cleavage, red_dress, bracelet, smile, hair_ribbon, garter_straps, red_gloves, tiara, simple_background, looking_at_viewer, thigh_boots, pink_thighhighs, side_slit, choker, pink_cape, red_footwear, strapless, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, bracelet, looking_at_viewer, pink_dress, ribbon, short_dress, side_slit, sleeveless_dress, solo, tiara, gem, official_alternate_costume, sash, smile, thighs, sideboob, sidelocks, simple_background, stone, bare_shoulders, closed_mouth, medium_breasts |
| 2 | 6 |  |  |  |  |  | 1girl, bracelet, full_body, shiny_hair, short_dress, simple_background, solo, thighs, tiara, white_background, medium_breasts, pink_dress, gold_trim, leg_up, looking_at_viewer, parted_lips, hands_up, sleeveless_dress, smile |
| 3 | 17 |  |  |  |  |  | 1girl, navel, smile, solo, cleavage, red_bikini, looking_at_viewer, beach, official_alternate_costume, sitting, hair_ribbon, outdoors, sand, sarong, tiara |
| 4 | 22 |  |  |  |  |  | smile, wedding_dress, white_dress, 1girl, tiara, solo, bridal_veil, official_alternate_costume, looking_at_viewer, bride, bouquet, rose, simple_background, bare_shoulders, hair_flower, open_mouth, red_flower |
| 5 | 7 |  |  |  |  |  | 1girl, blue_eyes, solo, cleavage, blush, open_mouth, looking_at_viewer, smile, bare_shoulders, dress, ribbon |
| 6 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, nipples, solo, completely_nude, navel, female_pubic_hair, pussy, smile, tiara, closed_mouth, medium_breasts, sitting, sweat |
| 7 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, penis, solo_focus, blush, looking_at_viewer, tiara, completely_nude, heart, breast_press, fellatio, handjob, paizuri, uncensored |
| 8 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, penis, pussy, sex, solo_focus, vaginal, open_mouth, spread_legs, tiara, cowgirl_position, cum, girl_on_top, breasts_out, mosaic_censoring, navel, nude, sweat |
| 9 | 10 |  |  |  |  |  | 1girl, hair_flower, solo, thighs, bare_shoulders, blush, circlet, looking_at_viewer, parted_bangs, smile, cleavage, collarbone, navel, crop_top, parted_lips, shirt, tassel, tight_pants, yoga_pants |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | short_dress | cleavage | red_dress | bracelet | smile | hair_ribbon | garter_straps | red_gloves | tiara | simple_background | looking_at_viewer | thigh_boots | pink_thighhighs | side_slit | choker | pink_cape | red_footwear | strapless | white_background | pink_dress | ribbon | sleeveless_dress | gem | official_alternate_costume | sash | thighs | sideboob | sidelocks | stone | bare_shoulders | closed_mouth | medium_breasts | full_body | shiny_hair | gold_trim | leg_up | parted_lips | hands_up | navel | red_bikini | beach | sitting | outdoors | sand | sarong | wedding_dress | white_dress | bridal_veil | bride | bouquet | rose | hair_flower | open_mouth | red_flower | blue_eyes | blush | dress | nipples | completely_nude | female_pubic_hair | pussy | sweat | 1boy | hetero | penis | solo_focus | heart | breast_press | fellatio | handjob | paizuri | uncensored | sex | vaginal | spread_legs | cowgirl_position | cum | girl_on_top | breasts_out | mosaic_censoring | nude | circlet | parted_bangs | collarbone | crop_top | shirt | tassel | tight_pants | yoga_pants |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------|:-----------|:------------|:-----------|:--------|:--------------|:----------------|:-------------|:--------|:--------------------|:--------------------|:--------------|:------------------|:------------|:---------|:------------|:---------------|:------------|:-------------------|:-------------|:---------|:-------------------|:------|:-----------------------------|:-------|:---------|:-----------|:------------|:--------|:-----------------|:---------------|:-----------------|:------------|:-------------|:------------|:---------|:--------------|:-----------|:--------|:-------------|:--------|:----------|:-----------|:-------|:---------|:----------------|:--------------|:--------------|:--------|:----------|:-------|:--------------|:-------------|:-------------|:------------|:--------|:--------|:----------|:------------------|:--------------------|:--------|:--------|:-------|:---------|:--------|:-------------|:--------|:---------------|:-----------|:----------|:----------|:-------------|:------|:----------|:--------------|:-------------------|:------|:--------------|:--------------|:-------------------|:-------|:----------|:---------------|:-------------|:-----------|:--------|:---------|:--------------|:-------------|
| 0 | 32 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | X | X | | | | X | X | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | | | X | X | | | | X | X | X | | | | | | | | X | X | | X | | | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 17 |  |  |  |  |  | X | X | | X | | | X | X | | | X | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 22 |  |  |  |  |  | X | X | | | | | X | | | | X | X | X | | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | X | | X | | | X | | | | | | X | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | | | | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | X | | | X | | | | | | | | | | | | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | X | | X | | | X | X | X | X | X | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 9 | 10 |  |  |  |  |  | X | X | | X | | | X | | | | | | X | | | | | | | | | | | | | | | X | | | | X | | | | | | | X | | X | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X |
| CyberHarem/chiki_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T18:32:20+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:16:58+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of chiki (Fire Emblem)
==============================
This is the dataset of chiki (Fire Emblem), containing 500 images and their tags.
The core tags of this character are 'green\_hair, long\_hair, pointy\_ears, green\_eyes, ponytail, breasts, large\_breasts, hair\_ornament, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
e29b2e4ec32ae6e4aa54576d8d5612223e2ed07d |
# Dataset of hapi (Fire Emblem)
This is the dataset of hapi (Fire Emblem), containing 205 images and their tags.
The core tags of this character are `red_hair, dark_skin, dark-skinned_female, red_eyes, long_hair, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 205 | 239.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hapi_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 205 | 143.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hapi_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 470 | 298.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hapi_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 205 | 217.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hapi_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 470 | 410.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hapi_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hapi_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 29 |  |  |  |  |  | 1girl, garreg_mach_monastery_uniform, solo, crop_top, simple_background, upper_body, midriff, long_sleeves, navel, white_background, closed_mouth, medium_hair |
| 1 | 7 |  |  |  |  |  | 1girl, black_footwear, crop_top, garreg_mach_monastery_uniform, midriff, skirt, solo, thigh_boots, thighhighs, navel, bracelet, closed_mouth, full_body, knee_boots, pink_eyes, simple_background, high_heel_boots, sitting, white_background |
| 2 | 9 |  |  |  |  |  | 1girl, crop_top, garreg_mach_monastery_uniform, midriff, miniskirt, navel, stomach, bangs, open_jacket, solo, white_jacket, white_skirt, long_sleeves, bracelet, cowboy_shot, hair_between_eyes, looking_at_viewer, zettai_ryouiki, cropped_jacket, groin, white_background, closed_mouth, medium_breasts, purple_thighhighs, shiny, shirt, simple_background |
| 3 | 7 |  |  |  |  |  | 1girl, large_breasts, shoulder_armor, cleavage, solo, looking_at_viewer, official_alternate_costume, official_alternate_hairstyle, brown_gloves, closed_mouth, smile, upper_body, belt, simple_background, white_background |
| 4 | 9 |  |  |  |  |  | 1girl, earrings, solo, looking_at_viewer, cleavage, large_breasts, thighhighs, ass, cape, parted_lips, boots, circlet, simple_background |
| 5 | 9 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, large_breasts, solo_focus, blush, penis, facial, paizuri, completely_nude, cum_on_breasts, earrings, smile |
| 6 | 9 |  |  |  |  |  | 1girl, hetero, nipples, penis, sex, solo_focus, vaginal, cum_in_pussy, blush, open_mouth, thighhighs, 1boy, large_breasts, 2boys, bar_censor, medium_breasts, medium_hair, mosaic_censoring, pubic_hair, rape, spread_legs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | garreg_mach_monastery_uniform | solo | crop_top | simple_background | upper_body | midriff | long_sleeves | navel | white_background | closed_mouth | medium_hair | black_footwear | skirt | thigh_boots | thighhighs | bracelet | full_body | knee_boots | pink_eyes | high_heel_boots | sitting | miniskirt | stomach | bangs | open_jacket | white_jacket | white_skirt | cowboy_shot | hair_between_eyes | looking_at_viewer | zettai_ryouiki | cropped_jacket | groin | medium_breasts | purple_thighhighs | shiny | shirt | large_breasts | shoulder_armor | cleavage | official_alternate_costume | official_alternate_hairstyle | brown_gloves | smile | belt | earrings | ass | cape | parted_lips | boots | circlet | 1boy | hetero | nipples | solo_focus | blush | penis | facial | paizuri | completely_nude | cum_on_breasts | sex | vaginal | cum_in_pussy | open_mouth | 2boys | bar_censor | mosaic_censoring | pubic_hair | rape | spread_legs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------------------|:-------|:-----------|:--------------------|:-------------|:----------|:---------------|:--------|:-------------------|:---------------|:--------------|:-----------------|:--------|:--------------|:-------------|:-----------|:------------|:-------------|:------------|:------------------|:----------|:------------|:----------|:--------|:--------------|:---------------|:--------------|:--------------|:--------------------|:--------------------|:-----------------|:-----------------|:--------|:-----------------|:--------------------|:--------|:--------|:----------------|:-----------------|:-----------|:-----------------------------|:-------------------------------|:---------------|:--------|:-------|:-----------|:------|:-------|:--------------|:--------|:----------|:-------|:---------|:----------|:-------------|:--------|:--------|:---------|:----------|:------------------|:-----------------|:------|:----------|:---------------|:-------------|:--------|:-------------|:-------------------|:-------------|:-------|:--------------|
| 0 | 29 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | X | X | X | | X | X | X | X | X | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | X | | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | X | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | X | | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | X | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/hapi_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T18:32:42+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:19:12+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hapi (Fire Emblem)
=============================
This is the dataset of hapi (Fire Emblem), containing 205 images and their tags.
The core tags of this character are 'red\_hair, dark\_skin, dark-skinned\_female, red\_eyes, long\_hair, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
86e8ba701ddd60349cba13701ad0628a13bc7c1b |
# MMC (Multilingual Multiparty Coreference)
- Project: https://github.com/boyuanzheng010/mmc
- Data source: https://github.com/boyuanzheng010/mmc/commit/a7007d1d4556a3f4347a3d7b686f71d66bd1e2d9
## Details
Data for the paper "Multilingual Coreference Resolution in Multiparty Dialogue" TACL 2023
## Citation
```
@article{zheng-etal-2023-multilingual,
title = "Multilingual Coreference Resolution in Multiparty Dialogue",
author = "Zheng, Boyuan and
Xia, Patrick and
Yarmohammadi, Mahsa and
Van Durme, Benjamin",
journal = "Transactions of the Association for Computational Linguistics",
volume = "11",
year = "2023",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2023.tacl-1.52",
doi = "10.1162/tacl_a_00581",
pages = "922--940",
abstract = "Existing multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts. Due to the availability of gold-quality subtitles in multiple languages, we propose reusing the annotations to create silver coreference resolution data in other languages (Chinese and Farsi) via annotation projection. On the gold (English) data, off-the-shelf models perform relatively poorly on MMC, suggesting that MMC has broader coverage of multiparty coreference than prior datasets. On the silver data, we find success both using it for data augmentation and training from scratch, which effectively simulates the zero-shot cross-lingual setting.",
}
``` | coref-data/mmc_raw | [
"license:apache-2.0",
"region:us"
] | 2024-01-17T18:33:10+00:00 | {"license": "apache-2.0", "configs": [{"config_name": "mmc_en", "data_files": [{"split": "train", "path": "mmc_en/train-*"}, {"split": "dev", "path": "mmc_en/dev-*"}, {"split": "test", "path": "mmc_en/test-*"}]}, {"config_name": "mmc_fa", "data_files": [{"split": "train", "path": "mmc_fa/train-*"}, {"split": "dev", "path": "mmc_fa/dev-*"}, {"split": "test", "path": "mmc_fa/test-*"}]}, {"config_name": "mmc_fa_corrected", "data_files": [{"split": "train", "path": "mmc_fa_corrected/train-*"}, {"split": "dev", "path": "mmc_fa_corrected/dev-*"}, {"split": "test", "path": "mmc_fa_corrected/test-*"}]}, {"config_name": "mmc_zh_corrected", "data_files": [{"split": "train", "path": "mmc_zh_corrected/train-*"}, {"split": "dev", "path": "mmc_zh_corrected/dev-*"}, {"split": "test", "path": "mmc_zh_corrected/test-*"}]}, {"config_name": "mmc_zh_uncorrected", "data_files": [{"split": "train", "path": "mmc_zh_uncorrected/train-*"}, {"split": "dev", "path": "mmc_zh_uncorrected/dev-*"}, {"split": "test", "path": "mmc_zh_uncorrected/test-*"}]}]} | 2024-01-19T00:03:40+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
# MMC (Multilingual Multiparty Coreference)
- Project: URL
- Data source: URL
## Details
Data for the paper "Multilingual Coreference Resolution in Multiparty Dialogue" TACL 2023
| [
"# MMC (Multilingual Multiparty Coreference)\n\n- Project: URL\n- Data source: URL",
"## Details\n\nData for the paper \"Multilingual Coreference Resolution in Multiparty Dialogue\" TACL 2023"
] | [
"TAGS\n#license-apache-2.0 #region-us \n",
"# MMC (Multilingual Multiparty Coreference)\n\n- Project: URL\n- Data source: URL",
"## Details\n\nData for the paper \"Multilingual Coreference Resolution in Multiparty Dialogue\" TACL 2023"
] |
8f44ef9892fa9ae0d0fe83471612f3b9350d2ec2 |
# Dataset of syalla (Fire Emblem)
This is the dataset of syalla (Fire Emblem), containing 94 images and their tags.
The core tags of this character are `black_hair, long_hair, breasts, bangs, blunt_bangs, hair_ornament, large_breasts, two_side_up, hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 94 | 103.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/syalla_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 94 | 57.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/syalla_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 210 | 112.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/syalla_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 94 | 90.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/syalla_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 210 | 160.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/syalla_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/syalla_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | looking_at_viewer, 1girl, blush, navel, completely_nude, pussy, solo, cleft_of_venus, huge_breasts, lactation, simple_background, smile, brown_eyes, inverted_nipples, uncensored, white_background, arms_behind_back, collarbone, cowboy_shot |
| 1 | 32 |  |  |  |  |  | 1girl, solo, bracelet, bridal_gauntlets, cleavage, looking_at_viewer, smile, black_eyes, bodystocking, simple_background, white_background |
| 2 | 9 |  |  |  |  |  | hetero, penis, 1girl, blush, nipples, sex, torn_clothes, 1boy, solo_focus, vaginal, uncensored, open_mouth, spread_legs, bodystocking, cum_in_pussy, stomach_bulge, testicles |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | 1girl | blush | navel | completely_nude | pussy | solo | cleft_of_venus | huge_breasts | lactation | simple_background | smile | brown_eyes | inverted_nipples | uncensored | white_background | arms_behind_back | collarbone | cowboy_shot | bracelet | bridal_gauntlets | cleavage | black_eyes | bodystocking | hetero | penis | nipples | sex | torn_clothes | 1boy | solo_focus | vaginal | open_mouth | spread_legs | cum_in_pussy | stomach_bulge | testicles |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:--------|:--------|:------------------|:--------|:-------|:-----------------|:---------------|:------------|:--------------------|:--------|:-------------|:-------------------|:-------------|:-------------------|:-------------------|:-------------|:--------------|:-----------|:-------------------|:-----------|:-------------|:---------------|:---------|:--------|:----------|:------|:---------------|:-------|:-------------|:----------|:-------------|:--------------|:---------------|:----------------|:------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 32 |  |  |  |  |  | X | X | | | | | X | | | | X | X | | | | X | | | | X | X | X | X | X | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | | X | X | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/syalla_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T18:56:03+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:17:18+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of syalla (Fire Emblem)
===============================
This is the dataset of syalla (Fire Emblem), containing 94 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, breasts, bangs, blunt\_bangs, hair\_ornament, large\_breasts, two\_side\_up, hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
5639c0a2234b712c1e8776d78d1c8507f715f511 |
# Dataset of sheeda (Fire Emblem)
This is the dataset of sheeda (Fire Emblem), containing 427 images and their tags.
The core tags of this character are `blue_hair, long_hair, blue_eyes, breasts, large_breasts, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 427 | 510.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 427 | 315.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 961 | 630.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 427 | 463.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 961 | 846.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sheeda_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, navel, nipples, blush, solo, looking_at_viewer, open_mouth, completely_nude, collarbone, pussy, sitting, smile, censored |
| 1 | 8 |  |  |  |  |  | 1girl, elbow_gloves, pegasus_knight_uniform_(fire_emblem), smile, solo, simple_background, belt, breastplate, thighhighs, zettai_ryouiki, fingerless_gloves, looking_at_viewer, shoulder_armor, side_slit, white_background, armored_dress |
| 2 | 7 |  |  |  |  |  | 1girl, blush, elbow_gloves, looking_at_viewer, red_dress, short_dress, white_gloves, shoulder_armor, solo, thighs, black_thighhighs, boots, breastplate, pantyshot, pegasus_knight_uniform_(fire_emblem), short_sleeves, white_panties, embarrassed, from_behind, looking_back, upskirt, garter_straps, holding, simple_background, white_background |
| 3 | 5 |  |  |  |  |  | 1girl, red_dress, solo, thighs, blush, closed_mouth, hair_between_eyes, looking_at_viewer, short_dress, short_sleeves, white_panties, ass, cameltoe, partially_visible_vulva, sitting, smile, black_thighhighs, heart, impossible_clothes, on_back, spread_legs |
| 4 | 10 |  |  |  |  |  | 1girl, hat, solo, simple_background, smile, white_background, boots, bracelet, looking_at_viewer, pantyhose, white_dress, cape, holding_book, elbow_gloves, full_body, medium_breasts, open_mouth, shiny_hair, white_footwear, white_gloves |
| 5 | 15 |  |  |  |  |  | 1girl, bride, wedding_dress, white_dress, hair_flower, smile, bouquet, solo, bare_shoulders, gloves, blush, simple_background, open_mouth, bridal_veil, looking_at_viewer, strapless, official_alternate_costume |
| 6 | 7 |  |  |  |  |  | 1girl, hair_flower, looking_at_viewer, red_bikini, smile, solo, closed_mouth, official_alternate_costume, blush, navel, simple_background, white_background, cowboy_shot, bare_shoulders, belt, bracelet, holding_staff, midriff, see-through, skirt |
| 7 | 5 |  |  |  |  |  | 1girl, hair_flower, looking_at_viewer, navel, official_alternate_costume, red_bikini, solo, beach, bracelet, open_mouth, water, blush, smile, day, lying, outdoors, sky |
| 8 | 7 |  |  |  |  |  | 1girl, hetero, nipples, solo_focus, 3boys, blush, gangbang, multiple_penises, vaginal, handjob, mosaic_censoring, open_mouth, thighhighs, cum_in_pussy, facial, medium_breasts, torn_clothes, bukkake, closed_eyes, cowgirl_position, cum_on_breasts, elbow_gloves, fellatio, white_gloves |
| 9 | 10 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, open_mouth, solo_focus, sweat, navel, penis, cum_in_pussy, vaginal, collarbone, mosaic_censoring, sex_from_behind, thighhighs, breast_grab, completely_nude, grabbing_from_behind, hair_ornament, heart-shaped_pupils, spread_legs, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | navel | nipples | blush | solo | looking_at_viewer | open_mouth | completely_nude | collarbone | pussy | sitting | smile | censored | elbow_gloves | pegasus_knight_uniform_(fire_emblem) | simple_background | belt | breastplate | thighhighs | zettai_ryouiki | fingerless_gloves | shoulder_armor | side_slit | white_background | armored_dress | red_dress | short_dress | white_gloves | thighs | black_thighhighs | boots | pantyshot | short_sleeves | white_panties | embarrassed | from_behind | looking_back | upskirt | garter_straps | holding | closed_mouth | hair_between_eyes | ass | cameltoe | partially_visible_vulva | heart | impossible_clothes | on_back | spread_legs | hat | bracelet | pantyhose | white_dress | cape | holding_book | full_body | medium_breasts | shiny_hair | white_footwear | bride | wedding_dress | hair_flower | bouquet | bare_shoulders | gloves | bridal_veil | strapless | official_alternate_costume | red_bikini | cowboy_shot | holding_staff | midriff | see-through | skirt | beach | water | day | lying | outdoors | sky | hetero | solo_focus | 3boys | gangbang | multiple_penises | vaginal | handjob | mosaic_censoring | cum_in_pussy | facial | torn_clothes | bukkake | closed_eyes | cowgirl_position | cum_on_breasts | fellatio | 1boy | sweat | penis | sex_from_behind | breast_grab | grabbing_from_behind | hair_ornament | heart-shaped_pupils | standing |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------|:--------|:-------|:--------------------|:-------------|:------------------|:-------------|:--------|:----------|:--------|:-----------|:---------------|:---------------------------------------|:--------------------|:-------|:--------------|:-------------|:-----------------|:--------------------|:-----------------|:------------|:-------------------|:----------------|:------------|:--------------|:---------------|:---------|:-------------------|:--------|:------------|:----------------|:----------------|:--------------|:--------------|:---------------|:----------|:----------------|:----------|:---------------|:--------------------|:------|:-----------|:--------------------------|:--------|:---------------------|:----------|:--------------|:------|:-----------|:------------|:--------------|:-------|:---------------|:------------|:-----------------|:-------------|:-----------------|:--------|:----------------|:--------------|:----------|:-----------------|:---------|:--------------|:------------|:-----------------------------|:-------------|:--------------|:----------------|:----------|:--------------|:--------|:--------|:--------|:------|:--------|:-----------|:------|:---------|:-------------|:--------|:-----------|:-------------------|:----------|:----------|:-------------------|:---------------|:---------|:---------------|:----------|:--------------|:-------------------|:-----------------|:-----------|:-------|:--------|:--------|:------------------|:--------------|:-----------------------|:----------------|:----------------------|:-----------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | | | X | X | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | | X | X | X | | | | | | | | X | X | X | | X | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | X | X | X | | | | | X | X | | | | | | | | | | | | | | X | X | | X | X | | | X | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | | | | X | X | X | | | | | X | | X | | X | | | | | | | | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | | | X | X | X | X | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | | X | X | X | | | | | | X | | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | X | | X | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | | X | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | | | | X | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | X | X | | | X | | | | | | | X | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 9 | 10 |  |  |  |  |  | X | X | X | X | | | X | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | X | | X | X | | | | | | | | X | X | X | X | X | X | X | X | X |
| CyberHarem/sheeda_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T18:56:15+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:32:28+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of sheeda (Fire Emblem)
===============================
This is the dataset of sheeda (Fire Emblem), containing 427 images and their tags.
The core tags of this character are 'blue\_hair, long\_hair, blue\_eyes, breasts, large\_breasts, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
f3b23a5f2390dc395a67bdb9734f6a798f3e23d6 |
# Dataset of sairi (Fire Emblem)
This is the dataset of sairi (Fire Emblem), containing 26 images and their tags.
The core tags of this character are `long_hair, black_hair, headband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 26 | 22.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sairi_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 26 | 16.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sairi_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 45 | 25.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sairi_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 26 | 21.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sairi_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 45 | 31.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sairi_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sairi_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, bangs, bridal_gauntlets, cherry_blossoms, full_body, hair_ornament, solo, circlet, flower, fur_trim, petals, brown_eyes, dress, looking_away, open_mouth, shiny_hair, teeth, branch, cape, detached_sleeves, holding_weapon, kimono, medium_breasts, obi, white_background, white_footwear |
| 1 | 13 |  |  |  |  |  | 1girl, japanese_clothes, solo, katana, black_eyes, fur_trim, japanese_armor, looking_at_viewer, sandals, sheath, simple_background, white_background, full_body, holding_sword, shoulder_armor |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bangs | bridal_gauntlets | cherry_blossoms | full_body | hair_ornament | solo | circlet | flower | fur_trim | petals | brown_eyes | dress | looking_away | open_mouth | shiny_hair | teeth | branch | cape | detached_sleeves | holding_weapon | kimono | medium_breasts | obi | white_background | white_footwear | japanese_clothes | katana | black_eyes | japanese_armor | looking_at_viewer | sandals | sheath | simple_background | holding_sword | shoulder_armor |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------------|:------------------|:------------|:----------------|:-------|:----------|:---------|:-----------|:---------|:-------------|:--------|:---------------|:-------------|:-------------|:--------|:---------|:-------|:-------------------|:-----------------|:---------|:-----------------|:------|:-------------------|:-----------------|:-------------------|:---------|:-------------|:-----------------|:--------------------|:----------|:---------|:--------------------|:----------------|:-----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | | | | X | | X | | | X | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/sairi_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T18:56:25+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:03:48+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of sairi (Fire Emblem)
==============================
This is the dataset of sairi (Fire Emblem), containing 26 images and their tags.
The core tags of this character are 'long\_hair, black\_hair, headband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
b8c46247b3e4069932110ad7420e05ff8d10ec03 |
# MiTTenS: A Dataset for Evaluating Misgendering in Translation
Misgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally underpresented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages.
## HuggingFace dataset
This mirrors the GitHub repository at https://github.com/google-research-datasets/mittens
| google/mittens | [
"task_categories:translation",
"size_categories:1K<n<10K",
"language:ar",
"language:fi",
"language:om",
"language:lg",
"language:as",
"language:tr",
"language:fa",
"language:id",
"language:bn",
"language:de",
"language:hi",
"language:pt",
"language:ru",
"language:zh",
"language:ja",
"language:pl",
"language:te",
"language:th",
"language:cs",
"language:fr",
"language:am",
"language:it",
"language:es",
"license:cc-by-4.0",
"multilingual",
"i18n",
"region:us"
] | 2024-01-17T19:03:53+00:00 | {"language": ["ar", "fi", "om", "lg", "as", "tr", "fa", "id", "bn", "de", "hi", "pt", "ru", "zh", "ja", "pl", "te", "th", "cs", "fr", "am", "it", "es"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "tags": ["multilingual", "i18n"]} | 2024-01-17T19:17:58+00:00 | [] | [
"ar",
"fi",
"om",
"lg",
"as",
"tr",
"fa",
"id",
"bn",
"de",
"hi",
"pt",
"ru",
"zh",
"ja",
"pl",
"te",
"th",
"cs",
"fr",
"am",
"it",
"es"
] | TAGS
#task_categories-translation #size_categories-1K<n<10K #language-Arabic #language-Finnish #language-Oromo #language-Ganda #language-Assamese #language-Turkish #language-Persian #language-Indonesian #language-Bengali #language-German #language-Hindi #language-Portuguese #language-Russian #language-Chinese #language-Japanese #language-Polish #language-Telugu #language-Thai #language-Czech #language-French #language-Amharic #language-Italian #language-Spanish #license-cc-by-4.0 #multilingual #i18n #region-us
|
# MiTTenS: A Dataset for Evaluating Misgendering in Translation
Misgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally underpresented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages.
## HuggingFace dataset
This mirrors the GitHub repository at URL
| [
"# MiTTenS: A Dataset for Evaluating Misgendering in Translation\nMisgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally underpresented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages.",
"## HuggingFace dataset\nThis mirrors the GitHub repository at URL"
] | [
"TAGS\n#task_categories-translation #size_categories-1K<n<10K #language-Arabic #language-Finnish #language-Oromo #language-Ganda #language-Assamese #language-Turkish #language-Persian #language-Indonesian #language-Bengali #language-German #language-Hindi #language-Portuguese #language-Russian #language-Chinese #language-Japanese #language-Polish #language-Telugu #language-Thai #language-Czech #language-French #language-Amharic #language-Italian #language-Spanish #license-cc-by-4.0 #multilingual #i18n #region-us \n",
"# MiTTenS: A Dataset for Evaluating Misgendering in Translation\nMisgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally underpresented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages.",
"## HuggingFace dataset\nThis mirrors the GitHub repository at URL"
] |
f7682a410a86f512afb2bd4a4a90a9bf51291a71 | # Dataset Card for "50000-60900-ultrafeedback-binarized-preferences-cleaned-ita"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | giux78/50000-60900-ultrafeedback-binarized-preferences-cleaned-ita | [
"region:us"
] | 2024-01-17T19:05:50+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "chosen-rating", "dtype": "float64"}, {"name": "chosen-model", "dtype": "string"}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected-rating", "dtype": "float64"}, {"name": "rejected-model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 97100085, "num_examples": 10900}], "download_size": 48433446, "dataset_size": 97100085}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-17T19:05:58+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "50000-60900-ultrafeedback-binarized-preferences-cleaned-ita"
More Information needed | [
"# Dataset Card for \"50000-60900-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"50000-60900-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed"
] |
4bfff42eb71b568694df992696a85fe820e1dab4 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | adonaivera/image-classification-mistakes | [
"region:us"
] | 2024-01-17T19:11:21+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2024-01-17T19:11:37+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Dataset Name",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
6d641aae71d190581909184c552b4e17ac72c79d |
# Dataset of dyute (Fire Emblem)
This is the dataset of dyute (Fire Emblem), containing 160 images and their tags.
The core tags of this character are `brown_hair, ponytail, bow, brown_eyes, long_hair, fang, hair_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 160 | 163.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 160 | 101.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 353 | 210.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 160 | 149.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 353 | 276.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dyute_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/dyute_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, bare_shoulders, bracelet, breastplate, open_mouth, simple_background, solo, cape, smile, boots, white_background, blush, dress, full_body |
| 1 | 15 |  |  |  |  |  | nipples, 1girl, nude, blush, navel, pussy, open_mouth, small_breasts, censored, solo_focus, spread_legs, 1boy, hetero, looking_at_viewer, sex, simple_background, smile, vaginal, penis |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | bracelet | breastplate | open_mouth | simple_background | solo | cape | smile | boots | white_background | blush | dress | full_body | nipples | nude | navel | pussy | small_breasts | censored | solo_focus | spread_legs | 1boy | hetero | looking_at_viewer | sex | vaginal | penis |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------|:--------------|:-------------|:--------------------|:-------|:-------|:--------|:--------|:-------------------|:--------|:--------|:------------|:----------|:-------|:--------|:--------|:----------------|:-----------|:-------------|:--------------|:-------|:---------|:--------------------|:------|:----------|:--------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | | | | X | X | | | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/dyute_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:11:31+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:43:59+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of dyute (Fire Emblem)
==============================
This is the dataset of dyute (Fire Emblem), containing 160 images and their tags.
The core tags of this character are 'brown\_hair, ponytail, bow, brown\_eyes, long\_hair, fang, hair\_bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
64fa57c01f36fa73a443c107f380f4d8350e272c |
# Dataset of dagr (Fire Emblem)
This is the dataset of dagr (Fire Emblem), containing 16 images and their tags.
The core tags of this character are `breasts, short_hair, blue_hair, grey_eyes, muscular_female, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 16 | 27.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dagr_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 14.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dagr_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 36 | 28.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dagr_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 23.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dagr_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 36 | 41.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dagr_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/dagr_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, solo, navel, abs, holding, smile, midriff, muscular, gloves, simple_background, cleavage, full_body, looking_at_viewer, sandals, weapon, white_background, jewelry, bird, teeth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | navel | abs | holding | smile | midriff | muscular | gloves | simple_background | cleavage | full_body | looking_at_viewer | sandals | weapon | white_background | jewelry | bird | teeth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:------|:----------|:--------|:----------|:-----------|:---------|:--------------------|:-----------|:------------|:--------------------|:----------|:---------|:-------------------|:----------|:-------|:--------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/dagr_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:11:37+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:16:09+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of dagr (Fire Emblem)
=============================
This is the dataset of dagr (Fire Emblem), containing 16 images and their tags.
The core tags of this character are 'breasts, short\_hair, blue\_hair, grey\_eyes, muscular\_female, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
2346c2eadfb024d31a2dc4b80abf72b7f4bb5431 |
# Dataset of nyx (Fire Emblem)
This is the dataset of nyx (Fire Emblem), containing 58 images and their tags.
The core tags of this character are `black_hair, long_hair, facial_mark, breasts, red_eyes, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 58 | 61.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyx_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 58 | 37.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyx_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 122 | 71.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyx_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 58 | 54.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyx_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 122 | 97.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyx_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nyx_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 58 |  |  |  |  |  | 1girl, solo, forehead_mark, looking_at_viewer, cape, bodystocking, simple_background, tiara, mouth_veil, covered_navel, book, cleavage, thighhighs, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | forehead_mark | looking_at_viewer | cape | bodystocking | simple_background | tiara | mouth_veil | covered_navel | book | cleavage | thighhighs | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------------|:--------------------|:-------|:---------------|:--------------------|:--------|:-------------|:----------------|:-------|:-----------|:-------------|:-------------------|
| 0 | 58 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/nyx_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:11:43+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:23:35+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of nyx (Fire Emblem)
============================
This is the dataset of nyx (Fire Emblem), containing 58 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, facial\_mark, breasts, red\_eyes, small\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
134802102af3f935d54707452c0919aff3a7399e |
# Dataset of kleine (Fire Emblem)
This is the dataset of kleine (Fire Emblem), containing 29 images and their tags.
The core tags of this character are `blonde_hair, long_hair, blue_eyes, breasts, bangs, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 29 | 36.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kleine_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 29 | 23.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kleine_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 71 | 47.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kleine_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 29 | 34.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kleine_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 71 | 63.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kleine_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kleine_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 29 |  |  |  |  |  | 1girl, solo, armor, looking_at_viewer, skirt, thighhighs, fingerless_gloves, simple_background, bow_(weapon), elbow_gloves, holding, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | armor | looking_at_viewer | skirt | thighhighs | fingerless_gloves | simple_background | bow_(weapon) | elbow_gloves | holding | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:--------|:-------------|:--------------------|:--------------------|:---------------|:---------------|:----------|:-------------------|
| 0 | 29 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/kleine_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:13:44+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:19:11+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kleine (Fire Emblem)
===============================
This is the dataset of kleine (Fire Emblem), containing 29 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, blue\_eyes, breasts, bangs, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
0a4d23c41ea9e381a3ef99cf4e8c8949b0c57765 | # Responsible Media Content Matrix (RMCM): Overview
The RMCM is a strategic tool developed to address various forms of bias and unethical practices in media reporting. It encompasses several key categories, each focusing on a specific type of bias or ethical concern. The primary objective of the RMCM is to foster responsible journalism and content creation by providing clear guidelines on identifying and rectifying biased or harmful content.
## Key Categories of RMCM:
### Toxicity
This includes content that is aggressive, rude, disrespectful, or contributes to a hostile environment. It particularly focuses on hate speech and other forms of communication that incite hatred or violence based on race, religion, gender, or sexual orientation.
### Stereotyping
This category deals with generalized or oversimplified beliefs about specific groups or communities. It aims to identify and correct content that perpetuates stereotypes, particularly those related to sexual themes, age, gender biases, or cultural misconceptions.
### Bias
This refers to any content showing an unjustifiable preference or prejudice towards certain viewpoints, groups, or individuals. It includes both overt bias and more subtle forms of partiality that can skew the presentation of information.
### Harm
This encompasses content that could cause distress or harm to individuals or society. It includes sensationalized or unethical reporting of criminal behavior, as well as content that inappropriately focuses on or glorifies violence and weaponry.
The RMCM serves as a guide for content creators, editors, and journalists, encouraging them to critically assess their work for potential biases and harmful elements. By adhering to the principles of the RMCM, media professionals can contribute to a more ethical, balanced, and responsible media landscape.
| newsmediabias/instruction-safe-llm | [
"region:us"
] | 2024-01-17T19:21:42+00:00 | {} | 2024-02-04T23:51:16+00:00 | [] | [] | TAGS
#region-us
| # Responsible Media Content Matrix (RMCM): Overview
The RMCM is a strategic tool developed to address various forms of bias and unethical practices in media reporting. It encompasses several key categories, each focusing on a specific type of bias or ethical concern. The primary objective of the RMCM is to foster responsible journalism and content creation by providing clear guidelines on identifying and rectifying biased or harmful content.
## Key Categories of RMCM:
### Toxicity
This includes content that is aggressive, rude, disrespectful, or contributes to a hostile environment. It particularly focuses on hate speech and other forms of communication that incite hatred or violence based on race, religion, gender, or sexual orientation.
### Stereotyping
This category deals with generalized or oversimplified beliefs about specific groups or communities. It aims to identify and correct content that perpetuates stereotypes, particularly those related to sexual themes, age, gender biases, or cultural misconceptions.
### Bias
This refers to any content showing an unjustifiable preference or prejudice towards certain viewpoints, groups, or individuals. It includes both overt bias and more subtle forms of partiality that can skew the presentation of information.
### Harm
This encompasses content that could cause distress or harm to individuals or society. It includes sensationalized or unethical reporting of criminal behavior, as well as content that inappropriately focuses on or glorifies violence and weaponry.
The RMCM serves as a guide for content creators, editors, and journalists, encouraging them to critically assess their work for potential biases and harmful elements. By adhering to the principles of the RMCM, media professionals can contribute to a more ethical, balanced, and responsible media landscape.
| [
"# Responsible Media Content Matrix (RMCM): Overview\n\nThe RMCM is a strategic tool developed to address various forms of bias and unethical practices in media reporting. It encompasses several key categories, each focusing on a specific type of bias or ethical concern. The primary objective of the RMCM is to foster responsible journalism and content creation by providing clear guidelines on identifying and rectifying biased or harmful content.",
"## Key Categories of RMCM:",
"### Toxicity\nThis includes content that is aggressive, rude, disrespectful, or contributes to a hostile environment. It particularly focuses on hate speech and other forms of communication that incite hatred or violence based on race, religion, gender, or sexual orientation.",
"### Stereotyping\nThis category deals with generalized or oversimplified beliefs about specific groups or communities. It aims to identify and correct content that perpetuates stereotypes, particularly those related to sexual themes, age, gender biases, or cultural misconceptions.",
"### Bias\nThis refers to any content showing an unjustifiable preference or prejudice towards certain viewpoints, groups, or individuals. It includes both overt bias and more subtle forms of partiality that can skew the presentation of information.",
"### Harm\nThis encompasses content that could cause distress or harm to individuals or society. It includes sensationalized or unethical reporting of criminal behavior, as well as content that inappropriately focuses on or glorifies violence and weaponry.\n\nThe RMCM serves as a guide for content creators, editors, and journalists, encouraging them to critically assess their work for potential biases and harmful elements. By adhering to the principles of the RMCM, media professionals can contribute to a more ethical, balanced, and responsible media landscape."
] | [
"TAGS\n#region-us \n",
"# Responsible Media Content Matrix (RMCM): Overview\n\nThe RMCM is a strategic tool developed to address various forms of bias and unethical practices in media reporting. It encompasses several key categories, each focusing on a specific type of bias or ethical concern. The primary objective of the RMCM is to foster responsible journalism and content creation by providing clear guidelines on identifying and rectifying biased or harmful content.",
"## Key Categories of RMCM:",
"### Toxicity\nThis includes content that is aggressive, rude, disrespectful, or contributes to a hostile environment. It particularly focuses on hate speech and other forms of communication that incite hatred or violence based on race, religion, gender, or sexual orientation.",
"### Stereotyping\nThis category deals with generalized or oversimplified beliefs about specific groups or communities. It aims to identify and correct content that perpetuates stereotypes, particularly those related to sexual themes, age, gender biases, or cultural misconceptions.",
"### Bias\nThis refers to any content showing an unjustifiable preference or prejudice towards certain viewpoints, groups, or individuals. It includes both overt bias and more subtle forms of partiality that can skew the presentation of information.",
"### Harm\nThis encompasses content that could cause distress or harm to individuals or society. It includes sensationalized or unethical reporting of criminal behavior, as well as content that inappropriately focuses on or glorifies violence and weaponry.\n\nThe RMCM serves as a guide for content creators, editors, and journalists, encouraging them to critically assess their work for potential biases and harmful elements. By adhering to the principles of the RMCM, media professionals can contribute to a more ethical, balanced, and responsible media landscape."
] |
2fffe9a720309c9ff4503feff906f963d661203e |
# Dataset of leonie_pinelli (Fire Emblem)
This is the dataset of leonie_pinelli (Fire Emblem), containing 313 images and their tags.
The core tags of this character are `orange_hair, orange_eyes, short_hair, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 313 | 312.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leonie_pinelli_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 313 | 187.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leonie_pinelli_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 647 | 360.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leonie_pinelli_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 313 | 281.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leonie_pinelli_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 647 | 486.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leonie_pinelli_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/leonie_pinelli_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | garreg_mach_monastery_uniform, 2girls, simple_background, black_gloves, open_mouth, fingerless_gloves, long_sleeves, boots, smile, white_hair |
| 1 | 7 |  |  |  |  |  | 1girl, simple_background, solo, garreg_mach_monastery_uniform, fingerless_gloves, holding_weapon, smile, upper_body, white_background, closed_mouth, long_sleeves, open_mouth |
| 2 | 5 |  |  |  |  |  | 1girl, black_gloves, garreg_mach_monastery_uniform, open_mouth, upper_body, simple_background, smile, solo |
| 3 | 11 |  |  |  |  |  | 1girl, solo, smile, armor, looking_at_viewer, cleavage, upper_body, medium_breasts, simple_background, fingerless_gloves, side_ponytail, black_gloves, holding_weapon, open_mouth, polearm, white_background |
| 4 | 10 |  |  |  |  |  | blue_sky, orange_bikini, 1girl, black_gloves, cleavage, day, ocean, outdoors, solo, bikini_top_only, cloud, fingerless_gloves, looking_at_viewer, medium_breasts, sunflower, clothes_around_waist, navel, official_alternate_costume, short_shorts, bangs, beach, bike_shorts, midriff, black_shorts, holding_weapon, armpits, bare_shoulders, collarbone, grin, jewelry, open_mouth, standing |
| 5 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, nipples, solo, completely_nude, navel, smile, closed_mouth, simple_background, blush, female_pubic_hair, medium_breasts, collarbone, pussy, sitting |
| 6 | 20 |  |  |  |  |  | 1girl, hetero, nipples, 1boy, penis, solo_focus, blush, open_mouth, medium_breasts, vaginal, breasts_out, cum_in_pussy, large_breasts, uncensored, clothed_sex, heart, black_gloves, garreg_mach_monastery_uniform, lying, spread_legs, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | garreg_mach_monastery_uniform | 2girls | simple_background | black_gloves | open_mouth | fingerless_gloves | long_sleeves | boots | smile | white_hair | 1girl | solo | holding_weapon | upper_body | white_background | closed_mouth | armor | looking_at_viewer | cleavage | medium_breasts | side_ponytail | polearm | blue_sky | orange_bikini | day | ocean | outdoors | bikini_top_only | cloud | sunflower | clothes_around_waist | navel | official_alternate_costume | short_shorts | bangs | beach | bike_shorts | midriff | black_shorts | armpits | bare_shoulders | collarbone | grin | jewelry | standing | nipples | completely_nude | blush | female_pubic_hair | pussy | sitting | hetero | 1boy | penis | solo_focus | vaginal | breasts_out | cum_in_pussy | large_breasts | uncensored | clothed_sex | heart | lying | spread_legs | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------|:--------------------|:---------------|:-------------|:--------------------|:---------------|:--------|:--------|:-------------|:--------|:-------|:-----------------|:-------------|:-------------------|:---------------|:--------|:--------------------|:-----------|:-----------------|:----------------|:----------|:-----------|:----------------|:------|:--------|:-----------|:------------------|:--------|:------------|:-----------------------|:--------|:-----------------------------|:---------------|:--------|:--------|:--------------|:----------|:---------------|:----------|:-----------------|:-------------|:-------|:----------|:-----------|:----------|:------------------|:--------|:--------------------|:--------|:----------|:---------|:-------|:--------|:-------------|:----------|:--------------|:---------------|:----------------|:-------------|:--------------|:--------|:--------|:--------------|:--------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | X | | X | X | X | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | | X | X | X | | | | X | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | | | X | X | X | X | | | X | | X | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | | | | X | X | X | | | | | X | X | X | | | | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | | | X | | | | | | X | | X | X | | | | X | | X | | X | | | | | | | | | | | | X | | | | | | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 6 | 20 |  |  |  |  |  | X | | | X | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/leonie_pinelli_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:24:18+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:42:38+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of leonie\_pinelli (Fire Emblem)
========================================
This is the dataset of leonie\_pinelli (Fire Emblem), containing 313 images and their tags.
The core tags of this character are 'orange\_hair, orange\_eyes, short\_hair, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
96faf253021214500cdaf7059bbce938c53d0a8e |
# Dataset of ursula (Fire Emblem)
This is the dataset of ursula (Fire Emblem), containing 124 images and their tags.
The core tags of this character are `breasts, short_hair, purple_hair, earrings, large_breasts, purple_eyes, blue_hair, blue_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 124 | 122.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ursula_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 124 | 79.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ursula_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 243 | 142.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ursula_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 124 | 112.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ursula_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 243 | 183.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ursula_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ursula_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, female_pubic_hair, medium_breasts, nipples, nude, solo, boots, elbow_gloves, pussy, uncensored |
| 1 | 7 |  |  |  |  |  | 1girl, blush, hetero, jewelry, nipples, solo_focus, vaginal, 3boys, gangbang, multiple_penises, cum_in_pussy, elbow_gloves, spread_legs, facial, mosaic_censoring, open_mouth, pubic_hair |
| 2 | 34 |  |  |  |  |  | 1girl, jewelry, dress, solo, elbow_gloves, bare_shoulders, cleavage, smile, looking_at_viewer, side_slit, simple_background, book, lipstick, white_background |
| 3 | 12 |  |  |  |  |  | 1girl, bikini, cleavage, solo, jewelry, hair_flower, navel, looking_at_viewer, smile, thigh_strap, bare_shoulders, simple_background, bangs, holding_weapon, knife |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | female_pubic_hair | medium_breasts | nipples | nude | solo | boots | elbow_gloves | pussy | uncensored | blush | hetero | jewelry | solo_focus | vaginal | 3boys | gangbang | multiple_penises | cum_in_pussy | spread_legs | facial | mosaic_censoring | open_mouth | pubic_hair | dress | bare_shoulders | cleavage | smile | looking_at_viewer | side_slit | simple_background | book | lipstick | white_background | bikini | hair_flower | navel | thigh_strap | bangs | holding_weapon | knife |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-----------------|:----------|:-------|:-------|:--------|:---------------|:--------|:-------------|:--------|:---------|:----------|:-------------|:----------|:--------|:-----------|:-------------------|:---------------|:--------------|:---------|:-------------------|:-------------|:-------------|:--------|:-----------------|:-----------|:--------|:--------------------|:------------|:--------------------|:-------|:-----------|:-------------------|:---------|:--------------|:--------|:--------------|:--------|:-----------------|:--------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 2 | 34 |  |  |  |  |  | X | | | | | X | | X | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | | | | | X | | | | | | | X | | | | | | | | | | | | | X | X | X | X | | X | | | | X | X | X | X | X | X | X |
| CyberHarem/ursula_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:24:21+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:47:54+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of ursula (Fire Emblem)
===============================
This is the dataset of ursula (Fire Emblem), containing 124 images and their tags.
The core tags of this character are 'breasts, short\_hair, purple\_hair, earrings, large\_breasts, purple\_eyes, blue\_hair, blue\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
8a4d42e5e7b82dd773d4e5f48d0462f52e618b4c |
# Dataset of jenny (Fire Emblem)
This is the dataset of jenny (Fire Emblem), containing 110 images and their tags.
The core tags of this character are `hairband, curly_hair, pink_hair, brown_eyes, short_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 110 | 116.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jenny_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 110 | 70.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jenny_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 248 | 142.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jenny_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 110 | 103.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jenny_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 248 | 195.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jenny_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/jenny_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, necklace, simple_background, solo, upper_body, white_background, open_mouth, smile |
| 1 | 7 |  |  |  |  |  | 1girl, dress, full_body, necklace, simple_background, solo, white_background, long_sleeves, staff |
| 2 | 9 |  |  |  |  |  | 1girl, hetero, nipples, open_mouth, sex, small_breasts, 1boy, penis, solo_focus, blush, censored, nude, pussy, vaginal, cum, necklace, orange_eyes, orange_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | necklace | simple_background | solo | upper_body | white_background | open_mouth | smile | dress | full_body | long_sleeves | staff | hetero | nipples | sex | small_breasts | 1boy | penis | solo_focus | blush | censored | nude | pussy | vaginal | cum | orange_eyes | orange_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------------|:-------|:-------------|:-------------------|:-------------|:--------|:--------|:------------|:---------------|:--------|:---------|:----------|:------|:----------------|:-------|:--------|:-------------|:--------|:-----------|:-------|:--------|:----------|:------|:--------------|:--------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | | X | | | X | X | X | X | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/jenny_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:24:26+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:47:04+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of jenny (Fire Emblem)
==============================
This is the dataset of jenny (Fire Emblem), containing 110 images and their tags.
The core tags of this character are 'hairband, curly\_hair, pink\_hair, brown\_eyes, short\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
40016a14ce9fc268ce564380ed2e19ac3bf4aa6c |
# Dataset of yuria (Fire Emblem)
This is the dataset of yuria (Fire Emblem), containing 329 images and their tags.
The core tags of this character are `long_hair, purple_hair, purple_eyes, breasts, light_purple_hair, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 329 | 253.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuria_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 329 | 173.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuria_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 635 | 315.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuria_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 329 | 231.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuria_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 635 | 399.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuria_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/yuria_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, hetero, nipples, 1boy, sex, blush, penis, solo_focus, open_mouth, censored, pussy, navel, large_breasts, medium_breasts, vaginal, completely_nude, cum, spread_legs |
| 1 | 23 |  |  |  |  |  | 1girl, bare_shoulders, circlet, looking_at_viewer, solo, simple_background, upper_body, white_background, blush, smile, hair_between_eyes, twitter_username, white_dress |
| 2 | 9 |  |  |  |  |  | 1girl, circlet, dress, solo, cape, smile, jewelry, blush |
| 3 | 6 |  |  |  |  |  | 1girl, bangs, circlet, full_body, holding_book, long_sleeves, medium_breasts, sandals, solo, wide_sleeves, looking_away, open_book, shiny_hair, toes, bare_shoulders, long_dress, transparent_background, hair_ornament, jewelry, magic, purple_cape, simple_background, toeless_footwear, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hetero | nipples | 1boy | sex | blush | penis | solo_focus | open_mouth | censored | pussy | navel | large_breasts | medium_breasts | vaginal | completely_nude | cum | spread_legs | bare_shoulders | circlet | looking_at_viewer | solo | simple_background | upper_body | white_background | smile | hair_between_eyes | twitter_username | white_dress | dress | cape | jewelry | bangs | full_body | holding_book | long_sleeves | sandals | wide_sleeves | looking_away | open_book | shiny_hair | toes | long_dress | transparent_background | hair_ornament | magic | purple_cape | toeless_footwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:----------|:-------|:------|:--------|:--------|:-------------|:-------------|:-----------|:--------|:--------|:----------------|:-----------------|:----------|:------------------|:------|:--------------|:-----------------|:----------|:--------------------|:-------|:--------------------|:-------------|:-------------------|:--------|:--------------------|:-------------------|:--------------|:--------|:-------|:----------|:--------|:------------|:---------------|:---------------|:----------|:---------------|:---------------|:------------|:-------------|:-------|:-------------|:-------------------------|:----------------|:--------|:--------------|:-------------------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 23 |  |  |  |  |  | X | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | | | | X | | | | | | | | | | | | | | X | | X | | | | X | | | | X | X | X | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | X | | | | | X | X | | X | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/yuria_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:24:31+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:25:02+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of yuria (Fire Emblem)
==============================
This is the dataset of yuria (Fire Emblem), containing 329 images and their tags.
The core tags of this character are 'long\_hair, purple\_hair, purple\_eyes, breasts, light\_purple\_hair, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
004d2e25afe537be6aae71b9db4a42d91e322254 |
# Dataset of maria (Fire Emblem)
This is the dataset of maria (Fire Emblem), containing 60 images and their tags.
The core tags of this character are `red_hair, short_hair, red_eyes, hairband, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 60 | 61.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maria_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 60 | 40.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maria_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 111 | 70.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maria_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 60 | 55.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maria_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 111 | 92.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maria_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/maria_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------|
| 0 | 45 |  |  |  |  |  | 1girl, solo, smile, blush, long_sleeves, looking_at_viewer, necklace, white_dress, open_mouth, simple_background, staff |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | blush | long_sleeves | looking_at_viewer | necklace | white_dress | open_mouth | simple_background | staff |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------|:---------------|:--------------------|:-----------|:--------------|:-------------|:--------------------|:--------|
| 0 | 45 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/maria_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:33:35+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:45:37+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of maria (Fire Emblem)
==============================
This is the dataset of maria (Fire Emblem), containing 60 images and their tags.
The core tags of this character are 'red\_hair, short\_hair, red\_eyes, hairband, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
faaf73c64fa8cc4394049398ca91eacd8fe5ddbb |
# Dataset of lapis (Fire Emblem)
This is the dataset of lapis (Fire Emblem), containing 130 images and their tags.
The core tags of this character are `pink_hair, hairband, short_hair, bangs, pink_eyes, braid, ribbon, red_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 130 | 176.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lapis_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 130 | 105.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lapis_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 320 | 229.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lapis_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 130 | 161.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lapis_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 320 | 322.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lapis_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/lapis_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | long_sleeves, looking_at_viewer, 1girl, solo, red_dress, white_shirt, blush, red_hairband, bow, shoulder_bag, hair_ribbon, simple_background, smile, white_ribbon |
| 1 | 10 |  |  |  |  |  | 1girl, breastplate, red_armor, gloves, looking_at_viewer, simple_background, upper_body, closed_mouth, solo, covered_navel, hair_ribbon, white_background, white_ribbon, blush, shoulder_armor, holding, red_hairband |
| 2 | 5 |  |  |  |  |  | 1girl, gloves, skirt, solo, boots, breastplate, holding_sword, cape, covered_navel, full_body, red_hairband, simple_background, thighhighs, white_background, bodystocking, boobplate, red_footwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | long_sleeves | looking_at_viewer | 1girl | solo | red_dress | white_shirt | blush | red_hairband | bow | shoulder_bag | hair_ribbon | simple_background | smile | white_ribbon | breastplate | red_armor | gloves | upper_body | closed_mouth | covered_navel | white_background | shoulder_armor | holding | skirt | boots | holding_sword | cape | full_body | thighhighs | bodystocking | boobplate | red_footwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------------------|:--------|:-------|:------------|:--------------|:--------|:---------------|:------|:---------------|:--------------|:--------------------|:--------|:---------------|:--------------|:------------|:---------|:-------------|:---------------|:----------------|:-------------------|:-----------------|:----------|:--------|:--------|:----------------|:-------|:------------|:-------------|:---------------|:------------|:---------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | | X | X | X | | | X | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | | | X | X | | | | X | | | | X | | | X | | X | | | X | X | | | X | X | X | X | X | X | X | X | X |
| CyberHarem/lapis_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:33:38+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:58:23+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of lapis (Fire Emblem)
==============================
This is the dataset of lapis (Fire Emblem), containing 130 images and their tags.
The core tags of this character are 'pink\_hair, hairband, short\_hair, bangs, pink\_eyes, braid, ribbon, red\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
c2e888ce9f904b71eacc73383e91e6eb99aa8da3 |
# Dataset of thrasir (Fire Emblem)
This is the dataset of thrasir (Fire Emblem), containing 40 images and their tags.
The core tags of this character are `long_hair, red_eyes, horns, white_hair, breasts, grey_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 40 | 48.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thrasir_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 40 | 29.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thrasir_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 89 | 57.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thrasir_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 40 | 42.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thrasir_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 89 | 76.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thrasir_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/thrasir_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 40 |  |  |  |  |  | 1girl, solo, simple_background, skeleton, bone, breastplate, cape, domino_mask, see-through, looking_at_viewer, smile, white_background, shoulder_armor |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | simple_background | skeleton | bone | breastplate | cape | domino_mask | see-through | looking_at_viewer | smile | white_background | shoulder_armor |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-----------|:-------|:--------------|:-------|:--------------|:--------------|:--------------------|:--------|:-------------------|:-----------------|
| 0 | 40 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/thrasir_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:33:39+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T19:41:03+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of thrasir (Fire Emblem)
================================
This is the dataset of thrasir (Fire Emblem), containing 40 images and their tags.
The core tags of this character are 'long\_hair, red\_eyes, horns, white\_hair, breasts, grey\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
197d19c561e652c9dbdaeb8e5528b96712120a3e | # NUFORC
147,890 UFO sightings from [NUFORC](https://nuforc.org/), scraped on January 16, 2024.
The best representation of the data is `nuforc.json`, but I also converted this to .csv files.
Each .csv has a different approach to the "Characteristics" column:
* `nuforc_str.csv` has a "Characteristics" column that looks like this: `"Aircraft nearby, Animals reacted"`
* `nuforc_list.csv` has a "Characteristics" column that look like: `"['Aircraft nearby', 'Animals reacted']"`
* `nuforc_bool.csv` has one column for each characteristic, with values as `True` or null (empty).
Note that while this is a crowdsourced database compiled from the reports of hundreds of thousands of individuals, NUFORC also claims copyright over the reports with invisible text at the bottom of every page reading "© 2023 National UFO Reporting Center. All rights reserved. Use or reproduction within any application without written consent is prohibited." So while I have licensed this repository as MIT, the true license may actually be significantly more restrictive. For more information you might try contacting NUFORC at [email protected]
---
license: mit
---
| kcimc/NUFORC | [
"region:us"
] | 2024-01-17T19:34:56+00:00 | {} | 2024-01-17T20:29:23+00:00 | [] | [] | TAGS
#region-us
| # NUFORC
147,890 UFO sightings from NUFORC, scraped on January 16, 2024.
The best representation of the data is 'URL', but I also converted this to .csv files.
Each .csv has a different approach to the "Characteristics" column:
* 'nuforc_str.csv' has a "Characteristics" column that looks like this: '"Aircraft nearby, Animals reacted"'
* 'nuforc_list.csv' has a "Characteristics" column that look like: '"['Aircraft nearby', 'Animals reacted']"'
* 'nuforc_bool.csv' has one column for each characteristic, with values as 'True' or null (empty).
Note that while this is a crowdsourced database compiled from the reports of hundreds of thousands of individuals, NUFORC also claims copyright over the reports with invisible text at the bottom of every page reading "© 2023 National UFO Reporting Center. All rights reserved. Use or reproduction within any application without written consent is prohibited." So while I have licensed this repository as MIT, the true license may actually be significantly more restrictive. For more information you might try contacting NUFORC at webmaster@URL
---
license: mit
---
| [
"# NUFORC\n\n147,890 UFO sightings from NUFORC, scraped on January 16, 2024.\n\nThe best representation of the data is 'URL', but I also converted this to .csv files.\n\nEach .csv has a different approach to the \"Characteristics\" column:\n\n* 'nuforc_str.csv' has a \"Characteristics\" column that looks like this: '\"Aircraft nearby, Animals reacted\"'\n* 'nuforc_list.csv' has a \"Characteristics\" column that look like: '\"['Aircraft nearby', 'Animals reacted']\"'\n* 'nuforc_bool.csv' has one column for each characteristic, with values as 'True' or null (empty).\n\nNote that while this is a crowdsourced database compiled from the reports of hundreds of thousands of individuals, NUFORC also claims copyright over the reports with invisible text at the bottom of every page reading \"© 2023 National UFO Reporting Center. All rights reserved. Use or reproduction within any application without written consent is prohibited.\" So while I have licensed this repository as MIT, the true license may actually be significantly more restrictive. For more information you might try contacting NUFORC at webmaster@URL\n\n---\nlicense: mit\n---"
] | [
"TAGS\n#region-us \n",
"# NUFORC\n\n147,890 UFO sightings from NUFORC, scraped on January 16, 2024.\n\nThe best representation of the data is 'URL', but I also converted this to .csv files.\n\nEach .csv has a different approach to the \"Characteristics\" column:\n\n* 'nuforc_str.csv' has a \"Characteristics\" column that looks like this: '\"Aircraft nearby, Animals reacted\"'\n* 'nuforc_list.csv' has a \"Characteristics\" column that look like: '\"['Aircraft nearby', 'Animals reacted']\"'\n* 'nuforc_bool.csv' has one column for each characteristic, with values as 'True' or null (empty).\n\nNote that while this is a crowdsourced database compiled from the reports of hundreds of thousands of individuals, NUFORC also claims copyright over the reports with invisible text at the bottom of every page reading \"© 2023 National UFO Reporting Center. All rights reserved. Use or reproduction within any application without written consent is prohibited.\" So while I have licensed this repository as MIT, the true license may actually be significantly more restrictive. For more information you might try contacting NUFORC at webmaster@URL\n\n---\nlicense: mit\n---"
] |
84c18e63d76c17c15846d254f4fc8377dac438d9 |
# Dataset of laevatein (Fire Emblem)
This is the dataset of laevatein (Fire Emblem), containing 98 images and their tags.
The core tags of this character are `long_hair, twintails, dark-skinned_female, dark_skin, pink_hair, red_eyes, hair_ornament, breasts, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 98 | 109.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laevatein_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 98 | 62.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laevatein_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 218 | 126.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laevatein_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 98 | 95.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laevatein_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 218 | 177.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/laevatein_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/laevatein_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, simple_background, solo, closed_mouth, gradient_hair, bare_shoulders, armor, feather_trim, white_background, cleavage, looking_at_viewer, weapon |
| 1 | 5 |  |  |  |  |  | 1girl, closed_mouth, solo, upper_body, smile, looking_at_viewer, simple_background, flower, red_kimono, white_background |
| 2 | 5 |  |  |  |  |  | 1girl, black_bikini, gradient_hair, hair_flower, solo, vines, barefoot, cleavage, holding, kickboard, navel, orange_hair, simple_background, bangs, black_jacket, cropped_jacket, looking_at_viewer, short_sleeves, sidelocks, stomach, toes, bare_legs, closed_mouth, criss-cross_halter, feet, full_body, fur-trimmed_jacket, grey_background, hibiscus, open_jacket, red_flower, sitting, white_background |
| 3 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, penis, open_mouth, sex, solo_focus, blush, spread_legs, vaginal, cum_in_pussy, interracial, bar_censor, breasts_out, nude, thighhighs |
| 4 | 9 |  |  |  |  |  | 1girl, nipples, pussy, solo, nude, large_breasts, navel, blush, looking_at_viewer, very_long_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | simple_background | solo | closed_mouth | gradient_hair | bare_shoulders | armor | feather_trim | white_background | cleavage | looking_at_viewer | weapon | upper_body | smile | flower | red_kimono | black_bikini | hair_flower | vines | barefoot | holding | kickboard | navel | orange_hair | bangs | black_jacket | cropped_jacket | short_sleeves | sidelocks | stomach | toes | bare_legs | criss-cross_halter | feet | full_body | fur-trimmed_jacket | grey_background | hibiscus | open_jacket | red_flower | sitting | 1boy | hetero | nipples | penis | open_mouth | sex | solo_focus | blush | spread_legs | vaginal | cum_in_pussy | interracial | bar_censor | breasts_out | nude | thighhighs | pussy | large_breasts | very_long_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:---------------|:----------------|:-----------------|:--------|:---------------|:-------------------|:-----------|:--------------------|:---------|:-------------|:--------|:---------|:-------------|:---------------|:--------------|:--------|:-----------|:----------|:------------|:--------|:--------------|:--------|:---------------|:-----------------|:----------------|:------------|:----------|:-------|:------------|:---------------------|:-------|:------------|:---------------------|:------------------|:-----------|:--------------|:-------------|:----------|:-------|:---------|:----------|:--------|:-------------|:------|:-------------|:--------|:--------------|:----------|:---------------|:--------------|:-------------|:--------------|:-------|:-------------|:--------|:----------------|:-----------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | | | | | X | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | | | | X | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | |
| 4 | 9 |  |  |  |  |  | X | | X | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | X | | X | X | X |
| CyberHarem/laevatein_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:37:37+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:06:13+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of laevatein (Fire Emblem)
==================================
This is the dataset of laevatein (Fire Emblem), containing 98 images and their tags.
The core tags of this character are 'long\_hair, twintails, dark-skinned\_female, dark\_skin, pink\_hair, red\_eyes, hair\_ornament, breasts, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1d3e402060e7eae9fd13371daa03b73864451b3d |
## OpenBohm
This dataset is an experimental conjugation of philosophical multi-turn long-form conversations from J. Krishnamurti, and D. Bohm, added to long-conversation filtered (count > 6) Capybara data, edited to be slightly less apologetic.
Removed references to names and locations where possible. Some conversations have been paraphrased somewhat to follow QA format better, however they keep the key content of the original.
 | distantquant/openbohm | [
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"multi-turn",
"philosophy",
"long-form",
"region:us"
] | 2024-01-17T19:43:54+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "pretty_name": "openBohm", "tags": ["multi-turn", "philosophy", "long-form"]} | 2024-01-17T20:32:26+00:00 | [] | [
"en"
] | TAGS
#size_categories-n<1K #language-English #license-cc-by-4.0 #multi-turn #philosophy #long-form #region-us
|
## OpenBohm
This dataset is an experimental conjugation of philosophical multi-turn long-form conversations from J. Krishnamurti, and D. Bohm, added to long-conversation filtered (count > 6) Capybara data, edited to be slightly less apologetic.
Removed references to names and locations where possible. Some conversations have been paraphrased somewhat to follow QA format better, however they keep the key content of the original.
 Capybara data, edited to be slightly less apologetic.\n\nRemoved references to names and locations where possible. Some conversations have been paraphrased somewhat to follow QA format better, however they keep the key content of the original.\n\n Capybara data, edited to be slightly less apologetic.\n\nRemoved references to names and locations where possible. Some conversations have been paraphrased somewhat to follow QA format better, however they keep the key content of the original.\n\n
This is the dataset of noire (Fire Emblem), containing 123 images and their tags.
The core tags of this character are `breasts, short_hair, black_hair, large_breasts, hair_ornament, feather_hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 123 | 129.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/noire_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 123 | 79.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/noire_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 259 | 150.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/noire_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 123 | 116.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/noire_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 259 | 207.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/noire_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/noire_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, cleavage, feathers, navel, solo, green_bikini, circlet, looking_at_viewer, blush, outdoors, day, blue_eyes, cloud, cowboy_shot, huge_breasts, sky |
| 1 | 13 |  |  |  |  |  | 1girl, solo, circlet, feathers, gloves, cleavage_cutout, arrow_(projectile), open_mouth, quiver, holding_bow_(weapon), medium_breasts, upper_body |
| 2 | 6 |  |  |  |  |  | 1boy, 1girl, hetero, blush, feathers, green_bikini, penis, spread_legs, uncensored, vaginal, bikini_bottom_aside, nipples, solo_focus, blue_eyes, cum_in_pussy, navel, one_eye_closed, open_mouth, sex_from_behind, sweat |
| 3 | 7 |  |  |  |  |  | 1girl, hetero, penis, 1boy, feathers, solo_focus, uncensored, fellatio, blue_eyes, cum, nipples, brown_hair, circlet, nude, open_mouth, paizuri, testicles |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | feathers | navel | solo | green_bikini | circlet | looking_at_viewer | blush | outdoors | day | blue_eyes | cloud | cowboy_shot | huge_breasts | sky | gloves | cleavage_cutout | arrow_(projectile) | open_mouth | quiver | holding_bow_(weapon) | medium_breasts | upper_body | 1boy | hetero | penis | spread_legs | uncensored | vaginal | bikini_bottom_aside | nipples | solo_focus | cum_in_pussy | one_eye_closed | sex_from_behind | sweat | fellatio | cum | brown_hair | nude | paizuri | testicles |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-----------|:--------|:-------|:---------------|:----------|:--------------------|:--------|:-----------|:------|:------------|:--------|:--------------|:---------------|:------|:---------|:------------------|:---------------------|:-------------|:---------|:-----------------------|:-----------------|:-------------|:-------|:---------|:--------|:--------------|:-------------|:----------|:----------------------|:----------|:-------------|:---------------|:-----------------|:------------------|:--------|:-----------|:------|:-------------|:-------|:----------|:------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | | X | | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | X | X | | X | | | X | | | X | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | X | | | | X | | | | | X | | | | | | | | X | | | | | X | X | X | | X | | | X | X | | | | | X | X | X | X | X | X |
| CyberHarem/noire_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:49:30+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:18:50+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of noire (Fire Emblem)
==============================
This is the dataset of noire (Fire Emblem), containing 123 images and their tags.
The core tags of this character are 'breasts, short\_hair, black\_hair, large\_breasts, hair\_ornament, feather\_hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
24fab9dee2071071bab50f2b169540cb0ab15819 |
# Dataset of plumeria (Fire Emblem)
This is the dataset of plumeria (Fire Emblem), containing 182 images and their tags.
The core tags of this character are `breasts, long_hair, red_eyes, grey_hair, large_breasts, pointy_ears, wings, ponytail, facial_mark, fairy_wings, butterfly_wings`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 182 | 284.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeria_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 182 | 160.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeria_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 435 | 334.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeria_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 182 | 249.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeria_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 435 | 468.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeria_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/plumeria_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, solo, looking_at_viewer, simple_background, forehead_mark, smile, white_background, upper_body, dress, bare_shoulders, sideboob, hair_ornament, open_mouth, thorns, twitter_username, vines |
| 1 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, sideboob, solo, vines, blush, thorns, bare_shoulders, open_mouth, dress |
| 2 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, solo, thorns, vines, smile, bangs, bare_shoulders, forehead_mark, blue_rose, cleavage, simple_background, dress, thighhighs, white_background, covered_navel, full_body, hair_flower, leotard |
| 3 | 5 |  |  |  |  |  | 1girl, bangs, bare_shoulders, dress, full_body, gradient_hair, hair_ornament, pelvic_curtain, shiny_hair, shiny_skin, sideboob, simple_background, solo, thigh_boots, thighhighs, thorns, detached_sleeves, floating_object, parted_lips, sleeveless, armpits, looking_at_viewer, shiny_clothes, black_footwear, forehead_mark, grey_background, leg_up, thighs, white_background |
| 4 | 15 |  |  |  |  |  | 1girl, cleavage, forehead_mark, solo, navel, looking_at_viewer, smile, black_one-piece_swimsuit, blush, very_long_hair, vines, fairy, water, bangs, hair_flower, open_mouth, thorns, alternate_costume, bikini, sitting |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, penis, sex, solo_focus, vaginal, forehead_mark, mosaic_censoring, navel, spread_legs, vines, nude, open_mouth, cum_in_pussy, female_pubic_hair, lying, thorns |
| 6 | 6 |  |  |  |  |  | 1girl, hetero, multiple_penises, 3boys, cum_in_pussy, handjob, nipples, vaginal, blush, solo_focus, gangbang, mosaic_censoring, spread_legs, thighhighs, vines |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | simple_background | forehead_mark | smile | white_background | upper_body | dress | bare_shoulders | sideboob | hair_ornament | open_mouth | thorns | twitter_username | vines | blush | bangs | blue_rose | cleavage | thighhighs | covered_navel | full_body | hair_flower | leotard | gradient_hair | pelvic_curtain | shiny_hair | shiny_skin | thigh_boots | detached_sleeves | floating_object | parted_lips | sleeveless | armpits | shiny_clothes | black_footwear | grey_background | leg_up | thighs | navel | black_one-piece_swimsuit | very_long_hair | fairy | water | alternate_costume | bikini | sitting | 1boy | hetero | nipples | penis | sex | solo_focus | vaginal | mosaic_censoring | spread_legs | nude | cum_in_pussy | female_pubic_hair | lying | multiple_penises | 3boys | handjob | gangbang |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------------|:----------------|:--------|:-------------------|:-------------|:--------|:-----------------|:-----------|:----------------|:-------------|:---------|:-------------------|:--------|:--------|:--------|:------------|:-----------|:-------------|:----------------|:------------|:--------------|:----------|:----------------|:-----------------|:-------------|:-------------|:--------------|:-------------------|:------------------|:--------------|:-------------|:----------|:----------------|:-----------------|:------------------|:---------|:---------|:--------|:---------------------------|:-----------------|:--------|:--------|:--------------------|:---------|:----------|:-------|:---------|:----------|:--------|:------|:-------------|:----------|:-------------------|:--------------|:-------|:---------------|:--------------------|:--------|:-------------------|:--------|:----------|:-----------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | | | | | | X | X | X | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | | | | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | X | X | | X | | | | X | | | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 15 |  |  |  |  |  | X | X | X | | X | X | | | | | | | X | X | | X | X | X | | X | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | | X | | | | | | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | |
| 6 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | X | X | X | X | | X | | | X | X | X | X |
| CyberHarem/plumeria_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:49:34+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:28:43+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of plumeria (Fire Emblem)
=================================
This is the dataset of plumeria (Fire Emblem), containing 182 images and their tags.
The core tags of this character are 'breasts, long\_hair, red\_eyes, grey\_hair, large\_breasts, pointy\_ears, wings, ponytail, facial\_mark, fairy\_wings, butterfly\_wings', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
2d71770827b7e1d0ca0fab4c1233beed6764bf53 |
# Dataset of katarina (Fire Emblem)
This is the dataset of katarina (Fire Emblem), containing 96 images and their tags.
The core tags of this character are `purple_hair, short_hair, breasts, grey_eyes, purple_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 96 | 127.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 96 | 72.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 213 | 143.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 96 | 112.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 213 | 204.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katarina_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/katarina_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 20 |  |  |  |  |  | 1girl, nipples, blush, hetero, open_mouth, penis, 1boy, sex, solo_focus, cum_in_pussy, nude, thighhighs, vaginal, medium_breasts, spread_legs, bar_censor, large_breasts, lying, scarf, sweat |
| 1 | 5 |  |  |  |  |  | 1girl, blush, large_breasts, looking_at_viewer, solo, white_background, nipples, simple_background, nude, ass, collarbone, looking_back, navel, smile |
| 2 | 8 |  |  |  |  |  | 1girl, solo, looking_at_viewer, open_mouth, simple_background, red_scarf, upper_body, short_sleeves, smile, blush, white_background, dress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | nipples | blush | hetero | open_mouth | penis | 1boy | sex | solo_focus | cum_in_pussy | nude | thighhighs | vaginal | medium_breasts | spread_legs | bar_censor | large_breasts | lying | scarf | sweat | looking_at_viewer | solo | white_background | simple_background | ass | collarbone | looking_back | navel | smile | red_scarf | upper_body | short_sleeves | dress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:--------|:---------|:-------------|:--------|:-------|:------|:-------------|:---------------|:-------|:-------------|:----------|:-----------------|:--------------|:-------------|:----------------|:--------|:--------|:--------|:--------------------|:-------|:-------------------|:--------------------|:------|:-------------|:---------------|:--------|:--------|:------------|:-------------|:----------------|:--------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | | | | | | X | | | | | | X | | | | X | X | X | X | X | X | X | X | X | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | | X | | | | | | | | | | | | | | | | X | X | X | X | | | | | X | X | X | X | X |
| CyberHarem/katarina_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:49:41+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:12:50+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of katarina (Fire Emblem)
=================================
This is the dataset of katarina (Fire Emblem), containing 96 images and their tags.
The core tags of this character are 'purple\_hair, short\_hair, breasts, grey\_eyes, purple\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
c9573658fcf20b9c7afbea895d8d19c8c744f555 |
# Dataset Card for Wildberries products
### Dataset Summary
The dataset contains product reviews from the Russian marketplace [Wildberries](https://www.wildberries.ru), collected by mining about The dataset was collected by bruteforcing possible product identifiers (about 230 million) and querying all available feedbacks for them. The data are stored in zstd-archives containing jsonl-files. The 'nmId' in the dataset usually corresponds to the valid product article on the site, but sometimes reviews are still available to retrieve via the API even if the product is hidden. The dataset solely includes information from the reviews. To access additional data, refer to my other dataset, [wb-products](https://huggingface.co/datasets/nyuuzyou/wb-products), collected from Wildberries. Merge the necessary data using the nmId identifier mentioned earlier. It is important to note that some fields in the dataset, particularly string fields, may be empty.
### Languages
The dataset is mostly in Russian, but there may be other languages present.
## Dataset Structure
### Data Fields
This dataset includes the following fields:
- `nmId`: Identifier for the item (integer)
- `productValuation`: Product valuation (integer)
- `color`: Color of the product (string)
- `text`: Text description of the product (string)
- `answer`: Answer (string)
### Data Splits
All examples are in the train split, there is no validation split.
## Additional Information
### License
This dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:
* Use it for any purpose, including commercial projects.
* Modify it however you like.
* Distribute it without asking permission.
No attribution is required, but it's always appreciated!
CC0 license: https://creativecommons.org/publicdomain/zero/1.0/deed.en
To learn more about CC0, visit the Creative Commons website: https://creativecommons.org/publicdomain/zero/1.0/
### Dataset Curators
- [nyuuzyou](https://ducks.party)
| nyuuzyou/wb-feedbacks | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:ru",
"license:cc0-1.0",
"region:us"
] | 2024-01-17T19:50:24+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ru"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["100M<n<1B"], "source_datasets": ["original"], "task_categories": ["text-generation", "text-classification"], "task_ids": ["language-modeling"], "pretty_name": "Wildberries products"} | 2024-01-17T20:04:38+00:00 | [] | [
"ru"
] | TAGS
#task_categories-text-generation #task_categories-text-classification #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Russian #license-cc0-1.0 #region-us
|
# Dataset Card for Wildberries products
### Dataset Summary
The dataset contains product reviews from the Russian marketplace Wildberries, collected by mining about The dataset was collected by bruteforcing possible product identifiers (about 230 million) and querying all available feedbacks for them. The data are stored in zstd-archives containing jsonl-files. The 'nmId' in the dataset usually corresponds to the valid product article on the site, but sometimes reviews are still available to retrieve via the API even if the product is hidden. The dataset solely includes information from the reviews. To access additional data, refer to my other dataset, wb-products, collected from Wildberries. Merge the necessary data using the nmId identifier mentioned earlier. It is important to note that some fields in the dataset, particularly string fields, may be empty.
### Languages
The dataset is mostly in Russian, but there may be other languages present.
## Dataset Structure
### Data Fields
This dataset includes the following fields:
- 'nmId': Identifier for the item (integer)
- 'productValuation': Product valuation (integer)
- 'color': Color of the product (string)
- 'text': Text description of the product (string)
- 'answer': Answer (string)
### Data Splits
All examples are in the train split, there is no validation split.
## Additional Information
### License
This dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:
* Use it for any purpose, including commercial projects.
* Modify it however you like.
* Distribute it without asking permission.
No attribution is required, but it's always appreciated!
CC0 license: URL
To learn more about CC0, visit the Creative Commons website: URL
### Dataset Curators
- nyuuzyou
| [
"# Dataset Card for Wildberries products",
"### Dataset Summary\n\nThe dataset contains product reviews from the Russian marketplace Wildberries, collected by mining about The dataset was collected by bruteforcing possible product identifiers (about 230 million) and querying all available feedbacks for them. The data are stored in zstd-archives containing jsonl-files. The 'nmId' in the dataset usually corresponds to the valid product article on the site, but sometimes reviews are still available to retrieve via the API even if the product is hidden. The dataset solely includes information from the reviews. To access additional data, refer to my other dataset, wb-products, collected from Wildberries. Merge the necessary data using the nmId identifier mentioned earlier. It is important to note that some fields in the dataset, particularly string fields, may be empty.",
"### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present.",
"## Dataset Structure",
"### Data Fields\n\nThis dataset includes the following fields:\n\n- 'nmId': Identifier for the item (integer)\n- 'productValuation': Product valuation (integer)\n- 'color': Color of the product (string)\n- 'text': Text description of the product (string)\n- 'answer': Answer (string)",
"### Data Splits\n\nAll examples are in the train split, there is no validation split.",
"## Additional Information",
"### License\n\nThis dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:\n\n* Use it for any purpose, including commercial projects.\n* Modify it however you like.\n* Distribute it without asking permission.\n\nNo attribution is required, but it's always appreciated!\n\nCC0 license: URL\n\nTo learn more about CC0, visit the Creative Commons website: URL",
"### Dataset Curators\n\n- nyuuzyou"
] | [
"TAGS\n#task_categories-text-generation #task_categories-text-classification #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Russian #license-cc0-1.0 #region-us \n",
"# Dataset Card for Wildberries products",
"### Dataset Summary\n\nThe dataset contains product reviews from the Russian marketplace Wildberries, collected by mining about The dataset was collected by bruteforcing possible product identifiers (about 230 million) and querying all available feedbacks for them. The data are stored in zstd-archives containing jsonl-files. The 'nmId' in the dataset usually corresponds to the valid product article on the site, but sometimes reviews are still available to retrieve via the API even if the product is hidden. The dataset solely includes information from the reviews. To access additional data, refer to my other dataset, wb-products, collected from Wildberries. Merge the necessary data using the nmId identifier mentioned earlier. It is important to note that some fields in the dataset, particularly string fields, may be empty.",
"### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present.",
"## Dataset Structure",
"### Data Fields\n\nThis dataset includes the following fields:\n\n- 'nmId': Identifier for the item (integer)\n- 'productValuation': Product valuation (integer)\n- 'color': Color of the product (string)\n- 'text': Text description of the product (string)\n- 'answer': Answer (string)",
"### Data Splits\n\nAll examples are in the train split, there is no validation split.",
"## Additional Information",
"### License\n\nThis dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:\n\n* Use it for any purpose, including commercial projects.\n* Modify it however you like.\n* Distribute it without asking permission.\n\nNo attribution is required, but it's always appreciated!\n\nCC0 license: URL\n\nTo learn more about CC0, visit the Creative Commons website: URL",
"### Dataset Curators\n\n- nyuuzyou"
] |
bf142045023d76a9472bbdc7c0726d68d8cb57a1 |
# Dataset of panette (Fire Emblem)
This is the dataset of panette (Fire Emblem), containing 142 images and their tags.
The core tags of this character are `long_hair, yellow_eyes, orange_hair, hair_ornament, bow, hair_flower, eyeshadow, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 142 | 186.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/panette_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 142 | 104.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/panette_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 320 | 219.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/panette_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 142 | 165.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/panette_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 320 | 316.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/panette_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/panette_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | 1girl, solo, looking_at_viewer, bandaged_arm, black_dress, simple_background, flower, white_background, choker, collarbone, makeup, closed_mouth, upper_body, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | bandaged_arm | black_dress | simple_background | flower | white_background | choker | collarbone | makeup | closed_mouth | upper_body | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:---------------|:--------------|:--------------------|:---------|:-------------------|:---------|:-------------|:---------|:---------------|:-------------|:--------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/panette_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T19:50:37+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:14:35+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of panette (Fire Emblem)
================================
This is the dataset of panette (Fire Emblem), containing 142 images and their tags.
The core tags of this character are 'long\_hair, yellow\_eyes, orange\_hair, hair\_ornament, bow, hair\_flower, eyeshadow, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
935c6f4b583b502a8e577c8419d31cca0d271ba5 |
# Dataset of ira (Fire Emblem)
This is the dataset of ira (Fire Emblem), containing 122 images and their tags.
The core tags of this character are `long_hair, black_hair, breasts, purple_eyes, earrings, large_breasts, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 122 | 157.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ira_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 122 | 90.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ira_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 259 | 169.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ira_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 122 | 138.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ira_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 259 | 236.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ira_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ira_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, belt, dress, elbow_gloves, holding_sword, shoulder_armor, solo, white_gloves, breastplate, thighhighs, jewelry, looking_at_viewer, pelvic_curtain, thigh_boots |
| 1 | 15 |  |  |  |  |  | 1girl, solo, looking_at_viewer, navel, medium_breasts, nipples, blush, simple_background, thighhighs, white_background, completely_nude, hand_on_hip, pussy, standing |
| 2 | 17 |  |  |  |  |  | hetero, 1girl, penis, solo_focus, 1boy, nipples, blush, open_mouth, vaginal, cum_in_pussy, thighhighs, torn_clothes, uncensored, completely_nude, gloves, jewelry, sex_from_behind |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | belt | dress | elbow_gloves | holding_sword | shoulder_armor | solo | white_gloves | breastplate | thighhighs | jewelry | looking_at_viewer | pelvic_curtain | thigh_boots | navel | medium_breasts | nipples | blush | simple_background | white_background | completely_nude | hand_on_hip | pussy | standing | hetero | penis | solo_focus | 1boy | open_mouth | vaginal | cum_in_pussy | torn_clothes | uncensored | gloves | sex_from_behind |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:---------------|:----------------|:-----------------|:-------|:---------------|:--------------|:-------------|:----------|:--------------------|:-----------------|:--------------|:--------|:-----------------|:----------|:--------|:--------------------|:-------------------|:------------------|:--------------|:--------|:-----------|:---------|:--------|:-------------|:-------|:-------------|:----------|:---------------|:---------------|:-------------|:---------|:------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | | | | | | X | | | X | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 2 | 17 |  |  |  |  |  | X | | | | | | | | | X | X | | | | | | X | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/ira_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:04:02+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:37:02+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of ira (Fire Emblem)
============================
This is the dataset of ira (Fire Emblem), containing 122 images and their tags.
The core tags of this character are 'long\_hair, black\_hair, breasts, purple\_eyes, earrings, large\_breasts, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
8bfe43eb6171a89bf80aba3bdacce6c1e9036779 |
# Dataset of catherine (Fire Emblem)
This is the dataset of catherine (Fire Emblem), containing 86 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, breasts, dark_skin, long_hair, dark-skinned_female, ponytail, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 86 | 86.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/catherine_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 86 | 62.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/catherine_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 181 | 118.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/catherine_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 86 | 81.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/catherine_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 181 | 146.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/catherine_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/catherine_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 20 |  |  |  |  |  | 1girl, breastplate, simple_background, solo, boobplate, smile, shoulder_armor, cape, gloves, looking_at_viewer, upper_body, open_mouth |
| 1 | 5 |  |  |  |  |  | 1girl, belt, boobplate, breastplate, elbow_pads, full_body, knee_pads, long_sleeves, shiny_hair, shoulder_armor, solo, white_pants, arm_guards, parted_bangs, puffy_sleeves, white_background, armored_boots, holding_sword, simple_background, smile, black_gloves, cape, closed_mouth, fire, hand_on_hip, leg_up, looking_at_viewer, looking_away, parted_lips, standing, teeth, transparent_background |
| 2 | 8 |  |  |  |  |  | cleavage, navel, 2girls, belt, looking_at_viewer, necklace, white_bikini, collarbone, smile, 1girl, hair_flower, see-through, blue_sky, closed_mouth, drinking_straw, hand_on_hip, holding_cup, sarong, shirt, simple_background, solo, stomach |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | breastplate | simple_background | solo | boobplate | smile | shoulder_armor | cape | gloves | looking_at_viewer | upper_body | open_mouth | belt | elbow_pads | full_body | knee_pads | long_sleeves | shiny_hair | white_pants | arm_guards | parted_bangs | puffy_sleeves | white_background | armored_boots | holding_sword | black_gloves | closed_mouth | fire | hand_on_hip | leg_up | looking_away | parted_lips | standing | teeth | transparent_background | cleavage | navel | 2girls | necklace | white_bikini | collarbone | hair_flower | see-through | blue_sky | drinking_straw | holding_cup | sarong | shirt | stomach |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:--------------------|:-------|:------------|:--------|:-----------------|:-------|:---------|:--------------------|:-------------|:-------------|:-------|:-------------|:------------|:------------|:---------------|:-------------|:--------------|:-------------|:---------------|:----------------|:-------------------|:----------------|:----------------|:---------------|:---------------|:-------|:--------------|:---------|:---------------|:--------------|:-----------|:--------|:-------------------------|:-----------|:--------|:---------|:-----------|:---------------|:-------------|:--------------|:--------------|:-----------|:-----------------|:--------------|:---------|:--------|:----------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | X | | X | | | | X | | | X | | | | | | | | | | | | | | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/catherine_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:04:03+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:21:57+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of catherine (Fire Emblem)
==================================
This is the dataset of catherine (Fire Emblem), containing 86 images and their tags.
The core tags of this character are 'blonde\_hair, blue\_eyes, breasts, dark\_skin, long\_hair, dark-skinned\_female, ponytail, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
fa50612ceea97c506925abc1ca34db83d1cf00de |
# Dataset of constance (Fire Emblem)
This is the dataset of constance (Fire Emblem), containing 133 images and their tags.
The core tags of this character are `blonde_hair, hairband, multicolored_hair, colored_inner_hair, purple_hair, two-tone_hair, blue_eyes, breasts, short_hair, blue_hairband, large_breasts, earrings`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 133 | 178.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constance_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 133 | 94.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constance_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 320 | 207.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constance_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 133 | 153.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constance_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 320 | 296.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/constance_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/constance_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 37 |  |  |  |  |  | 1girl, solo, garreg_mach_monastery_uniform, long_sleeves, holding, jewelry, hand_fan, simple_background, smile, drill_hair, closed_mouth, looking_at_viewer, open_mouth |
| 1 | 5 |  |  |  |  |  | 1girl, breasts_out, hetero, nipples, rape, garreg_mach_monastery_uniform, medium_breasts, open_mouth, torn_clothes, vaginal, 2boys, crying, cum_in_pussy, long_sleeves, medium_hair, multiple_penises, solo_focus, tears, thighhighs, breasts_apart, holding_another's_wrist, jewelry, mmf_threesome, mosaic_censoring, restrained, sex_from_behind |
| 2 | 5 |  |  |  |  |  | navel, nipples, 1girl, blush, completely_nude, female_pubic_hair, jewelry, bangs, open_mouth, purple_eyes, glasses, id_card, lanyard, pussy, solo |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | garreg_mach_monastery_uniform | long_sleeves | holding | jewelry | hand_fan | simple_background | smile | drill_hair | closed_mouth | looking_at_viewer | open_mouth | breasts_out | hetero | nipples | rape | medium_breasts | torn_clothes | vaginal | 2boys | crying | cum_in_pussy | medium_hair | multiple_penises | solo_focus | tears | thighhighs | breasts_apart | holding_another's_wrist | mmf_threesome | mosaic_censoring | restrained | sex_from_behind | navel | blush | completely_nude | female_pubic_hair | bangs | purple_eyes | glasses | id_card | lanyard | pussy |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------------------|:---------------|:----------|:----------|:-----------|:--------------------|:--------|:-------------|:---------------|:--------------------|:-------------|:--------------|:---------|:----------|:-------|:-----------------|:---------------|:----------|:--------|:---------|:---------------|:--------------|:-------------------|:-------------|:--------|:-------------|:----------------|:--------------------------|:----------------|:-------------------|:-------------|:------------------|:--------|:--------|:------------------|:--------------------|:--------|:--------------|:----------|:----------|:----------|:--------|
| 0 | 37 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | X | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | X | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/constance_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:05:27+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:49:44+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of constance (Fire Emblem)
==================================
This is the dataset of constance (Fire Emblem), containing 133 images and their tags.
The core tags of this character are 'blonde\_hair, hairband, multicolored\_hair, colored\_inner\_hair, purple\_hair, two-tone\_hair, blue\_eyes, breasts, short\_hair, blue\_hairband, large\_breasts, earrings', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
677253b18be611076dbee3ea90230b65c34591a1 |
Generated product names and their associated store types. Use to train [store_type_classifyer](https://huggingface.co/beny2000/store_type_classifyer) | beny2000/product_store_type_classification | [
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
] | 2024-01-17T20:17:31+00:00 | {"language": ["en"], "license": "mit", "task_categories": ["text-classification"]} | 2024-01-17T20:19:34+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #language-English #license-mit #region-us
|
Generated product names and their associated store types. Use to train store_type_classifyer | [] | [
"TAGS\n#task_categories-text-classification #language-English #license-mit #region-us \n"
] |
988171e043e2be18e85214f188ba2e9a47fa99f1 |
## PUG Animals
The PUG: Animals dataset contains 215,040 pre-rendered images based on Unreal-Engine using 70 animal assets, 64 environments, 3 sizes, 4 textures, under 4 camera orientations.
It was designed with the intent to create a dataset with variation factors available. Inspired by research on out-of-distribution generalization, PUG: Animals allows one to precisely control distribution shifts between training and testing which can provide better insight on how a deep neural network generalizes on held out variation factors.
## LICENSE
The datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.
## Citing PUG
If you use one of the PUG datasets, please cite:
```
@misc{bordes2023pug,
title={PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning},
author={Florian Bordes and Shashank Shekhar and Mark Ibrahim and Diane Bouchacourt and Pascal Vincent and Ari S. Morcos},
year={2023},
eprint={2308.03977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## To learn more about the PUG datasets:
Please visit the [website](https://pug.metademolab.com/) and the [github](https://github.com/facebookresearch/PUG) | facebook/PUG_Animals | [
"license:cc-by-nc-4.0",
"arxiv:2308.03977",
"region:us"
] | 2024-01-17T20:23:06+00:00 | {"license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "world_name", "dtype": "string"}, {"name": "character_name", "dtype": "string"}, {"name": "character_scale", "dtype": "float64"}, {"name": "camera_yaw", "dtype": "int64"}, {"name": "character_texture", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82030062942.72, "num_examples": 215040}], "download_size": 84628407574, "dataset_size": 82030062942.72}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-18T16:05:43+00:00 | [
"2308.03977"
] | [] | TAGS
#license-cc-by-nc-4.0 #arxiv-2308.03977 #region-us
|
## PUG Animals
The PUG: Animals dataset contains 215,040 pre-rendered images based on Unreal-Engine using 70 animal assets, 64 environments, 3 sizes, 4 textures, under 4 camera orientations.
It was designed with the intent to create a dataset with variation factors available. Inspired by research on out-of-distribution generalization, PUG: Animals allows one to precisely control distribution shifts between training and testing which can provide better insight on how a deep neural network generalizes on held out variation factors.
## LICENSE
The datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.
## Citing PUG
If you use one of the PUG datasets, please cite:
## To learn more about the PUG datasets:
Please visit the website and the github | [
"## PUG Animals\nThe PUG: Animals dataset contains 215,040 pre-rendered images based on Unreal-Engine using 70 animal assets, 64 environments, 3 sizes, 4 textures, under 4 camera orientations. \nIt was designed with the intent to create a dataset with variation factors available. Inspired by research on out-of-distribution generalization, PUG: Animals allows one to precisely control distribution shifts between training and testing which can provide better insight on how a deep neural network generalizes on held out variation factors.",
"## LICENSE\nThe datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.",
"## Citing PUG\nIf you use one of the PUG datasets, please cite:",
"## To learn more about the PUG datasets:\nPlease visit the website and the github"
] | [
"TAGS\n#license-cc-by-nc-4.0 #arxiv-2308.03977 #region-us \n",
"## PUG Animals\nThe PUG: Animals dataset contains 215,040 pre-rendered images based on Unreal-Engine using 70 animal assets, 64 environments, 3 sizes, 4 textures, under 4 camera orientations. \nIt was designed with the intent to create a dataset with variation factors available. Inspired by research on out-of-distribution generalization, PUG: Animals allows one to precisely control distribution shifts between training and testing which can provide better insight on how a deep neural network generalizes on held out variation factors.",
"## LICENSE\nThe datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.",
"## Citing PUG\nIf you use one of the PUG datasets, please cite:",
"## To learn more about the PUG datasets:\nPlease visit the website and the github"
] |
9b3409dfb6f1eb39a2741bfaa44f792600106dc7 |
# Dataset of naga (Fire Emblem)
This is the dataset of naga (Fire Emblem), containing 30 images and their tags.
The core tags of this character are `long_hair, pointy_ears, green_hair, breasts, green_eyes, very_long_hair, large_breasts, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 30 | 34.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/naga_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 30 | 21.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/naga_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 58 | 35.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/naga_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 30 | 31.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/naga_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 58 | 48.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/naga_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/naga_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, solo, dress, navel, looking_at_viewer, bracelet, smile |
| 1 | 5 |  |  |  |  |  | 1boy, hetero, nude, tiara, nipples, penis, 1girl, blush, cum_on_breasts, ejaculation, facial, paizuri, uncensored, 3girls, abs, ass, group_sex, looking_at_viewer, parted_lips, ponytail, ribbon, smile, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | dress | navel | looking_at_viewer | bracelet | smile | 1boy | hetero | nude | tiara | nipples | penis | blush | cum_on_breasts | ejaculation | facial | paizuri | uncensored | 3girls | abs | ass | group_sex | parted_lips | ponytail | ribbon | solo_focus |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------|:--------------------|:-----------|:--------|:-------|:---------|:-------|:--------|:----------|:--------|:--------|:-----------------|:--------------|:---------|:----------|:-------------|:---------|:------|:------|:------------|:--------------|:-----------|:---------|:-------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/naga_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:28:04+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:34:34+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of naga (Fire Emblem)
=============================
This is the dataset of naga (Fire Emblem), containing 30 images and their tags.
The core tags of this character are 'long\_hair, pointy\_ears, green\_hair, breasts, green\_eyes, very\_long\_hair, large\_breasts, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
86b52fb8f9e4b0e4effcd9c0bb94a42d34ff4f99 |
# Dataset of reginn (Fire Emblem)
This is the dataset of reginn (Fire Emblem), containing 47 images and their tags.
The core tags of this character are `blue_hair, ponytail, yellow_eyes, bangs, long_hair, earrings, breasts, braid`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 47 | 73.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/reginn_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 47 | 39.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/reginn_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 125 | 90.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/reginn_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 47 | 63.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/reginn_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 125 | 130.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/reginn_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/reginn_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, hair_bow, kimono, looking_at_viewer, solo, wide_sleeves, ahoge, fur_trim, grin, jewelry, long_sleeves, black_footwear, floral_print, full_body, hakama_skirt, holding_animal, one_eye_closed, red_bow, scroll, simple_background |
| 1 | 15 |  |  |  |  |  | 1girl, solo, jewelry, looking_at_viewer, smile, cape, cleavage, black_gloves, black_bodysuit, medium_breasts, open_mouth, blush, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hair_bow | kimono | looking_at_viewer | solo | wide_sleeves | ahoge | fur_trim | grin | jewelry | long_sleeves | black_footwear | floral_print | full_body | hakama_skirt | holding_animal | one_eye_closed | red_bow | scroll | simple_background | smile | cape | cleavage | black_gloves | black_bodysuit | medium_breasts | open_mouth | blush | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:---------|:--------------------|:-------|:---------------|:--------|:-----------|:-------|:----------|:---------------|:-----------------|:---------------|:------------|:---------------|:-----------------|:-----------------|:----------|:---------|:--------------------|:--------|:-------|:-----------|:---------------|:-----------------|:-----------------|:-------------|:--------|:-------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | | | X | X | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/reginn_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:28:06+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:38:17+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of reginn (Fire Emblem)
===============================
This is the dataset of reginn (Fire Emblem), containing 47 images and their tags.
The core tags of this character are 'blue\_hair, ponytail, yellow\_eyes, bangs, long\_hair, earrings, breasts, braid', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1665e82056b6377525c483b4ef15971572570e98 |
# Dataset of velvet (Fire Emblem)
This is the dataset of velvet (Fire Emblem), containing 111 images and their tags.
The core tags of this character are `long_hair, breasts, brown_hair, animal_ears, large_breasts, facial_mark, rabbit_ears, dark_skin, braid, red_eyes, brown_eyes, tail, dark-skinned_female`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 111 | 130.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velvet_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 111 | 75.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velvet_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 249 | 147.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velvet_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 111 | 114.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velvet_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 249 | 203.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velvet_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/velvet_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, penis, solo_focus, black_hair, nude, blush, looking_at_viewer, bangs, cum_in_mouth, heart, irrumatio, nipples, pov, simple_background, sweat, uncensored, whisker_markings, white_background |
| 1 | 8 |  |  |  |  |  | hetero, nipples, 1boy, 1girl, penis, black_hair, navel, sex, solo_focus, completely_nude, cum_in_pussy, open_mouth, spread_legs, vaginal, blush, bangs, cum_on_breasts, mosaic_censoring, on_back, one_eye_closed, shiny |
| 2 | 13 |  |  |  |  |  | 1girl, hetero, sex_from_behind, open_mouth, blush, nipples, doggystyle, 1boy, solo_focus, penis, twin_braids, bestiality, saliva, sweat, vaginal, bent_over, tongue |
| 3 | 6 |  |  |  |  |  | 1girl, lying, nude, solo, blush, looking_at_viewer, smile, twin_braids, ass, horns, nipples, pussy |
| 4 | 28 |  |  |  |  |  | 1girl, solo, armor, navel, looking_at_viewer, medium_breasts, black_hair, cleavage, twin_braids, rabbit_girl, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | penis | solo_focus | black_hair | nude | blush | looking_at_viewer | bangs | cum_in_mouth | heart | irrumatio | nipples | pov | simple_background | sweat | uncensored | whisker_markings | white_background | navel | sex | completely_nude | cum_in_pussy | open_mouth | spread_legs | vaginal | cum_on_breasts | mosaic_censoring | on_back | one_eye_closed | shiny | sex_from_behind | doggystyle | twin_braids | bestiality | saliva | bent_over | tongue | lying | solo | smile | ass | horns | pussy | armor | medium_breasts | cleavage | rabbit_girl |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:--------|:-------------|:-------------|:-------|:--------|:--------------------|:--------|:---------------|:--------|:------------|:----------|:------|:--------------------|:--------|:-------------|:-------------------|:-------------------|:--------|:------|:------------------|:---------------|:-------------|:--------------|:----------|:-----------------|:-------------------|:----------|:-----------------|:--------|:------------------|:-------------|:--------------|:-------------|:---------|:------------|:---------|:--------|:-------|:--------|:------|:--------|:--------|:--------|:-----------------|:-----------|:--------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | X | X | | X | | X | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | X | X | X | X | X | | | X | | | | | | X | | | X | | | | | | | | X | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | | X | | | | | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | | | | |
| 4 | 28 |  |  |  |  |  | | X | | | | X | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | X | X | | | | X | X | X | X |
| CyberHarem/velvet_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:28:09+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:55:57+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of velvet (Fire Emblem)
===============================
This is the dataset of velvet (Fire Emblem), containing 111 images and their tags.
The core tags of this character are 'long\_hair, breasts, brown\_hair, animal\_ears, large\_breasts, facial\_mark, rabbit\_ears, dark\_skin, braid, red\_eyes, brown\_eyes, tail, dark-skinned\_female', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
f118255d45202af129f8e702ef4d52383762825a |
# Dataset of fir (Fire Emblem)
This is the dataset of fir (Fire Emblem), containing 135 images and their tags.
The core tags of this character are `ponytail, purple_eyes, purple_hair, long_hair, breasts, ribbon, hair_ribbon, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 135 | 182.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fir_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 135 | 97.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fir_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 327 | 215.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fir_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 135 | 159.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fir_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 327 | 312.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fir_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/fir_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, blush, hetero, penis, 1boy, nipples, solo_focus, gloves, cum_in_pussy, mosaic_censoring, open_mouth, large_breasts, sex, vaginal, looking_at_viewer, medium_breasts |
| 1 | 14 |  |  |  |  |  | 1girl, fake_animal_ears, open_mouth, rabbit_ears, solo, leotard, white_gloves, looking_at_viewer, blush, cleavage, white_background, playboy_bunny, simple_background, carrot, holding, medium_breasts, official_alternate_costume, bow, full_body, high_heels, sleeveless, smile |
| 2 | 38 |  |  |  |  |  | solo, 1girl, fingerless_gloves, holding_sword, looking_at_viewer, simple_background, dress, sheath, white_background, short_sleeves, katana, boots |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | hetero | penis | 1boy | nipples | solo_focus | gloves | cum_in_pussy | mosaic_censoring | open_mouth | large_breasts | sex | vaginal | looking_at_viewer | medium_breasts | fake_animal_ears | rabbit_ears | solo | leotard | white_gloves | cleavage | white_background | playboy_bunny | simple_background | carrot | holding | official_alternate_costume | bow | full_body | high_heels | sleeveless | smile | fingerless_gloves | holding_sword | dress | sheath | short_sleeves | katana | boots |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:---------|:--------|:-------|:----------|:-------------|:---------|:---------------|:-------------------|:-------------|:----------------|:------|:----------|:--------------------|:-----------------|:-------------------|:--------------|:-------|:----------|:---------------|:-----------|:-------------------|:----------------|:--------------------|:---------|:----------|:-----------------------------|:------|:------------|:-------------|:-------------|:--------|:--------------------|:----------------|:--------|:---------|:----------------|:---------|:--------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 2 | 38 |  |  |  |  |  | X | | | | | | | | | | | | | | X | | | | X | | | | X | | X | | | | | | | | | X | X | X | X | X | X | X |
| CyberHarem/fir_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:28:29+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:04:39+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of fir (Fire Emblem)
============================
This is the dataset of fir (Fire Emblem), containing 135 images and their tags.
The core tags of this character are 'ponytail, purple\_eyes, purple\_hair, long\_hair, breasts, ribbon, hair\_ribbon, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
8b05663b23070de3319c107df29c7882b2eeef25 |
All 2507 questions from the first stage of ExpertQA. | katielink/expertqa | [
"license:mit",
"region:us"
] | 2024-01-17T20:32:13+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "metadata.field", "dtype": "string"}, {"name": "metadata.question_type", "dtype": "string"}, {"name": "metadata.specific_field", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 611616, "num_examples": 2507}], "download_size": 206376, "dataset_size": 611616}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-17T23:26:14+00:00 | [] | [] | TAGS
#license-mit #region-us
|
All 2507 questions from the first stage of ExpertQA. | [] | [
"TAGS\n#license-mit #region-us \n"
] |
fcc31218c0442e2cbf6ea6714e2eda31fc50573b |
# Dataset of may (Fire Emblem)
This is the dataset of may (Fire Emblem), containing 82 images and their tags.
The core tags of this character are `twintails, pink_hair, long_hair, red_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 82 | 90.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/may_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 82 | 57.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/may_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 180 | 110.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/may_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 82 | 82.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/may_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 180 | 149.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/may_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/may_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, solo, breastplate, gloves, smile, simple_background, circlet, upper_body, open_mouth, looking_at_viewer, pink_eyes |
| 1 | 5 |  |  |  |  |  | 1girl, boots, full_body, gloves, smile, solo, breastplate, one_eye_closed, simple_background, white_background, circlet, dress |
| 2 | 6 |  |  |  |  |  | 1boy, blush, hetero, nipples, open_mouth, penis, solo_focus, 1girl, cum_in_pussy, heart-shaped_pupils, navel, sex, spread_legs, vaginal, bar_censor, brown_eyes, completely_nude, large_breasts, lying, medium_breasts, smile, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | breastplate | gloves | smile | simple_background | circlet | upper_body | open_mouth | looking_at_viewer | pink_eyes | boots | full_body | one_eye_closed | white_background | dress | 1boy | blush | hetero | nipples | penis | solo_focus | cum_in_pussy | heart-shaped_pupils | navel | sex | spread_legs | vaginal | bar_censor | brown_eyes | completely_nude | large_breasts | lying | medium_breasts | uncensored |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------|:---------|:--------|:--------------------|:----------|:-------------|:-------------|:--------------------|:------------|:--------|:------------|:-----------------|:-------------------|:--------|:-------|:--------|:---------|:----------|:--------|:-------------|:---------------|:----------------------|:--------|:------|:--------------|:----------|:-------------|:-------------|:------------------|:----------------|:--------|:-----------------|:-------------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | | | X | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/may_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:37:42+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:52:24+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of may (Fire Emblem)
============================
This is the dataset of may (Fire Emblem), containing 82 images and their tags.
The core tags of this character are 'twintails, pink\_hair, long\_hair, red\_eyes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
7ce55042fc265e46350e09b02c10b3f212228631 |
# Dataset of flora (Fire Emblem)
This is the dataset of flora (Fire Emblem), containing 171 images and their tags.
The core tags of this character are `blue_hair, long_hair, maid_headdress, twintails, breasts, grey_eyes, blue_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 171 | 188.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flora_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 171 | 120.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flora_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 379 | 230.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flora_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 171 | 172.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flora_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 379 | 305.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flora_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/flora_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, maid_apron, solo, black_thighhighs, dagger, long_sleeves, puffy_sleeves, zettai_ryouiki, bridal_gauntlets, dress, holding_knife, ice, simple_background, snowflakes |
| 1 | 6 |  |  |  |  |  | 1girl, solo, thighhighs, looking_at_viewer, navel, panties, blush, nipples, smile, large_breasts, maid, simple_background, white_background |
| 2 | 16 |  |  |  |  |  | 1girl, 1boy, hetero, blush, nipples, open_mouth, large_breasts, penis, sex, completely_nude, cum, solo_focus, thighhighs, heart-shaped_pupils, navel, simple_background, uncensored |
| 3 | 7 |  |  |  |  |  | 1girl, hetero, penis, pussy, vaginal, mosaic_censoring, nipples, solo_focus, 1boy, breasts_out, medium_breasts, tears, blush, long_sleeves, rape, thighhighs, ass, cum, doggystyle, looking_back, maid_apron, ponytail, puffy_sleeves, sex_from_behind, spread_legs, teeth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | maid_apron | solo | black_thighhighs | dagger | long_sleeves | puffy_sleeves | zettai_ryouiki | bridal_gauntlets | dress | holding_knife | ice | simple_background | snowflakes | thighhighs | looking_at_viewer | navel | panties | blush | nipples | smile | large_breasts | maid | white_background | 1boy | hetero | open_mouth | penis | sex | completely_nude | cum | solo_focus | heart-shaped_pupils | uncensored | pussy | vaginal | mosaic_censoring | breasts_out | medium_breasts | tears | rape | ass | doggystyle | looking_back | ponytail | sex_from_behind | spread_legs | teeth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:-------|:-------------------|:---------|:---------------|:----------------|:-----------------|:-------------------|:--------|:----------------|:------|:--------------------|:-------------|:-------------|:--------------------|:--------|:----------|:--------|:----------|:--------|:----------------|:-------|:-------------------|:-------|:---------|:-------------|:--------|:------|:------------------|:------|:-------------|:----------------------|:-------------|:--------|:----------|:-------------------|:--------------|:-----------------|:--------|:-------|:------|:-------------|:---------------|:-----------|:------------------|:--------------|:--------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 16 |  |  |  |  |  | X | | | | | | | | | | | | X | | X | | X | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | | | | X | X | | | | | | | | X | | | | X | X | | | | | X | X | | X | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/flora_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:37:43+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:15:56+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of flora (Fire Emblem)
==============================
This is the dataset of flora (Fire Emblem), containing 171 images and their tags.
The core tags of this character are 'blue\_hair, long\_hair, maid\_headdress, twintails, breasts, grey\_eyes, blue\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
09f0e49e992bbecf69801c3c07601f320005bbc3 |
# Dataset of gunnthra (Fire Emblem)
This is the dataset of gunnthra (Fire Emblem), containing 244 images and their tags.
The core tags of this character are `pink_hair, long_hair, breasts, blue_eyes, earrings, large_breasts, multicolored_hair, gradient_hair, hair_ornament, blonde_hair, hair_flower`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 244 | 289.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gunnthra_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 244 | 171.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gunnthra_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 548 | 345.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gunnthra_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 244 | 258.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gunnthra_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 548 | 471.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gunnthra_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/gunnthra_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 36 |  |  |  |  |  | 1girl, jewelry, solo, smile, flower, looking_at_viewer, bikini, cleavage, head_wreath, simple_background, navel, closed_mouth, collarbone, blush, sarong |
| 1 | 5 |  |  |  |  |  | 1girl, bangs, full_body, fur_trim, jewelry, kimono, long_sleeves, obi, sandals, shiny_hair, solo, wide_sleeves, flower, gradient, holding_sword, katana, simple_background, white_background, floral_print, looking_at_viewer, open_mouth, smile, tabi, sheathed, sitting, snowflakes, transparent_background |
| 2 | 5 |  |  |  |  |  | 1girl, flower, jewelry, kimono, smile, solo, fur_trim, long_sleeves, simple_background, upper_body, wide_sleeves, looking_at_viewer, obi, open_mouth, white_background |
| 3 | 18 |  |  |  |  |  | hetero, sex, 1girl, nipples, solo_focus, vaginal, 1boy, jewelry, blush, cum_in_pussy, penis, censored, navel, open_mouth, smile, cowgirl_position, girl_on_top, nude, spread_legs, overflow |
| 4 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, looking_at_viewer, nipples, paizuri, penis, solo_focus, hat, jewelry, pov, breasts_squeezed_together, tongue_out, uncensored, blush, cum_on_body, ejaculation, flower, nude, open_mouth, projectile_cum, sweat, veil |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, heart, hetero, jewelry, huge_breasts, solo_focus, simple_background, breasts_out, closed_mouth, gloves, guided_breast_grab, lactation, looking_at_viewer, nipples, open_mouth, smile, upper_body, veil |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jewelry | solo | smile | flower | looking_at_viewer | bikini | cleavage | head_wreath | simple_background | navel | closed_mouth | collarbone | blush | sarong | bangs | full_body | fur_trim | kimono | long_sleeves | obi | sandals | shiny_hair | wide_sleeves | gradient | holding_sword | katana | white_background | floral_print | open_mouth | tabi | sheathed | sitting | snowflakes | transparent_background | upper_body | hetero | sex | nipples | solo_focus | vaginal | 1boy | cum_in_pussy | penis | censored | cowgirl_position | girl_on_top | nude | spread_legs | overflow | paizuri | hat | pov | breasts_squeezed_together | tongue_out | uncensored | cum_on_body | ejaculation | projectile_cum | sweat | veil | heart | huge_breasts | breasts_out | gloves | guided_breast_grab | lactation |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:-------|:--------|:---------|:--------------------|:---------|:-----------|:--------------|:--------------------|:--------|:---------------|:-------------|:--------|:---------|:--------|:------------|:-----------|:---------|:---------------|:------|:----------|:-------------|:---------------|:-----------|:----------------|:---------|:-------------------|:---------------|:-------------|:-------|:-----------|:----------|:-------------|:-------------------------|:-------------|:---------|:------|:----------|:-------------|:----------|:-------|:---------------|:--------|:-----------|:-------------------|:--------------|:-------|:--------------|:-----------|:----------|:------|:------|:----------------------------|:-------------|:-------------|:--------------|:--------------|:-----------------|:--------|:-------|:--------|:---------------|:--------------|:---------|:---------------------|:------------|
| 0 | 36 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | | | | X | | | | | | | | X | X | X | X | | | X | | | | X | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 18 |  |  |  |  |  | X | X | | X | | | | | | | X | | | X | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | | | X | X | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | X | | X | X | | X | | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | X | | X | | | | X | | X | | X | | | | | | | | | | | | | | | | X | | | | | | X | X | | X | X | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X |
| CyberHarem/gunnthra_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:37:56+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:31:00+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of gunnthra (Fire Emblem)
=================================
This is the dataset of gunnthra (Fire Emblem), containing 244 images and their tags.
The core tags of this character are 'pink\_hair, long\_hair, breasts, blue\_eyes, earrings, large\_breasts, multicolored\_hair, gradient\_hair, hair\_ornament, blonde\_hair, hair\_flower', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
e77069a876d1902dd7c6d312d26bfd657b25103f |
# Dataset of minerva (Fire Emblem)
This is the dataset of minerva (Fire Emblem), containing 125 images and their tags.
The core tags of this character are `red_hair, red_eyes, short_hair, headband, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 125 | 133.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minerva_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 125 | 84.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minerva_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 272 | 163.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minerva_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 125 | 121.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minerva_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 272 | 214.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minerva_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/minerva_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, rabbit_ears, solo, hat, pantyhose, fake_animal_ears, leotard, playboy_bunny, white_gloves, looking_at_viewer, smile, cleavage, flower, rabbit_tail, sleeveless, blush, medium_breasts, simple_background |
| 1 | 22 |  |  |  |  |  | 1girl, gloves, solo, red_armor, holding_weapon, breastplate, white_background, boots, simple_background, shoulder_armor, spear |
| 2 | 12 |  |  |  |  |  | 1girl, solo, simple_background, red_armor, upper_body, white_background, looking_at_viewer, closed_mouth, breastplate, smile |
| 3 | 12 |  |  |  |  |  | hetero, solo_focus, 1girl, nipples, penis, 1boy, blush, large_breasts, censored, sex, open_mouth, vaginal, cum_in_pussy, cum_on_breasts, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | rabbit_ears | solo | hat | pantyhose | fake_animal_ears | leotard | playboy_bunny | white_gloves | looking_at_viewer | smile | cleavage | flower | rabbit_tail | sleeveless | blush | medium_breasts | simple_background | gloves | red_armor | holding_weapon | breastplate | white_background | boots | shoulder_armor | spear | upper_body | closed_mouth | hetero | solo_focus | nipples | penis | 1boy | large_breasts | censored | sex | open_mouth | vaginal | cum_in_pussy | cum_on_breasts | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-------|:------|:------------|:-------------------|:----------|:----------------|:---------------|:--------------------|:--------|:-----------|:---------|:--------------|:-------------|:--------|:-----------------|:--------------------|:---------|:------------|:-----------------|:--------------|:-------------------|:--------|:-----------------|:--------|:-------------|:---------------|:---------|:-------------|:----------|:--------|:-------|:----------------|:-----------|:------|:-------------|:----------|:---------------|:-----------------|:--------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 22 |  |  |  |  |  | X | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | | X | | | | | | | X | X | | | | | | | X | | X | | X | X | | | | X | X | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/minerva_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:37:58+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:10:13+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of minerva (Fire Emblem)
================================
This is the dataset of minerva (Fire Emblem), containing 125 images and their tags.
The core tags of this character are 'red\_hair, red\_eyes, short\_hair, headband, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
5ca1feb11b9c5c579468aee0c353c9f15d78c292 |
# Portuguese-Corpus (tokenized)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://nkluge-correa.github.io/TeenyTinyLlama/
- **Repository:** https://github.com/Nkluge-correa/TeenyTinyLlama
- **Paper:** [TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://arxiv.org/abs/2401.16640)
- **Point of Contact:** [AIRES at PUCRS](mailto:[email protected])
### Dataset Summary
This repository has a tokenized version (using the [TeenyTinyLlama tokenizer](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m)) of the [Portuguese-Corpus dataset](https://huggingface.co/datasets/nicholasKluge/Pt-Corpus). All sequences are 2048 tokens long. This dataset was used in "_[TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://arxiv.org/abs/2401.16640)_".
For more information, see the [original dataset card](https://huggingface.co/datasets/nicholasKluge/Pt-Corpus).
## Languages
Portuguese.
## Dataset Structure
### Data Instances
The dataset consists of the following features:
- **input_ids:** sequence of tokens.
- **attention_mask:** binary tensor indicating the position of the padded indices.
- **labels:** sequence of tokens.
### Data Fields
```python
{
"input_ids": [ 1026, 1531, 1009, 8067,...],
"attention_mask": [1, 1, 1, 1, ...],
"labels": [ 1026, 1531, 1009, 8067,...]
}
```
### Data Splits
Available splits are `train` (~ 2M) and `test` (20K).
```python
from datasets import load_dataset
dataset = load_dataset("nicholasKluge/Pt-Corpus-tokenized", split='train')
# If you don't want to download the entire dataset, set streaming to `True`
dataset = load_dataset("nicholasKluge/Pt-Corpus-tokenized", split='train', streaming=True)
```
## Additional Information
### Dataset Curators
[Nicholas Kluge Corrêa](mailto:[email protected]).
### Citation Information
```latex
@misc{correa24ttllama,
title = {TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese},
author = {Corr{\^e}a, Nicholas Kluge and Falk, Sophia and Fatimah, Shiza and Sen, Aniket and De Oliveira, Nythamar},
journal={arXiv preprint arXiv:2401.16640},
year={2024}
}
```
### Contributions
If you would like to contribute, contact me at [[email protected]](mailto:[email protected])!
| nicholasKluge/Pt-Corpus-tokenized | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:pt",
"license:other",
"portuguese",
"language-modeling",
"arxiv:2401.16640",
"region:us"
] | 2024-01-17T20:38:45+00:00 | {"language": ["pt"], "license": "other", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "Pt-Corpus tokenized", "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 53397189200.0, "num_examples": 2004700}, {"name": "test", "num_bytes": 532720000.0, "num_examples": 20000}], "download_size": 16064350610, "dataset_size": 53929909200.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "tags": ["portuguese", "language-modeling"]} | 2024-02-15T18:09:55+00:00 | [
"2401.16640"
] | [
"pt"
] | TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-Portuguese #license-other #portuguese #language-modeling #arxiv-2401.16640 #region-us
|
# Portuguese-Corpus (tokenized)
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Additional Information
- Dataset Curators
- Citation Information
- Contributions
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese
- Point of Contact: AIRES at PUCRS
### Dataset Summary
This repository has a tokenized version (using the TeenyTinyLlama tokenizer) of the Portuguese-Corpus dataset. All sequences are 2048 tokens long. This dataset was used in "_TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese_".
For more information, see the original dataset card.
## Languages
Portuguese.
## Dataset Structure
### Data Instances
The dataset consists of the following features:
- input_ids: sequence of tokens.
- attention_mask: binary tensor indicating the position of the padded indices.
- labels: sequence of tokens.
### Data Fields
### Data Splits
Available splits are 'train' (~ 2M) and 'test' (20K).
## Additional Information
### Dataset Curators
Nicholas Kluge Corrêa.
### Contributions
If you would like to contribute, contact me at nicholas@URL!
| [
"# Portuguese-Corpus (tokenized)",
"## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Additional Information\n - Dataset Curators\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese\n- Point of Contact: AIRES at PUCRS",
"### Dataset Summary\n\nThis repository has a tokenized version (using the TeenyTinyLlama tokenizer) of the Portuguese-Corpus dataset. All sequences are 2048 tokens long. This dataset was used in \"_TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese_\".\n\nFor more information, see the original dataset card.",
"## Languages\n\nPortuguese.",
"## Dataset Structure",
"### Data Instances\n\nThe dataset consists of the following features:\n\n- input_ids: sequence of tokens.\n- attention_mask: binary tensor indicating the position of the padded indices.\n- labels: sequence of tokens.",
"### Data Fields",
"### Data Splits\n\nAvailable splits are 'train' (~ 2M) and 'test' (20K).",
"## Additional Information",
"### Dataset Curators\n\nNicholas Kluge Corrêa.",
"### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!"
] | [
"TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Portuguese #license-other #portuguese #language-modeling #arxiv-2401.16640 #region-us \n",
"# Portuguese-Corpus (tokenized)",
"## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Additional Information\n - Dataset Curators\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese\n- Point of Contact: AIRES at PUCRS",
"### Dataset Summary\n\nThis repository has a tokenized version (using the TeenyTinyLlama tokenizer) of the Portuguese-Corpus dataset. All sequences are 2048 tokens long. This dataset was used in \"_TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese_\".\n\nFor more information, see the original dataset card.",
"## Languages\n\nPortuguese.",
"## Dataset Structure",
"### Data Instances\n\nThe dataset consists of the following features:\n\n- input_ids: sequence of tokens.\n- attention_mask: binary tensor indicating the position of the padded indices.\n- labels: sequence of tokens.",
"### Data Fields",
"### Data Splits\n\nAvailable splits are 'train' (~ 2M) and 'test' (20K).",
"## Additional Information",
"### Dataset Curators\n\nNicholas Kluge Corrêa.",
"### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!"
] |
ce21024eb37f2d0f57421904c49c880df1bccb2f |
# Dataset of nifl (Fire Emblem)
This is the dataset of nifl (Fire Emblem), containing 86 images and their tags.
The core tags of this character are `blue_hair, blue_eyes, breasts, bangs, short_hair, large_breasts, hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 86 | 130.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nifl_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 86 | 68.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nifl_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 207 | 146.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nifl_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 86 | 112.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nifl_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 207 | 216.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nifl_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nifl_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 32 |  |  |  |  |  | 1girl, solo, fur_trim, black_gloves, elbow_gloves, short_dress, looking_at_viewer, blue_dress, white_cape, ice, medium_breasts, cleavage, clothing_cutout |
| 1 | 8 |  |  |  |  |  | 1girl, solo, cleavage_cutout, hat, looking_at_viewer, navel, white_bikini, blue_bikini, official_alternate_costume, side-tie_bikini_bottom, white_headwear, blue_flower, hair_flower, holding, long_hair, long_sleeves, open_jacket, sidelocks, spoon |
| 2 | 6 |  |  |  |  |  | 1girl, blush, nipples, cum_in_pussy, hetero, penis, 1boy, mosaic_censoring, navel, cum_on_breasts, cum_on_hair, facial, open_mouth, sex, spread_legs, vaginal |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | fur_trim | black_gloves | elbow_gloves | short_dress | looking_at_viewer | blue_dress | white_cape | ice | medium_breasts | cleavage | clothing_cutout | cleavage_cutout | hat | navel | white_bikini | blue_bikini | official_alternate_costume | side-tie_bikini_bottom | white_headwear | blue_flower | hair_flower | holding | long_hair | long_sleeves | open_jacket | sidelocks | spoon | blush | nipples | cum_in_pussy | hetero | penis | 1boy | mosaic_censoring | cum_on_breasts | cum_on_hair | facial | open_mouth | sex | spread_legs | vaginal |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:---------------|:---------------|:--------------|:--------------------|:-------------|:-------------|:------|:-----------------|:-----------|:------------------|:------------------|:------|:--------|:---------------|:--------------|:-----------------------------|:-------------------------|:-----------------|:--------------|:--------------|:----------|:------------|:---------------|:--------------|:------------|:--------|:--------|:----------|:---------------|:---------|:--------|:-------|:-------------------|:-----------------|:--------------|:---------|:-------------|:------|:--------------|:----------|
| 0 | 32 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/nifl_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:48:27+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:07:28+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of nifl (Fire Emblem)
=============================
This is the dataset of nifl (Fire Emblem), containing 86 images and their tags.
The core tags of this character are 'blue\_hair, blue\_eyes, breasts, bangs, short\_hair, large\_breasts, hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
e756310f13c39c6285599da70bbdcafc50d5876a |
# Dataset of diadora (Fire Emblem)
This is the dataset of diadora (Fire Emblem), containing 69 images and their tags.
The core tags of this character are `long_hair, purple_eyes, purple_hair, breasts, light_purple_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 69 | 85.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 69 | 50.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 140 | 96.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 69 | 76.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 140 | 135.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/diadora_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, solo, smile, circlet, looking_at_viewer, cape, simple_background, upper_body, white_background, very_long_hair, white_dress |
| 1 | 14 |  |  |  |  |  | 1girl, blush, large_breasts, hetero, 1boy, nipples, penis, open_mouth, completely_nude, blue_hair, cum_in_pussy, hair_ornament, navel, solo_focus, female_pubic_hair, hair_between_eyes, heart, lying, sex, uncensored, vaginal |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | circlet | looking_at_viewer | cape | simple_background | upper_body | white_background | very_long_hair | white_dress | blush | large_breasts | hetero | 1boy | nipples | penis | open_mouth | completely_nude | blue_hair | cum_in_pussy | hair_ornament | navel | solo_focus | female_pubic_hair | hair_between_eyes | heart | lying | sex | uncensored | vaginal |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:----------|:--------------------|:-------|:--------------------|:-------------|:-------------------|:-----------------|:--------------|:--------|:----------------|:---------|:-------|:----------|:--------|:-------------|:------------------|:------------|:---------------|:----------------|:--------|:-------------|:--------------------|:--------------------|:--------|:--------|:------|:-------------|:----------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/diadora_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:48:39+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:10:06+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of diadora (Fire Emblem)
================================
This is the dataset of diadora (Fire Emblem), containing 69 images and their tags.
The core tags of this character are 'long\_hair, purple\_eyes, purple\_hair, breasts, light\_purple\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
f70966c5ca1491c91c137e05e4142eecfa83f508 |
# Dataset of ylgr (Fire Emblem)
This is the dataset of ylgr (Fire Emblem), containing 54 images and their tags.
The core tags of this character are `short_hair, blue_hair, gradient_hair, multicolored_hair, white_hair, purple_eyes, blonde_hair, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 54 | 46.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ylgr_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 54 | 31.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ylgr_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 97 | 54.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ylgr_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 54 | 42.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ylgr_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 97 | 74.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ylgr_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ylgr_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, hair_flower, hairband, open_mouth, one-piece_swimsuit, solo, smile, bangs, sandals, water, holding, breasts, bag, bare_shoulders, day, outdoors, sky, wading, white_flower |
| 1 | 10 |  |  |  |  |  | 1girl, dress, long_sleeves, tiara, open_mouth, solo, simple_background, cape, belt, smile, full_body |
| 2 | 7 |  |  |  |  |  | 1girl, simple_background, solo, tiara, upper_body, long_sleeves, open_mouth, smile, looking_at_viewer, white_background, cape |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hair_flower | hairband | open_mouth | one-piece_swimsuit | solo | smile | bangs | sandals | water | holding | breasts | bag | bare_shoulders | day | outdoors | sky | wading | white_flower | dress | long_sleeves | tiara | simple_background | cape | belt | full_body | upper_body | looking_at_viewer | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-----------|:-------------|:---------------------|:-------|:--------|:--------|:----------|:--------|:----------|:----------|:------|:-----------------|:------|:-----------|:------|:---------|:---------------|:--------|:---------------|:--------|:--------------------|:-------|:-------|:------------|:-------------|:--------------------|:-------------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | | | X | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | | | |
| 2 | 7 |  |  |  |  |  | X | | | X | | X | X | | | | | | | | | | | | | | X | X | X | X | | | X | X | X |
| CyberHarem/ylgr_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:48:40+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T20:59:08+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of ylgr (Fire Emblem)
=============================
This is the dataset of ylgr (Fire Emblem), containing 54 images and their tags.
The core tags of this character are 'short\_hair, blue\_hair, gradient\_hair, multicolored\_hair, white\_hair, purple\_eyes, blonde\_hair, hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1f061d4e6d9a4cbc9ea984c98f70b7accdc2544e |
# Dataset of altina (Fire Emblem)
This is the dataset of altina (Fire Emblem), containing 54 images and their tags.
The core tags of this character are `long_hair, blue_eyes, purple_hair, breasts, bangs, large_breasts, very_long_hair, blue_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 54 | 83.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 54 | 48.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 127 | 90.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 54 | 73.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 127 | 124.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/altina_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, armor, looking_at_viewer, solo, sword, dress, fingerless_gloves, holding, thighhighs, simple_background, white_background, boots, elbow_gloves |
| 1 | 5 |  |  |  |  |  | 1girl, bell, christmas, fur_trim, reindeer_antlers, solo, black_thighhighs, deer_ears, full_body, looking_at_viewer, candy_cane, fake_animal_ears, gift_box, holding_sword, medium_breasts, simple_background, smile, white_footwear, white_gloves, elbow_gloves, low-tied_long_hair, open_mouth, parted_lips |
| 2 | 17 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, navel, smile, hat, blush, abs, collarbone, muscular_female, one-piece_swimsuit, simple_background, white_background, bracelet |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armor | looking_at_viewer | solo | sword | dress | fingerless_gloves | holding | thighhighs | simple_background | white_background | boots | elbow_gloves | bell | christmas | fur_trim | reindeer_antlers | black_thighhighs | deer_ears | full_body | candy_cane | fake_animal_ears | gift_box | holding_sword | medium_breasts | smile | white_footwear | white_gloves | low-tied_long_hair | open_mouth | parted_lips | cleavage | navel | hat | blush | abs | collarbone | muscular_female | one-piece_swimsuit | bracelet |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:--------|:--------|:--------------------|:----------|:-------------|:--------------------|:-------------------|:--------|:---------------|:-------|:------------|:-----------|:-------------------|:-------------------|:------------|:------------|:-------------|:-------------------|:-----------|:----------------|:-----------------|:--------|:-----------------|:---------------|:---------------------|:-------------|:--------------|:-----------|:--------|:------|:--------|:------|:-------------|:------------------|:---------------------|:-----------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | X | X | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 2 | 17 |  |  |  |  |  | X | | X | X | | | | | | X | X | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X |
| CyberHarem/altina_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T20:48:52+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:01:22+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of altina (Fire Emblem)
===============================
This is the dataset of altina (Fire Emblem), containing 54 images and their tags.
The core tags of this character are 'long\_hair, blue\_eyes, purple\_hair, breasts, bangs, large\_breasts, very\_long\_hair, blue\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
6517407f60a878f2dbf2023e009093ec2f36b81d |
# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0](https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-Instruct-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-17T20:51:00.181001](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-Instruct-v1.0/blob/main/results_2024-01-17T20-51-00.181001.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6491690289461749,
"acc_stderr": 0.03225689205861614,
"acc_norm": 0.6482560818309024,
"acc_norm_stderr": 0.03293476544167865,
"mc1": 0.5789473684210527,
"mc1_stderr": 0.017283936248136476,
"mc2": 0.6961261081361256,
"mc2_stderr": 0.015300239859443631
},
"harness|arc:challenge|25": {
"acc": 0.7184300341296929,
"acc_stderr": 0.013143376735009022,
"acc_norm": 0.7406143344709898,
"acc_norm_stderr": 0.012808273573927106
},
"harness|hellaswag|10": {
"acc": 0.7194781915952997,
"acc_stderr": 0.004483360370140576,
"acc_norm": 0.8824935271858195,
"acc_norm_stderr": 0.0032136470410029485
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695238,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.027943219989337128,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.027943219989337128
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
"acc_stderr": 0.036563436533531585,
"acc_norm": 0.6416184971098265,
"acc_norm_stderr": 0.036563436533531585
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5872340425531914,
"acc_stderr": 0.03218471141400351,
"acc_norm": 0.5872340425531914,
"acc_norm_stderr": 0.03218471141400351
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370333,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370333
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.025467149045469546,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.025467149045469546
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7774193548387097,
"acc_stderr": 0.023664216671642518,
"acc_norm": 0.7774193548387097,
"acc_norm_stderr": 0.023664216671642518
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.47783251231527096,
"acc_stderr": 0.03514528562175008,
"acc_norm": 0.47783251231527096,
"acc_norm_stderr": 0.03514528562175008
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.02962022787479049,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.02962022787479049
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.02098685459328974,
"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.02098685459328974
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6641025641025641,
"acc_stderr": 0.023946724741563973,
"acc_norm": 0.6641025641025641,
"acc_norm_stderr": 0.023946724741563973
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34814814814814815,
"acc_stderr": 0.029045600290616255,
"acc_norm": 0.34814814814814815,
"acc_norm_stderr": 0.029045600290616255
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6554621848739496,
"acc_stderr": 0.03086868260412162,
"acc_norm": 0.6554621848739496,
"acc_norm_stderr": 0.03086868260412162
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
"acc_stderr": 0.03780445850526732,
"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526732
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8403669724770643,
"acc_stderr": 0.015703498348461766,
"acc_norm": 0.8403669724770643,
"acc_norm_stderr": 0.015703498348461766
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7848101265822784,
"acc_stderr": 0.02675082699467618,
"acc_norm": 0.7848101265822784,
"acc_norm_stderr": 0.02675082699467618
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6816143497757847,
"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7786259541984732,
"acc_stderr": 0.036412970813137296,
"acc_norm": 0.7786259541984732,
"acc_norm_stderr": 0.036412970813137296
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990946,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990946
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.046840993210771065,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.046840993210771065
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406974,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406974
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8199233716475096,
"acc_stderr": 0.013740797258579823,
"acc_norm": 0.8199233716475096,
"acc_norm_stderr": 0.013740797258579823
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7427745664739884,
"acc_stderr": 0.02353292543104429,
"acc_norm": 0.7427745664739884,
"acc_norm_stderr": 0.02353292543104429
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.423463687150838,
"acc_stderr": 0.0165254258987735,
"acc_norm": 0.423463687150838,
"acc_norm_stderr": 0.0165254258987735
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7124183006535948,
"acc_stderr": 0.02591780611714716,
"acc_norm": 0.7124183006535948,
"acc_norm_stderr": 0.02591780611714716
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7266881028938906,
"acc_stderr": 0.025311765975426122,
"acc_norm": 0.7266881028938906,
"acc_norm_stderr": 0.025311765975426122
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7438271604938271,
"acc_stderr": 0.0242885336377261,
"acc_norm": 0.7438271604938271,
"acc_norm_stderr": 0.0242885336377261
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.029790719243829727,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.029790719243829727
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46284224250325945,
"acc_stderr": 0.012734923579532067,
"acc_norm": 0.46284224250325945,
"acc_norm_stderr": 0.012734923579532067
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6691176470588235,
"acc_stderr": 0.028582709753898445,
"acc_norm": 0.6691176470588235,
"acc_norm_stderr": 0.028582709753898445
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6748366013071896,
"acc_stderr": 0.018950886770806315,
"acc_norm": 0.6748366013071896,
"acc_norm_stderr": 0.018950886770806315
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.028123429335142783,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.028123429335142783
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454115,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454115
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.82,
"acc_stderr": 0.038612291966536955,
"acc_norm": 0.82,
"acc_norm_stderr": 0.038612291966536955
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.02991312723236804,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.02991312723236804
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5789473684210527,
"mc1_stderr": 0.017283936248136476,
"mc2": 0.6961261081361256,
"mc2_stderr": 0.015300239859443631
},
"harness|winogrande|5": {
"acc": 0.8429360694554064,
"acc_stderr": 0.010226303949598484
},
"harness|gsm8k|5": {
"acc": 0.6944655041698257,
"acc_stderr": 0.012688134076726879
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-Instruct-v1.0 | [
"region:us"
] | 2024-01-17T20:53:14+00:00 | {"pretty_name": "Evaluation run of zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0](https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-Instruct-v1.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T20:51:00.181001](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-Instruct-v1.0/blob/main/results_2024-01-17T20-51-00.181001.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6491690289461749,\n \"acc_stderr\": 0.03225689205861614,\n \"acc_norm\": 0.6482560818309024,\n \"acc_norm_stderr\": 0.03293476544167865,\n \"mc1\": 0.5789473684210527,\n \"mc1_stderr\": 0.017283936248136476,\n \"mc2\": 0.6961261081361256,\n \"mc2_stderr\": 0.015300239859443631\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7184300341296929,\n \"acc_stderr\": 0.013143376735009022,\n \"acc_norm\": 0.7406143344709898,\n \"acc_norm_stderr\": 0.012808273573927106\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7194781915952997,\n \"acc_stderr\": 0.004483360370140576,\n \"acc_norm\": 0.8824935271858195,\n \"acc_norm_stderr\": 0.0032136470410029485\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337128,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337128\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.025467149045469546,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.025467149045469546\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479049,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479049\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328974,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328974\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.03086868260412162,\n \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.03086868260412162\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461766,\n \"acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461766\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467618,\n \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467618\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137296,\n \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137296\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n \"acc_stderr\": 0.013740797258579823,\n \"acc_norm\": 0.8199233716475096,\n \"acc_norm_stderr\": 0.013740797258579823\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.02353292543104429,\n \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.02353292543104429\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.423463687150838,\n \"acc_stderr\": 0.0165254258987735,\n \"acc_norm\": 0.423463687150838,\n \"acc_norm_stderr\": 0.0165254258987735\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.0242885336377261,\n \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.0242885336377261\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46284224250325945,\n \"acc_stderr\": 0.012734923579532067,\n \"acc_norm\": 0.46284224250325945,\n \"acc_norm_stderr\": 0.012734923579532067\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898445,\n \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898445\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5789473684210527,\n \"mc1_stderr\": 0.017283936248136476,\n \"mc2\": 0.6961261081361256,\n \"mc2_stderr\": 0.015300239859443631\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598484\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6944655041698257,\n \"acc_stderr\": 0.012688134076726879\n }\n}\n```", "repo_url": "https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|arc:challenge|25_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|gsm8k|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hellaswag|10_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T20-51-00.181001.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_abstract_algebra_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_anatomy_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_astronomy_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_business_ethics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_computer_science_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_mathematics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_medicine_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_physics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_computer_security_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_conceptual_physics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_econometrics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_electrical_engineering_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_elementary_mathematics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_formal_logic_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_global_facts_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_european_history_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_geography_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_government_and_politics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_macroeconomics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_mathematics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_microeconomics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_physics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_psychology_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_statistics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_us_history_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["**/details_harness|winogrande|5_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-17T20-51-00.181001.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_17T20_51_00.181001", "path": ["results_2024-01-17T20-51-00.181001.parquet"]}, {"split": "latest", "path": ["results_2024-01-17T20-51-00.181001.parquet"]}]}]} | 2024-01-17T20:53:37+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0
Dataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-17T20:51:00.181001(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T20:51:00.181001(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-Instruct-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T20:51:00.181001(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
259908157980e1c34455d7f0f2e3d1bbba89237a |
# Dataset Card for Evaluation run of wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1](https://huggingface.co/wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_wang7776__Mistral-7B-Instruct-v0.2-sparsity-30-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-17T20:56:09.604059](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Mistral-7B-Instruct-v0.2-sparsity-30-v0.1/blob/main/results_2024-01-17T20-56-09.604059.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6024881396252101,
"acc_stderr": 0.03335765627539204,
"acc_norm": 0.6070050987236348,
"acc_norm_stderr": 0.03403883355182919,
"mc1": 0.5079559363525091,
"mc1_stderr": 0.017501285074551825,
"mc2": 0.6627552049915408,
"mc2_stderr": 0.015444533101130177
},
"harness|arc:challenge|25": {
"acc": 0.5844709897610921,
"acc_stderr": 0.014401366641216384,
"acc_norm": 0.6331058020477816,
"acc_norm_stderr": 0.0140841331181043
},
"harness|hellaswag|10": {
"acc": 0.6559450308703445,
"acc_stderr": 0.004740882120999965,
"acc_norm": 0.8436566421031667,
"acc_norm_stderr": 0.0036243831208234508
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.618421052631579,
"acc_stderr": 0.03953173377749194,
"acc_norm": 0.618421052631579,
"acc_norm_stderr": 0.03953173377749194
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.03942082639927213,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.03942082639927213
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5491329479768786,
"acc_stderr": 0.037940126746970296,
"acc_norm": 0.5491329479768786,
"acc_norm_stderr": 0.037940126746970296
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.45098039215686275,
"acc_stderr": 0.04951218252396262,
"acc_norm": 0.45098039215686275,
"acc_norm_stderr": 0.04951218252396262
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.548936170212766,
"acc_stderr": 0.03252909619613197,
"acc_norm": 0.548936170212766,
"acc_norm_stderr": 0.03252909619613197
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3684210526315789,
"acc_stderr": 0.04537815354939391,
"acc_norm": 0.3684210526315789,
"acc_norm_stderr": 0.04537815354939391
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.041227371113703316,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.041227371113703316
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3835978835978836,
"acc_stderr": 0.025043757318520196,
"acc_norm": 0.3835978835978836,
"acc_norm_stderr": 0.025043757318520196
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6709677419354839,
"acc_stderr": 0.02672949906834996,
"acc_norm": 0.6709677419354839,
"acc_norm_stderr": 0.02672949906834996
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.03477691162163659,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.03477691162163659
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7676767676767676,
"acc_stderr": 0.030088629490217487,
"acc_norm": 0.7676767676767676,
"acc_norm_stderr": 0.030088629490217487
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8341968911917098,
"acc_stderr": 0.026839845022314415,
"acc_norm": 0.8341968911917098,
"acc_norm_stderr": 0.026839845022314415
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5615384615384615,
"acc_stderr": 0.025158266016868578,
"acc_norm": 0.5615384615384615,
"acc_norm_stderr": 0.025158266016868578
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.02857834836547308,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.02857834836547308
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6134453781512605,
"acc_stderr": 0.03163145807552378,
"acc_norm": 0.6134453781512605,
"acc_norm_stderr": 0.03163145807552378
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.03943966699183629,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7853211009174312,
"acc_stderr": 0.01760430414925648,
"acc_norm": 0.7853211009174312,
"acc_norm_stderr": 0.01760430414925648
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.46296296296296297,
"acc_stderr": 0.03400603625538271,
"acc_norm": 0.46296296296296297,
"acc_norm_stderr": 0.03400603625538271
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7401960784313726,
"acc_stderr": 0.03077855467869326,
"acc_norm": 0.7401960784313726,
"acc_norm_stderr": 0.03077855467869326
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7510548523206751,
"acc_stderr": 0.028146970599422644,
"acc_norm": 0.7510548523206751,
"acc_norm_stderr": 0.028146970599422644
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6636771300448431,
"acc_stderr": 0.031708824268455005,
"acc_norm": 0.6636771300448431,
"acc_norm_stderr": 0.031708824268455005
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6870229007633588,
"acc_stderr": 0.04066962905677698,
"acc_norm": 0.6870229007633588,
"acc_norm_stderr": 0.04066962905677698
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.036401182719909476,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.036401182719909476
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.04330043749650743,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.04330043749650743
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.6893203883495146,
"acc_stderr": 0.04582124160161549,
"acc_norm": 0.6893203883495146,
"acc_norm_stderr": 0.04582124160161549
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8504273504273504,
"acc_stderr": 0.023365051491753715,
"acc_norm": 0.8504273504273504,
"acc_norm_stderr": 0.023365051491753715
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7879948914431673,
"acc_stderr": 0.01461609938583367,
"acc_norm": 0.7879948914431673,
"acc_norm_stderr": 0.01461609938583367
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.02507071371915319,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.02507071371915319
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3396648044692737,
"acc_stderr": 0.015839400406212494,
"acc_norm": 0.3396648044692737,
"acc_norm_stderr": 0.015839400406212494
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6633986928104575,
"acc_stderr": 0.027057974624494382,
"acc_norm": 0.6633986928104575,
"acc_norm_stderr": 0.027057974624494382
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6977491961414791,
"acc_stderr": 0.02608270069539966,
"acc_norm": 0.6977491961414791,
"acc_norm_stderr": 0.02608270069539966
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6728395061728395,
"acc_stderr": 0.026105673861409825,
"acc_norm": 0.6728395061728395,
"acc_norm_stderr": 0.026105673861409825
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.029658235097666904,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.029658235097666904
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4106910039113429,
"acc_stderr": 0.01256487154253435,
"acc_norm": 0.4106910039113429,
"acc_norm_stderr": 0.01256487154253435
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6102941176470589,
"acc_stderr": 0.0296246635811597,
"acc_norm": 0.6102941176470589,
"acc_norm_stderr": 0.0296246635811597
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6209150326797386,
"acc_stderr": 0.019627444748412232,
"acc_norm": 0.6209150326797386,
"acc_norm_stderr": 0.019627444748412232
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7,
"acc_stderr": 0.04389311454644287,
"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644287
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.0289205832206756,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.0289205832206756
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.746268656716418,
"acc_stderr": 0.03076944496729602,
"acc_norm": 0.746268656716418,
"acc_norm_stderr": 0.03076944496729602
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.81,
"acc_stderr": 0.03942772444036625,
"acc_norm": 0.81,
"acc_norm_stderr": 0.03942772444036625
},
"harness|hendrycksTest-virology|5": {
"acc": 0.45180722891566266,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.45180722891566266,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.030611116557432528,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.030611116557432528
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5079559363525091,
"mc1_stderr": 0.017501285074551825,
"mc2": 0.6627552049915408,
"mc2_stderr": 0.015444533101130177
},
"harness|winogrande|5": {
"acc": 0.7805840568271507,
"acc_stderr": 0.01163126836060778
},
"harness|gsm8k|5": {
"acc": 0.39423805913570886,
"acc_stderr": 0.013460852357095656
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_wang7776__Mistral-7B-Instruct-v0.2-sparsity-30-v0.1 | [
"region:us"
] | 2024-01-17T20:58:25+00:00 | {"pretty_name": "Evaluation run of wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1](https://huggingface.co/wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wang7776__Mistral-7B-Instruct-v0.2-sparsity-30-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T20:56:09.604059](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Mistral-7B-Instruct-v0.2-sparsity-30-v0.1/blob/main/results_2024-01-17T20-56-09.604059.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6024881396252101,\n \"acc_stderr\": 0.03335765627539204,\n \"acc_norm\": 0.6070050987236348,\n \"acc_norm_stderr\": 0.03403883355182919,\n \"mc1\": 0.5079559363525091,\n \"mc1_stderr\": 0.017501285074551825,\n \"mc2\": 0.6627552049915408,\n \"mc2_stderr\": 0.015444533101130177\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5844709897610921,\n \"acc_stderr\": 0.014401366641216384,\n \"acc_norm\": 0.6331058020477816,\n \"acc_norm_stderr\": 0.0140841331181043\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6559450308703445,\n \"acc_stderr\": 0.004740882120999965,\n \"acc_norm\": 0.8436566421031667,\n \"acc_norm_stderr\": 0.0036243831208234508\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5491329479768786,\n \"acc_stderr\": 0.037940126746970296,\n \"acc_norm\": 0.5491329479768786,\n \"acc_norm_stderr\": 0.037940126746970296\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396262,\n \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396262\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.03252909619613197,\n \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.03252909619613197\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n \"acc_stderr\": 0.04537815354939391,\n \"acc_norm\": 0.3684210526315789,\n \"acc_norm_stderr\": 0.04537815354939391\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3835978835978836,\n \"acc_stderr\": 0.025043757318520196,\n \"acc_norm\": 0.3835978835978836,\n \"acc_norm_stderr\": 0.025043757318520196\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6709677419354839,\n \"acc_stderr\": 0.02672949906834996,\n \"acc_norm\": 0.6709677419354839,\n \"acc_norm_stderr\": 0.02672949906834996\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.03477691162163659,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03477691162163659\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5615384615384615,\n \"acc_stderr\": 0.025158266016868578,\n \"acc_norm\": 0.5615384615384615,\n \"acc_norm_stderr\": 0.025158266016868578\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6134453781512605,\n \"acc_stderr\": 0.03163145807552378,\n \"acc_norm\": 0.6134453781512605,\n \"acc_norm_stderr\": 0.03163145807552378\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7853211009174312,\n \"acc_stderr\": 0.01760430414925648,\n \"acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.01760430414925648\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538271,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538271\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7401960784313726,\n \"acc_stderr\": 0.03077855467869326,\n \"acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.03077855467869326\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n \"acc_stderr\": 0.031708824268455005,\n \"acc_norm\": 0.6636771300448431,\n \"acc_norm_stderr\": 0.031708824268455005\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6870229007633588,\n \"acc_stderr\": 0.04066962905677698,\n \"acc_norm\": 0.6870229007633588,\n \"acc_norm_stderr\": 0.04066962905677698\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.036401182719909476,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.036401182719909476\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.04330043749650743,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.04330043749650743\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6893203883495146,\n \"acc_stderr\": 0.04582124160161549,\n \"acc_norm\": 0.6893203883495146,\n \"acc_norm_stderr\": 0.04582124160161549\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7879948914431673,\n \"acc_stderr\": 0.01461609938583367,\n \"acc_norm\": 0.7879948914431673,\n \"acc_norm_stderr\": 0.01461609938583367\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.02507071371915319,\n \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.02507071371915319\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3396648044692737,\n \"acc_stderr\": 0.015839400406212494,\n \"acc_norm\": 0.3396648044692737,\n \"acc_norm_stderr\": 0.015839400406212494\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6633986928104575,\n \"acc_stderr\": 0.027057974624494382,\n \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.027057974624494382\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6728395061728395,\n \"acc_stderr\": 0.026105673861409825,\n \"acc_norm\": 0.6728395061728395,\n \"acc_norm_stderr\": 0.026105673861409825\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666904,\n \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666904\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4106910039113429,\n \"acc_stderr\": 0.01256487154253435,\n \"acc_norm\": 0.4106910039113429,\n \"acc_norm_stderr\": 0.01256487154253435\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6102941176470589,\n \"acc_stderr\": 0.0296246635811597,\n \"acc_norm\": 0.6102941176470589,\n \"acc_norm_stderr\": 0.0296246635811597\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6209150326797386,\n \"acc_stderr\": 0.019627444748412232,\n \"acc_norm\": 0.6209150326797386,\n \"acc_norm_stderr\": 0.019627444748412232\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.746268656716418,\n \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.746268656716418,\n \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5079559363525091,\n \"mc1_stderr\": 0.017501285074551825,\n \"mc2\": 0.6627552049915408,\n \"mc2_stderr\": 0.015444533101130177\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7805840568271507,\n \"acc_stderr\": 0.01163126836060778\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.39423805913570886,\n \"acc_stderr\": 0.013460852357095656\n }\n}\n```", "repo_url": "https://huggingface.co/wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|arc:challenge|25_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|gsm8k|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hellaswag|10_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-management|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-virology|5_2024-01-17T20-56-09.604059.parquet", "**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_abstract_algebra_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_anatomy_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-anatomy|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_astronomy_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-astronomy|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_business_ethics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_biology|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_computer_science_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_mathematics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_medicine_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_physics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_physics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_computer_security_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-computer_security|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_conceptual_physics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_econometrics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-econometrics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_electrical_engineering_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_elementary_mathematics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_formal_logic_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_global_facts_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_european_history_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_geography_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_government_and_politics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_macroeconomics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_mathematics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_microeconomics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_physics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_psychology_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_statistics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_us_history_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["**/details_harness|winogrande|5_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-17T20-56-09.604059.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_17T20_56_09.604059", "path": ["results_2024-01-17T20-56-09.604059.parquet"]}, {"split": "latest", "path": ["results_2024-01-17T20-56-09.604059.parquet"]}]}]} | 2024-01-17T20:58:50+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1
Dataset automatically created during the evaluation run of model wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-17T20:56:09.604059(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1\n\n\n\nDataset automatically created during the evaluation run of model wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T20:56:09.604059(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1\n\n\n\nDataset automatically created during the evaluation run of model wang7776/Mistral-7B-Instruct-v0.2-sparsity-30-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-17T20:56:09.604059(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
5ca2cbbd275244624daaefcf51a9441529495e4b |
# Dataset of lilith (Fire Emblem)
This is the dataset of lilith (Fire Emblem), containing 87 images and their tags.
The core tags of this character are `blue_hair, long_hair, braid, yellow_eyes, multicolored_hair, single_braid, breasts, gradient_hair, red_hair, two-tone_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 87 | 79.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lilith_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 87 | 48.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lilith_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 201 | 98.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lilith_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 87 | 70.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lilith_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 201 | 128.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lilith_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/lilith_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, solo, apron, smile, facial_mark, simple_background, hair_over_shoulder, white_background, dress, looking_at_viewer, maid |
| 1 | 8 |  |  |  |  |  | 1girl, apron, bangs, knee_boots, long_sleeves, puffy_sleeves, solo, white_pantyhose, blue_dress, full_body, shiny_hair, forehead_jewel, gradient, pale_skin, hat, transparent_background, closed_mouth, looking_at_viewer, medium_breasts, smile, dark_aura |
| 2 | 6 |  |  |  |  |  | 1girl, hetero, penis, sex, 1boy, ass, blush, open_mouth, testicles, vaginal, barefoot, cum_in_pussy, nipples, nude, solo_focus, uncensored, large_breasts, looking_back, smile |
| 3 | 7 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, 1boy, penis, forehead_jewel, nipples, open_mouth, sweat, ass, facial_mark, oral, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | apron | smile | facial_mark | simple_background | hair_over_shoulder | white_background | dress | looking_at_viewer | maid | bangs | knee_boots | long_sleeves | puffy_sleeves | white_pantyhose | blue_dress | full_body | shiny_hair | forehead_jewel | gradient | pale_skin | hat | transparent_background | closed_mouth | medium_breasts | dark_aura | hetero | penis | sex | 1boy | ass | blush | open_mouth | testicles | vaginal | barefoot | cum_in_pussy | nipples | nude | solo_focus | uncensored | large_breasts | looking_back | sweat | oral |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------|:--------------|:--------------------|:---------------------|:-------------------|:--------|:--------------------|:-------|:--------|:-------------|:---------------|:----------------|:------------------|:-------------|:------------|:-------------|:-----------------|:-----------|:------------|:------|:-------------------------|:---------------|:-----------------|:------------|:---------|:--------|:------|:-------|:------|:--------|:-------------|:------------|:----------|:-----------|:---------------|:----------|:-------|:-------------|:-------------|:----------------|:---------------|:--------|:-------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | |
| 3 | 7 |  |  |  |  |  | X | | | | X | | | | | | | | | | | | | | | X | | | | | | | | X | X | | X | X | X | X | | | | | X | | X | X | | | X | X |
| CyberHarem/lilith_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:04:26+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:23:58+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of lilith (Fire Emblem)
===============================
This is the dataset of lilith (Fire Emblem), containing 87 images and their tags.
The core tags of this character are 'blue\_hair, long\_hair, braid, yellow\_eyes, multicolored\_hair, single\_braid, breasts, gradient\_hair, red\_hair, two-tone\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
f5f51a4980cd217ce82021f546754d3f87639e3c |
# Dataset of karla (Fire Emblem)
This is the dataset of karla (Fire Emblem), containing 43 images and their tags.
The core tags of this character are `black_hair, long_hair, breasts, black_eyes, large_breasts, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 43 | 58.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karla_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 43 | 31.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karla_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 81 | 57.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karla_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 43 | 50.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karla_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 81 | 82.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karla_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/karla_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, solo, cleavage, dress, hair_ornament, simple_background, full_body, white_background, holding_weapon, looking_at_viewer, bare_shoulders, pantyhose, see-through, spear, very_long_hair, standing, flower, jewelry, strapless, toeless_footwear |
| 1 | 19 |  |  |  |  |  | 1girl, solo, holding_sword, boots, dress, katana, simple_background, sash, full_body, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | dress | hair_ornament | simple_background | full_body | white_background | holding_weapon | looking_at_viewer | bare_shoulders | pantyhose | see-through | spear | very_long_hair | standing | flower | jewelry | strapless | toeless_footwear | holding_sword | boots | katana | sash |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:--------|:----------------|:--------------------|:------------|:-------------------|:-----------------|:--------------------|:-----------------|:------------|:--------------|:--------|:-----------------|:-----------|:---------|:----------|:------------|:-------------------|:----------------|:--------|:---------|:-------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | |
| 1 | 19 |  |  |  |  |  | X | X | | X | | X | X | X | | | | | | | | | | | | | X | X | X | X |
| CyberHarem/karla_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:04:26+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:17:25+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of karla (Fire Emblem)
==============================
This is the dataset of karla (Fire Emblem), containing 43 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, breasts, black\_eyes, large\_breasts, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
ea806548b70a32c9ae8572278a4d98289c256b3e |
# Dataset of nagi (Fire Emblem)
This is the dataset of nagi (Fire Emblem), containing 33 images and their tags.
The core tags of this character are `green_hair, long_hair, green_eyes, pointy_ears, breasts, very_long_hair, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 33 | 43.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagi_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 33 | 25.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagi_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 65 | 43.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagi_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 33 | 38.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagi_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 65 | 58.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagi_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nagi_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, solo, tiara, thighhighs, detached_sleeves, smile, pink_dress, thigh_boots, looking_at_viewer, white_background, full_body |
| 1 | 6 |  |  |  |  |  | blush, hetero, nipples, tiara, 1boy, nude, penis, 1girl, cum_on_breasts, open_mouth, ejaculation, facial, group_sex, paizuri, smile, solo_focus, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | tiara | thighhighs | detached_sleeves | smile | pink_dress | thigh_boots | looking_at_viewer | white_background | full_body | blush | hetero | nipples | 1boy | nude | penis | cum_on_breasts | open_mouth | ejaculation | facial | group_sex | paizuri | solo_focus | uncensored |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-------------|:-------------------|:--------|:-------------|:--------------|:--------------------|:-------------------|:------------|:--------|:---------|:----------|:-------|:-------|:--------|:-----------------|:-------------|:--------------|:---------|:------------|:----------|:-------------|:-------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/nagi_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:04:39+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:10:13+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of nagi (Fire Emblem)
=============================
This is the dataset of nagi (Fire Emblem), containing 33 images and their tags.
The core tags of this character are 'green\_hair, long\_hair, green\_eyes, pointy\_ears, breasts, very\_long\_hair, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
dc291a28b3e95653bc7f3ff6213325bfe2e4898d |
# Dataset of triandra (Fire Emblem)
This is the dataset of triandra (Fire Emblem), containing 88 images and their tags.
The core tags of this character are `blue_eyes, breasts, wings, hair_ornament, hair_over_one_eye, facial_mark, butterfly_wings, hair_flower, fairy_wings, purple_hair, large_breasts, medium_breasts, medium_hair, red_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 88 | 151.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/triandra_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 88 | 79.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/triandra_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 218 | 172.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/triandra_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 88 | 128.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/triandra_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 218 | 244.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/triandra_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/triandra_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, bridal_gauntlets, cleavage_cutout, full_body, gradient_clothes, rose, simple_background, solo, thorns, bangs, black_footwear, center_opening, covered_navel, floating_object, shiny_hair, gradient_background, looking_at_viewer, open_mouth, sleeveless_dress, white_background, detached_sleeves, fairy, grey_background, knee_boots, long_hair, petals, shiny_skin, thighs, torn_clothes |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, midriff, navel, solo, thorns, vines, pink_hair, butterfly, detached_sleeves, parted_lips, thighs, alternate_costume, center_opening, cleavage_cutout, fairy, revealing_clothes, rose |
| 2 | 5 |  |  |  |  |  | 1girl, blush, open_mouth, bare_shoulders, vaginal, 1boy, cleavage, hetero, mosaic_censoring, penis, pussy_juice, spread_legs, sweat, thorns, vines, clothed_sex, detached_sleeves, long_hair, looking_at_viewer, navel, nipples, rose, saliva, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_dress | bridal_gauntlets | cleavage_cutout | full_body | gradient_clothes | rose | simple_background | solo | thorns | bangs | black_footwear | center_opening | covered_navel | floating_object | shiny_hair | gradient_background | looking_at_viewer | open_mouth | sleeveless_dress | white_background | detached_sleeves | fairy | grey_background | knee_boots | long_hair | petals | shiny_skin | thighs | torn_clothes | midriff | navel | vines | pink_hair | butterfly | parted_lips | alternate_costume | revealing_clothes | blush | vaginal | 1boy | cleavage | hetero | mosaic_censoring | penis | pussy_juice | spread_legs | sweat | clothed_sex | nipples | saliva | solo_focus |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------|:-------------------|:------------------|:------------|:-------------------|:-------|:--------------------|:-------|:---------|:--------|:-----------------|:-----------------|:----------------|:------------------|:-------------|:----------------------|:--------------------|:-------------|:-------------------|:-------------------|:-------------------|:--------|:------------------|:-------------|:------------|:---------|:-------------|:---------|:---------------|:----------|:--------|:--------|:------------|:------------|:--------------|:--------------------|:--------------------|:--------|:----------|:-------|:-----------|:---------|:-------------------|:--------|:--------------|:--------------|:--------|:--------------|:----------|:---------|:-------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | | | X | | | X | | X | X | | | X | | | | | X | | | | X | X | | | | | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | | | X | | | X | | | | | | | | X | X | | | X | | | | X | | | | | | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/triandra_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:04:44+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:24:37+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of triandra (Fire Emblem)
=================================
This is the dataset of triandra (Fire Emblem), containing 88 images and their tags.
The core tags of this character are 'blue\_eyes, breasts, wings, hair\_ornament, hair\_over\_one\_eye, facial\_mark, butterfly\_wings, hair\_flower, fairy\_wings, purple\_hair, large\_breasts, medium\_breasts, medium\_hair, red\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
b3d631977691cd10439296ece2decee60c178992 | # Dataset Card for "cai-conversation-dev1705525723"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vwxyzjn/cai-conversation-dev1705525723 | [
"region:us"
] | 2024-01-17T21:11:47+00:00 | {"dataset_info": {"features": [{"name": "init_prompt", "dtype": "string"}, {"name": "init_response", "dtype": "string"}, {"name": "critic_prompt", "dtype": "string"}, {"name": "critic_response", "dtype": "string"}, {"name": "revision_prompt", "dtype": "string"}, {"name": "revision_response", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "messages", "sequence": "string"}, {"name": "chosen", "sequence": "string"}, {"name": "rejected", "sequence": "string"}], "splits": [{"name": "train_sft", "num_bytes": 187558, "num_examples": 64}, {"name": "train_prefs", "num_bytes": 186315, "num_examples": 64}, {"name": "test_sft", "num_bytes": 186016, "num_examples": 64}, {"name": "test_prefs", "num_bytes": 195711, "num_examples": 64}], "download_size": 419031, "dataset_size": 755600}, "configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-*"}, {"split": "train_prefs", "path": "data/train_prefs-*"}, {"split": "test_sft", "path": "data/test_sft-*"}, {"split": "test_prefs", "path": "data/test_prefs-*"}]}]} | 2024-01-17T21:11:51+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "cai-conversation-dev1705525723"
More Information needed | [
"# Dataset Card for \"cai-conversation-dev1705525723\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"cai-conversation-dev1705525723\"\n\nMore Information needed"
] |
6d547acb041e03fff470c26c3f0db7078b3e8d28 | # Dataset Card for "cai-conversation-dev1705526305"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vwxyzjn/cai-conversation-dev1705526305 | [
"region:us"
] | 2024-01-17T21:21:28+00:00 | {"dataset_info": {"features": [{"name": "init_prompt", "dtype": "string"}, {"name": "init_response", "dtype": "string"}, {"name": "critic_prompt", "dtype": "string"}, {"name": "critic_response", "dtype": "string"}, {"name": "revision_prompt", "dtype": "string"}, {"name": "revision_response", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "messages", "sequence": "string"}, {"name": "chosen", "sequence": "string"}, {"name": "rejected", "sequence": "string"}], "splits": [{"name": "train_sft", "num_bytes": 193601, "num_examples": 64}, {"name": "train_prefs", "num_bytes": 187186, "num_examples": 64}, {"name": "test_sft", "num_bytes": 187464, "num_examples": 64}, {"name": "test_prefs", "num_bytes": 197351, "num_examples": 64}], "download_size": 421041, "dataset_size": 765602}, "configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-*"}, {"split": "train_prefs", "path": "data/train_prefs-*"}, {"split": "test_sft", "path": "data/test_sft-*"}, {"split": "test_prefs", "path": "data/test_prefs-*"}]}]} | 2024-01-17T21:21:49+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "cai-conversation-dev1705526305"
More Information needed | [
"# Dataset Card for \"cai-conversation-dev1705526305\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"cai-conversation-dev1705526305\"\n\nMore Information needed"
] |
4e9b00711e6285ba9b8f333dd73aa15606b9656a |
# Dataset of lyre (Fire Emblem)
This is the dataset of lyre (Fire Emblem), containing 31 images and their tags.
The core tags of this character are `animal_ears, cat_ears, cat_girl, purple_eyes, orange_hair, tail, cat_tail, braid, bangs, facial_mark, breasts, long_hair, fang`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 31 | 50.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyre_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 31 | 30.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyre_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 67 | 57.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyre_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 31 | 44.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyre_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 67 | 83.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyre_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/lyre_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, open_mouth, blush, kimono, smile, hair_ornament, whisker_markings, 1boy, bracelet, hetero, large_breasts, looking_at_viewer, mosaic_censoring, nipples, obi, penis, shimenawa, solo_focus |
| 1 | 19 |  |  |  |  |  | 1girl, choker, whisker_markings, brown_belt, side_slit_shorts, smile, open_mouth, simple_background, solo, thighhighs, 2girls, blush, collarbone, gloves, white_background, detached_sleeves, looking_at_viewer, single_braid |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | open_mouth | blush | kimono | smile | hair_ornament | whisker_markings | 1boy | bracelet | hetero | large_breasts | looking_at_viewer | mosaic_censoring | nipples | obi | penis | shimenawa | solo_focus | choker | brown_belt | side_slit_shorts | simple_background | solo | thighhighs | 2girls | collarbone | gloves | white_background | detached_sleeves | single_braid |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------|:---------|:--------|:----------------|:-------------------|:-------|:-----------|:---------|:----------------|:--------------------|:-------------------|:----------|:------|:--------|:------------|:-------------|:---------|:-------------|:-------------------|:--------------------|:-------|:-------------|:---------|:-------------|:---------|:-------------------|:-------------------|:---------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 1 | 19 |  |  |  |  |  | X | X | X | | X | | X | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/lyre_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:25:55+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:33:02+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of lyre (Fire Emblem)
=============================
This is the dataset of lyre (Fire Emblem), containing 31 images and their tags.
The core tags of this character are 'animal\_ears, cat\_ears, cat\_girl, purple\_eyes, orange\_hair, tail, cat\_tail, braid, bangs, facial\_mark, breasts, long\_hair, fang', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
7c6d6ea3ea09a8cfb8a697820d377ee0edd16f19 |
# Dataset of efi (Fire Emblem)
This is the dataset of efi (Fire Emblem), containing 182 images and their tags.
The core tags of this character are `long_hair, braid, brown_eyes, twin_braids, blonde_hair, bow, breasts, brown_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 182 | 197.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/efi_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 182 | 121.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/efi_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 428 | 246.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/efi_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 182 | 177.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/efi_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 428 | 331.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/efi_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/efi_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | apron, dress, 1girl, open_mouth, simple_background, smile, solo, blush, short_sleeves, bracelet, capelet, hair_bow, white_background |
| 1 | 7 |  |  |  |  |  | 1girl, upper_body, dress, hat, open_mouth, smile, solo, hair_flower, holding_flower, simple_background, white_background |
| 2 | 13 |  |  |  |  |  | nipples, blush, 1boy, 1girl, hetero, penis, mosaic_censoring, open_mouth, solo_focus, large_breasts, sex, vaginal, cum_in_pussy, navel, completely_nude, medium_breasts, sweat |
| 3 | 5 |  |  |  |  |  | 1girl, blush, medium_breasts, nipples, pussy, solo, looking_at_viewer, anus, completely_nude, navel, on_back, open_mouth, smile, ass, bangs, closed_mouth, english_text, large_breasts, pillow, spread_legs, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | apron | dress | 1girl | open_mouth | simple_background | smile | solo | blush | short_sleeves | bracelet | capelet | hair_bow | white_background | upper_body | hat | hair_flower | holding_flower | nipples | 1boy | hetero | penis | mosaic_censoring | solo_focus | large_breasts | sex | vaginal | cum_in_pussy | navel | completely_nude | medium_breasts | sweat | pussy | looking_at_viewer | anus | on_back | ass | bangs | closed_mouth | english_text | pillow | spread_legs | uncensored |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------|:-------------|:--------------------|:--------|:-------|:--------|:----------------|:-----------|:----------|:-----------|:-------------------|:-------------|:------|:--------------|:-----------------|:----------|:-------|:---------|:--------|:-------------------|:-------------|:----------------|:------|:----------|:---------------|:--------|:------------------|:-----------------|:--------|:--------|:--------------------|:-------|:----------|:------|:--------|:---------------|:---------------|:---------|:--------------|:-------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | | X | X | X | X | X | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | | | X | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | | | X | X | | X | X | X | | | | | | | | | | X | | | | | | X | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/efi_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:26:02+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:06:54+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of efi (Fire Emblem)
============================
This is the dataset of efi (Fire Emblem), containing 182 images and their tags.
The core tags of this character are 'long\_hair, braid, brown\_eyes, twin\_braids, blonde\_hair, bow, breasts, brown\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
ef7d08a6d219d3e08494e5de1c373c1b5df45b34 |
# Dataset of florina (Fire Emblem)
This is the dataset of florina (Fire Emblem), containing 223 images and their tags.
The core tags of this character are `long_hair, purple_hair, green_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 223 | 234.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florina_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 223 | 157.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florina_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 449 | 294.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florina_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 223 | 217.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florina_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 449 | 379.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florina_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/florina_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, breastplate, fingerless_gloves, solo, circlet, thighhighs, zettai_ryouiki, belt, elbow_gloves, pegasus_knight_uniform_(fire_emblem), simple_background, white_background, pauldrons, smile, thigh_boots, white_dress, looking_at_viewer, short_dress |
| 1 | 5 |  |  |  |  |  | 1girl, circlet, looking_at_viewer, simple_background, solo, upper_body, blush, breastplate, gloves, parted_bangs, pauldrons, white_background, belt, closed_mouth, dress, smile, short_sleeves |
| 2 | 8 |  |  |  |  |  | 1girl, armor, circlet, fingerless_gloves, 1boy, blush, green_hair, light_purple_hair, open_mouth, pegasus_knight_uniform_(fire_emblem), smile, cape, closed_eyes |
| 3 | 6 |  |  |  |  |  | 1girl, hetero, penis, sex, vaginal, 1boy, blush, cum_in_pussy, on_back, open_mouth, solo_focus, gloves, thighhighs, bar_censor, breastplate, medium_breasts, nipples, spread_legs |
| 4 | 6 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, solo_focus, sweat, nude, blue_eyes, blush, medium_breasts, sex, cum |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | breastplate | fingerless_gloves | solo | circlet | thighhighs | zettai_ryouiki | belt | elbow_gloves | pegasus_knight_uniform_(fire_emblem) | simple_background | white_background | pauldrons | smile | thigh_boots | white_dress | looking_at_viewer | short_dress | upper_body | blush | gloves | parted_bangs | closed_mouth | dress | short_sleeves | armor | 1boy | green_hair | light_purple_hair | open_mouth | cape | closed_eyes | hetero | penis | sex | vaginal | cum_in_pussy | on_back | solo_focus | bar_censor | medium_breasts | nipples | spread_legs | sweat | nude | blue_eyes | cum |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:--------------------|:-------|:----------|:-------------|:-----------------|:-------|:---------------|:---------------------------------------|:--------------------|:-------------------|:------------|:--------|:--------------|:--------------|:--------------------|:--------------|:-------------|:--------|:---------|:---------------|:---------------|:--------|:----------------|:--------|:-------|:-------------|:--------------------|:-------------|:-------|:--------------|:---------|:--------|:------|:----------|:---------------|:----------|:-------------|:-------------|:-----------------|:----------|:--------------|:--------|:-------|:------------|:------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | X | | | X | | | X | X | X | X | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | | X | | | | | X | | | | X | | | | | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | | | | X | | | | | | | | | | | | | | X | X | | | | | | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | |
| 4 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | X | | X | | | | X | | X | X | | X | X | X | X |
| CyberHarem/florina_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:26:04+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:12:23+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of florina (Fire Emblem)
================================
This is the dataset of florina (Fire Emblem), containing 223 images and their tags.
The core tags of this character are 'long\_hair, purple\_hair, green\_eyes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
d7ccf1dd5e34c4705ad4af23393dfb47b66044c2 |
# Dataset of peony (Fire Emblem)
This is the dataset of peony (Fire Emblem), containing 69 images and their tags.
The core tags of this character are `breasts, blonde_hair, wings, pointy_ears, hair_ornament, fairy_wings, purple_eyes, hair_flower, long_hair, multicolored_hair, large_breasts, bangs, gradient_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 69 | 94.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peony_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 69 | 57.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peony_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 172 | 121.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peony_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 69 | 85.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peony_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 172 | 162.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peony_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/peony_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, bare_shoulders, dress, flower, solo, hairband, smile, looking_at_viewer, sleeveless, open_mouth, blush, cleavage, orange_hair, simple_background, thighhighs |
| 1 | 5 |  |  |  |  |  | 1girl, bare_shoulders, flower, gradient_clothes, open_mouth, short_dress, sleeveless, smile, solo, thighhighs, cleavage, floating_object, looking_at_viewer, medium_breasts, shiny_hair, thigh_boots, twintails, black_footwear, hairband, halterneck, headband, orange_hair, petals, shiny_skin, zettai_ryouiki, butterfly, company_name, copyright_name, full_body, jewelry, leaf |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | dress | flower | solo | hairband | smile | looking_at_viewer | sleeveless | open_mouth | blush | cleavage | orange_hair | simple_background | thighhighs | gradient_clothes | short_dress | floating_object | medium_breasts | shiny_hair | thigh_boots | twintails | black_footwear | halterneck | headband | petals | shiny_skin | zettai_ryouiki | butterfly | company_name | copyright_name | full_body | jewelry | leaf |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:---------|:-------|:-----------|:--------|:--------------------|:-------------|:-------------|:--------|:-----------|:--------------|:--------------------|:-------------|:-------------------|:--------------|:------------------|:-----------------|:-------------|:--------------|:------------|:-----------------|:-------------|:-----------|:---------|:-------------|:-----------------|:------------|:---------------|:-----------------|:------------|:----------|:-------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/peony_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:26:04+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:42:43+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of peony (Fire Emblem)
==============================
This is the dataset of peony (Fire Emblem), containing 69 images and their tags.
The core tags of this character are 'breasts, blonde\_hair, wings, pointy\_ears, hair\_ornament, fairy\_wings, purple\_eyes, hair\_flower, long\_hair, multicolored\_hair, large\_breasts, bangs, gradient\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
d19ca6176e3b3ae34826b4a015c87af1f3d90c49 | Images via https://unsplash.com/@alvannee
shared under the unsplash license: https://unsplash.com/license | dmarx/corgi-small | [
"region:us"
] | 2024-01-17T21:30:37+00:00 | {} | 2024-01-17T22:37:41+00:00 | [] | [] | TAGS
#region-us
| Images via URL
shared under the unsplash license: URL | [] | [
"TAGS\n#region-us \n"
] |
1bff97da8d090b83b951225deb6eff8619954d6a |
# Dataset of miriel (Fire Emblem)
This is the dataset of miriel (Fire Emblem), containing 89 images and their tags.
The core tags of this character are `glasses, short_hair, red_hair, hat, witch_hat, breasts, large_breasts, brown_hair, red_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 89 | 78.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miriel_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 89 | 48.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miriel_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 176 | 92.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miriel_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 89 | 71.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miriel_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 176 | 128.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miriel_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/miriel_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, hetero, solo_focus, 1boy, nipples, uncensored, pussy, sex, nude, blush, spread_legs, clitoris, cum, large_penis, navel, vaginal |
| 1 | 44 |  |  |  |  |  | 1girl, solo, book, cape, brown_eyes, simple_background, bridal_gauntlets |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hetero | solo_focus | 1boy | nipples | uncensored | pussy | sex | nude | blush | spread_legs | clitoris | cum | large_penis | navel | vaginal | solo | book | cape | brown_eyes | simple_background | bridal_gauntlets |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------------|:-------|:----------|:-------------|:--------|:------|:-------|:--------|:--------------|:-----------|:------|:--------------|:--------|:----------|:-------|:-------|:-------|:-------------|:--------------------|:-------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 1 | 44 |  |  |  |  |  | X | | | | | | | | | | | | | | | | X | X | X | X | X | X |
| CyberHarem/miriel_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:36:06+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:54:08+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of miriel (Fire Emblem)
===============================
This is the dataset of miriel (Fire Emblem), containing 89 images and their tags.
The core tags of this character are 'glasses, short\_hair, red\_hair, hat, witch\_hat, breasts, large\_breasts, brown\_hair, red\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
d292a110ae15ff67040ae5d35b6875e08d0d4cda |
# Dataset of mist (Fire Emblem)
This is the dataset of mist (Fire Emblem), containing 180 images and their tags.
The core tags of this character are `brown_hair, blue_eyes, short_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 180 | 139.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mist_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 180 | 103.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mist_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 326 | 176.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mist_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 180 | 131.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mist_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 326 | 213.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mist_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mist_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, solo, hair_tubes, scarf, simple_background, white_background, open_mouth, long_hair, staff, :d, boots, fingerless_gloves, full_body, miniskirt |
| 1 | 8 |  |  |  |  |  | 1girl, hair_tubes, solo, open_mouth, skirt, elbow_gloves, scarf, :d, cape |
| 2 | 5 |  |  |  |  |  | 1girl, hair_tubes, looking_at_viewer, simple_background, upper_body, cape, detached_sleeves, open_mouth, shirt, short_sleeves, solo, white_background, :d, bangs, hair_between_eyes, shiny_hair, long_sleeves |
| 3 | 7 |  |  |  |  |  | 1girl, hairband, smile, dress, hair_flower, open_mouth, long_sleeves, hair_tubes, simple_background, 1boy, blush, closed_eyes, solo |
| 4 | 6 |  |  |  |  |  | 1girl, hetero, long_hair, mosaic_censoring, nipples, open_mouth, penis, vaginal, blush, cum_in_pussy, large_breasts, pregnant, spread_legs, hair_tubes, navel, solo_focus, 1boy, 3boys, completely_nude, gangbang, smile, sweat, tears, tongue |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | hair_tubes | scarf | simple_background | white_background | open_mouth | long_hair | staff | :d | boots | fingerless_gloves | full_body | miniskirt | skirt | elbow_gloves | cape | looking_at_viewer | upper_body | detached_sleeves | shirt | short_sleeves | bangs | hair_between_eyes | shiny_hair | long_sleeves | hairband | smile | dress | hair_flower | 1boy | blush | closed_eyes | hetero | mosaic_censoring | nipples | penis | vaginal | cum_in_pussy | large_breasts | pregnant | spread_legs | navel | solo_focus | 3boys | completely_nude | gangbang | sweat | tears | tongue |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:--------|:--------------------|:-------------------|:-------------|:------------|:--------|:-----|:--------|:--------------------|:------------|:------------|:--------|:---------------|:-------|:--------------------|:-------------|:-------------------|:--------|:----------------|:--------|:--------------------|:-------------|:---------------|:-----------|:--------|:--------|:--------------|:-------|:--------|:--------------|:---------|:-------------------|:----------|:--------|:----------|:---------------|:----------------|:-----------|:--------------|:--------|:-------------|:--------|:------------------|:-----------|:--------|:--------|:---------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | | | X | | | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | | X | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | X | | X | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/mist_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:36:07+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:14:16+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of mist (Fire Emblem)
=============================
This is the dataset of mist (Fire Emblem), containing 180 images and their tags.
The core tags of this character are 'brown\_hair, blue\_eyes, short\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
b2b17ac96bef5cf61addc5a9f75858cfa0b38870 |
# Dataset of serra (Fire Emblem)
This is the dataset of serra (Fire Emblem), containing 155 images and their tags.
The core tags of this character are `pink_hair, twintails, long_hair, purple_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 155 | 128.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serra_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 155 | 86.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serra_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 265 | 153.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serra_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 155 | 117.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serra_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 265 | 199.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serra_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/serra_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | 1girl, elbow_gloves, solo, simple_background, dress, smile, scarf, white_gloves, open_mouth, white_background, belt, looking_at_viewer, holding_staff, cape, sleeveless |
| 1 | 19 |  |  |  |  |  | 1girl, elbow_gloves, solo, smile, dress, cape, open_mouth, scarf, staff, blush |
| 2 | 7 |  |  |  |  |  | 1girl, nude, solo, medium_breasts, nipples, blush, navel |
| 3 | 22 |  |  |  |  |  | 1girl, hetero, solo_focus, 1boy, penis, blush, mosaic_censoring, nipples, pussy, cum, medium_breasts, nude, sex, sweat, elbow_gloves, vaginal |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | elbow_gloves | solo | simple_background | dress | smile | scarf | white_gloves | open_mouth | white_background | belt | looking_at_viewer | holding_staff | cape | sleeveless | staff | blush | nude | medium_breasts | nipples | navel | hetero | solo_focus | 1boy | penis | mosaic_censoring | pussy | cum | sex | sweat | vaginal |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:--------------------|:--------|:--------|:--------|:---------------|:-------------|:-------------------|:-------|:--------------------|:----------------|:-------|:-------------|:--------|:--------|:-------|:-----------------|:----------|:--------|:---------|:-------------|:-------|:--------|:-------------------|:--------|:------|:------|:--------|:----------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 19 |  |  |  |  |  | X | X | X | | X | X | X | | X | | | | | X | | X | X | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | X | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | |
| 3 | 22 |  |  |  |  |  | X | X | | | | | | | | | | | | | | | X | X | X | X | | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/serra_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:36:25+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:03:35+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of serra (Fire Emblem)
==============================
This is the dataset of serra (Fire Emblem), containing 155 images and their tags.
The core tags of this character are 'pink\_hair, twintails, long\_hair, purple\_eyes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
babe5fbf64802f1124e0a85d341628313a99c827 |
# Dataset of seiros (Fire Emblem)
This is the dataset of seiros (Fire Emblem), containing 47 images and their tags.
The core tags of this character are `long_hair, green_hair, green_eyes, hair_ornament, hair_flower, breasts, braid`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 47 | 50.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seiros_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 47 | 33.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seiros_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 100 | 62.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seiros_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 47 | 47.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seiros_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 100 | 81.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/seiros_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/seiros_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, solo, cape, simple_background, upper_body, looking_at_viewer, white_background, holding_sword, white_flower |
| 1 | 5 |  |  |  |  |  | 1girl, cape, flower, solo, white_dress, arm_guards, armor, blonde_hair, full_body, gradient_hair, long_dress, medium_breasts, sandals, shield, single_braid, sleeveless_dress, sword, thigh_strap, toeless_footwear, toes, bangs, belt, chain, sheath, two-tone_hair, white_background, closed_mouth, high_heels, holding, looking_at_viewer, parted_lips |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cape | simple_background | upper_body | looking_at_viewer | white_background | holding_sword | white_flower | flower | white_dress | arm_guards | armor | blonde_hair | full_body | gradient_hair | long_dress | medium_breasts | sandals | shield | single_braid | sleeveless_dress | sword | thigh_strap | toeless_footwear | toes | bangs | belt | chain | sheath | two-tone_hair | closed_mouth | high_heels | holding | parted_lips |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------|:--------------------|:-------------|:--------------------|:-------------------|:----------------|:---------------|:---------|:--------------|:-------------|:--------|:--------------|:------------|:----------------|:-------------|:-----------------|:----------|:---------|:---------------|:-------------------|:--------|:--------------|:-------------------|:-------|:--------|:-------|:--------|:---------|:----------------|:---------------|:-------------|:----------|:--------------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/seiros_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:48:23+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:56:24+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of seiros (Fire Emblem)
===============================
This is the dataset of seiros (Fire Emblem), containing 47 images and their tags.
The core tags of this character are 'long\_hair, green\_hair, green\_eyes, hair\_ornament, hair\_flower, breasts, braid', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
4a4966a85b5c080314f32511974291d6fb432b5b |
# Dataset of igrene (Fire Emblem)
This is the dataset of igrene (Fire Emblem), containing 198 images and their tags.
The core tags of this character are `blonde_hair, long_hair, breasts, large_breasts, dark_skin, mole, yellow_eyes, dark-skinned_female, mole_under_eye`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 198 | 321.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igrene_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 198 | 160.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igrene_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 481 | 340.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igrene_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 198 | 272.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igrene_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 481 | 513.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igrene_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/igrene_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, penis, solo_focus, facial, nipples, nude, blush, cum_on_breasts, mosaic_censoring, paizuri, sweat, tears |
| 1 | 5 |  |  |  |  |  | 1girl, black_gloves, elbow_gloves, nipples, thigh_boots, thighhighs, 1boy, cum_in_pussy, hetero, no_mole, on_back, penis, red_dress, vaginal, blush, breasts_out, clothed_sex, female_pubic_hair, fingerless_gloves, mosaic_censoring, open_mouth, spread_legs, bar_censor, cape, missionary, scarf, solo_focus, torn_clothes |
| 2 | 6 |  |  |  |  |  | 1girl, belt, black_gloves, cleavage, elbow_gloves, fingerless_gloves, quiver, red_dress, short_dress, solo, thigh_boots, thighhighs, arrow_(projectile), black_footwear, bow_(weapon), brown_cape, looking_at_viewer, smile |
| 3 | 10 |  |  |  |  |  | 1girl, black_gloves, cleavage, elbow_gloves, ninja, official_alternate_costume, solo, thighhighs, fingerless_gloves, kunai, looking_at_viewer, red_dress, thighs, brown_scarf, pelvic_curtain, smile, thigh_boots, cape, holding, night |
| 4 | 5 |  |  |  |  |  | 1girl, blush, nipples, smile, solo, bangs, collarbone, completely_nude, hair_between_eyes, looking_at_viewer, navel, pussy, barefoot, holding, standing, very_long_hair, blurry, brown_eyes, lipstick, lying, shiny_skin, simple_background, uncensored, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | penis | solo_focus | facial | nipples | nude | blush | cum_on_breasts | mosaic_censoring | paizuri | sweat | tears | black_gloves | elbow_gloves | thigh_boots | thighhighs | cum_in_pussy | no_mole | on_back | red_dress | vaginal | breasts_out | clothed_sex | female_pubic_hair | fingerless_gloves | open_mouth | spread_legs | bar_censor | cape | missionary | scarf | torn_clothes | belt | cleavage | quiver | short_dress | solo | arrow_(projectile) | black_footwear | bow_(weapon) | brown_cape | looking_at_viewer | smile | ninja | official_alternate_costume | kunai | thighs | brown_scarf | pelvic_curtain | holding | night | bangs | collarbone | completely_nude | hair_between_eyes | navel | pussy | barefoot | standing | very_long_hair | blurry | brown_eyes | lipstick | lying | shiny_skin | simple_background | uncensored | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:--------|:-------------|:---------|:----------|:-------|:--------|:-----------------|:-------------------|:----------|:--------|:--------|:---------------|:---------------|:--------------|:-------------|:---------------|:----------|:----------|:------------|:----------|:--------------|:--------------|:--------------------|:--------------------|:-------------|:--------------|:-------------|:-------|:-------------|:--------|:---------------|:-------|:-----------|:---------|:--------------|:-------|:---------------------|:-----------------|:---------------|:-------------|:--------------------|:--------|:--------|:-----------------------------|:--------|:---------|:--------------|:-----------------|:----------|:--------|:--------|:-------------|:------------------|:--------------------|:--------|:--------|:-----------|:-----------|:-----------------|:---------|:-------------|:-----------|:--------|:-------------|:--------------------|:-------------|:-------------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | X | | X | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | | X | | | | | | | | | | | | | X | X | X | X | | | | X | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | | X | | | | | | | | | | | | | X | X | X | X | | | | X | | | | | X | | | | X | | | | | X | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/igrene_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:48:29+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:40:30+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of igrene (Fire Emblem)
===============================
This is the dataset of igrene (Fire Emblem), containing 198 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, breasts, large\_breasts, dark\_skin, mole, yellow\_eyes, dark-skinned\_female, mole\_under\_eye', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
b95cc9d6777a83aa04fe24443b59d698bab356f6 |
# Dataset of mikoto (Fire Emblem)
This is the dataset of mikoto (Fire Emblem), containing 23 images and their tags.
The core tags of this character are `black_hair, long_hair, breasts, mole, ponytail, brown_eyes, mole_under_mouth, large_breasts, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 23 | 24.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikoto_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 23 | 15.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikoto_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 50 | 28.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikoto_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 23 | 21.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikoto_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 50 | 35.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikoto_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mikoto_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, solo, smile, looking_at_viewer |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | looking_at_viewer |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X |
| CyberHarem/mikoto_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:48:32+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T21:54:18+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of mikoto (Fire Emblem)
===============================
This is the dataset of mikoto (Fire Emblem), containing 23 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, breasts, mole, ponytail, brown\_eyes, mole\_under\_mouth, large\_breasts, hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
246306663b6adb4722b75726a1b033df547ef384 |
# Dataset of kagerou (Fire Emblem)
This is the dataset of kagerou (Fire Emblem), containing 424 images and their tags.
The core tags of this character are `breasts, long_hair, hair_over_one_eye, brown_eyes, brown_hair, large_breasts, ponytail`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 424 | 518.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kagerou_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 424 | 298.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kagerou_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1035 | 617.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kagerou_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 424 | 457.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kagerou_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1035 | 857.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kagerou_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kagerou_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, blush, nipples, solo, looking_at_viewer, navel, completely_nude, scarf |
| 1 | 18 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, solo_focus, sex, open_mouth, penis, completely_nude, navel, pussy, sweat, black_hair, uncensored, vaginal, pov, lying, spread_legs |
| 2 | 6 |  |  |  |  |  | 1boy, 1girl, hetero, paizuri, penis, blush, nipples, sweat, huge_breasts, mosaic_censoring, open_mouth, breasts_squeezed_together, completely_nude, fake_animal_ears, gloves, rabbit_ears, solo_focus |
| 3 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, penis, solo_focus, spread_legs, blush, cum_in_pussy, female_pubic_hair, mosaic_censoring, open_mouth, sex_from_behind, testicles, vaginal, sweat, teeth, anus, arms_up, black_hair, closed_eyes, reverse_suspended_congress, tears, thighhighs, yellow_scarf |
| 4 | 7 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, scarf, solo, ninja, simple_background, upper_body, white_background |
| 5 | 8 |  |  |  |  |  | 1girl, cleavage, ninja, solo, japanese_clothes, simple_background, kunai, looking_at_viewer, yellow_scarf, black_hair, thighs |
| 6 | 6 |  |  |  |  |  | 1girl, solo, maid_headdress, blush, looking_at_viewer, nipples, upper_body, breasts_out, huge_breasts, simple_background, smile |
| 7 | 17 |  |  |  |  |  | cleavage, 1girl, bare_shoulders, looking_at_viewer, solo, smile, kimono, thighs, white_background, blush, necklace, simple_background, hair_ornament, bangs, beads, detached_sleeves, sash |
| 8 | 48 |  |  |  |  |  | rabbit_ears, 1girl, fake_animal_ears, playboy_bunny, solo, pantyhose, cleavage, gloves, blush, carrot, leotard, simple_background, looking_at_viewer, bare_shoulders, ninja |
| 9 | 5 |  |  |  |  |  | 1girl, cleavage, navel, solo, braid, looking_at_viewer, bangs, closed_mouth, white_bikini, bare_shoulders, black_hair, blush, simple_background, white_background |
| 10 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, cleavage, navel, solo, bra, thighhighs, blush, lingerie, collarbone, smile, choker, parted_lips, red_panties |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | nipples | solo | looking_at_viewer | navel | completely_nude | scarf | 1boy | hetero | solo_focus | sex | open_mouth | penis | pussy | sweat | black_hair | uncensored | vaginal | pov | lying | spread_legs | paizuri | huge_breasts | mosaic_censoring | breasts_squeezed_together | fake_animal_ears | gloves | rabbit_ears | cum_in_pussy | female_pubic_hair | sex_from_behind | testicles | teeth | anus | arms_up | closed_eyes | reverse_suspended_congress | tears | thighhighs | yellow_scarf | cleavage | ninja | simple_background | upper_body | white_background | japanese_clothes | kunai | thighs | maid_headdress | breasts_out | smile | bare_shoulders | kimono | necklace | hair_ornament | bangs | beads | detached_sleeves | sash | playboy_bunny | pantyhose | carrot | leotard | braid | closed_mouth | white_bikini | bra | lingerie | collarbone | choker | parted_lips | red_panties |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:----------|:-------|:--------------------|:--------|:------------------|:--------|:-------|:---------|:-------------|:------|:-------------|:--------|:--------|:--------|:-------------|:-------------|:----------|:------|:--------|:--------------|:----------|:---------------|:-------------------|:----------------------------|:-------------------|:---------|:--------------|:---------------|:--------------------|:------------------|:------------|:--------|:-------|:----------|:--------------|:-----------------------------|:--------|:-------------|:---------------|:-----------|:--------|:--------------------|:-------------|:-------------------|:-------------------|:--------|:---------|:-----------------|:--------------|:--------|:-----------------|:---------|:-----------|:----------------|:--------|:--------|:-------------------|:-------|:----------------|:------------|:---------|:----------|:--------|:---------------|:---------------|:------|:-----------|:-------------|:---------|:--------------|:--------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 18 |  |  |  |  |  | X | X | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | | | | X | | X | X | X | | X | X | | X | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | X | | | | | | X | X | X | | X | X | | X | X | | X | | | X | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 7 | 17 |  |  |  |  |  | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | X | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 8 | 48 |  |  |  |  |  | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | X | X | X | | | | | | | | | X | | | | | | | | X | X | X | X | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | X | | X | X | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | X | | | | | | | X | | | | X | | | | | | | | X | X | X | | | | | | |
| 10 | 10 |  |  |  |  |  | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X |
| CyberHarem/kagerou_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T21:48:38+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T23:37:08+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kagerou (Fire Emblem)
================================
This is the dataset of kagerou (Fire Emblem), containing 424 images and their tags.
The core tags of this character are 'breasts, long\_hair, hair\_over\_one\_eye, brown\_eyes, brown\_hair, large\_breasts, ponytail', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
db1a67c1bdd54fbb8536af026dc8596f00f9c41d |
## Description
A dataset with texts and the categories to which these texts belong.
## Usage
This dataset can be used to check language models for the correct classification of texts by category.
## Dataset structure:
- **lang**: the language to which the text source belongs;
- **title**: the title of the text;
- **original_text**: original text taken from a web page;
- **processed_text**: processed text using preprocessing functions;
- **category**: the category to which the text belongs;
- **processed**: flag indicating that one or more sentence has been deleted from the text;
- **url**: link to the source;
- **date**: date of publication of the text;
## The creation process
This dataset was obtained by parsing news resources of countries and regions of native speakers of Turkic languages, such as Bashkir, Kazakh and Kyrgyz.
During parsing, it was a priori believed that the language of the articles was written in the language of the region about which the news was written.
After parsing, the text of the articles was processed through the preprocessing functions described on [github](https://github.com/Electrotubbie/turk_langs_analyse ).
The scheme of text preprocessing and validation is as follows:
- cleaning the text from unnecessary constructions using regular expressions;
- splitting text into sentences using the sentenize function of the razdel module;
- making predictions for each sentence using the lid.176.bin model, as well as the fasttext module;
- deleting sentences written in non-Turkic languages;
- combining valid sentences into text and getting the processed_text column. | Electrotubbie/classification_Turkic_languages | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:ba",
"language:kk",
"language:ky",
"region:us"
] | 2024-01-17T22:02:43+00:00 | {"language": ["ba", "kk", "ky"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"]} | 2024-01-17T22:39:59+00:00 | [] | [
"ba",
"kk",
"ky"
] | TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-Bashkir #language-Kazakh #language-Kirghiz #region-us
|
## Description
A dataset with texts and the categories to which these texts belong.
## Usage
This dataset can be used to check language models for the correct classification of texts by category.
## Dataset structure:
- lang: the language to which the text source belongs;
- title: the title of the text;
- original_text: original text taken from a web page;
- processed_text: processed text using preprocessing functions;
- category: the category to which the text belongs;
- processed: flag indicating that one or more sentence has been deleted from the text;
- url: link to the source;
- date: date of publication of the text;
## The creation process
This dataset was obtained by parsing news resources of countries and regions of native speakers of Turkic languages, such as Bashkir, Kazakh and Kyrgyz.
During parsing, it was a priori believed that the language of the articles was written in the language of the region about which the news was written.
After parsing, the text of the articles was processed through the preprocessing functions described on github.
The scheme of text preprocessing and validation is as follows:
- cleaning the text from unnecessary constructions using regular expressions;
- splitting text into sentences using the sentenize function of the razdel module;
- making predictions for each sentence using the URL model, as well as the fasttext module;
- deleting sentences written in non-Turkic languages;
- combining valid sentences into text and getting the processed_text column. | [
"## Description\nA dataset with texts and the categories to which these texts belong.",
"## Usage\nThis dataset can be used to check language models for the correct classification of texts by category.",
"## Dataset structure:\n- lang: the language to which the text source belongs;\n- title: the title of the text;\n- original_text: original text taken from a web page;\n- processed_text: processed text using preprocessing functions;\n- category: the category to which the text belongs;\n- processed: flag indicating that one or more sentence has been deleted from the text;\n- url: link to the source;\n- date: date of publication of the text;",
"## The creation process\nThis dataset was obtained by parsing news resources of countries and regions of native speakers of Turkic languages, such as Bashkir, Kazakh and Kyrgyz.\nDuring parsing, it was a priori believed that the language of the articles was written in the language of the region about which the news was written.\nAfter parsing, the text of the articles was processed through the preprocessing functions described on github.\n\nThe scheme of text preprocessing and validation is as follows:\n- cleaning the text from unnecessary constructions using regular expressions;\n- splitting text into sentences using the sentenize function of the razdel module;\n- making predictions for each sentence using the URL model, as well as the fasttext module;\n- deleting sentences written in non-Turkic languages;\n- combining valid sentences into text and getting the processed_text column."
] | [
"TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-Bashkir #language-Kazakh #language-Kirghiz #region-us \n",
"## Description\nA dataset with texts and the categories to which these texts belong.",
"## Usage\nThis dataset can be used to check language models for the correct classification of texts by category.",
"## Dataset structure:\n- lang: the language to which the text source belongs;\n- title: the title of the text;\n- original_text: original text taken from a web page;\n- processed_text: processed text using preprocessing functions;\n- category: the category to which the text belongs;\n- processed: flag indicating that one or more sentence has been deleted from the text;\n- url: link to the source;\n- date: date of publication of the text;",
"## The creation process\nThis dataset was obtained by parsing news resources of countries and regions of native speakers of Turkic languages, such as Bashkir, Kazakh and Kyrgyz.\nDuring parsing, it was a priori believed that the language of the articles was written in the language of the region about which the news was written.\nAfter parsing, the text of the articles was processed through the preprocessing functions described on github.\n\nThe scheme of text preprocessing and validation is as follows:\n- cleaning the text from unnecessary constructions using regular expressions;\n- splitting text into sentences using the sentenize function of the razdel module;\n- making predictions for each sentence using the URL model, as well as the fasttext module;\n- deleting sentences written in non-Turkic languages;\n- combining valid sentences into text and getting the processed_text column."
] |
99f019871d4d5a83124a8805c8b5294d6df975f7 |
# Dataset of amelia (Fire Emblem)
This is the dataset of amelia (Fire Emblem), containing 63 images and their tags.
The core tags of this character are `blonde_hair, short_hair, green_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 63 | 63.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amelia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 63 | 37.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amelia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 115 | 69.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amelia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 63 | 57.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amelia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 115 | 94.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amelia_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/amelia_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | 1girl, solo, skirt, open_mouth, zettai_ryouiki, shoulder_armor, gauntlets, full_body, white_background, bangs, smile, chain, holding_weapon, red_cape, battle_axe, black_thighhighs, elbow_gloves, huge_weapon, looking_at_viewer, simple_background |
| 1 | 21 |  |  |  |  |  | 1girl, hetero, solo_focus, penis, 1boy, nipples, sex, blush, open_mouth, vaginal, thighhighs, cum_in_pussy, nude, large_breasts, medium_breasts, mosaic_censoring, sweat, spread_legs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | skirt | open_mouth | zettai_ryouiki | shoulder_armor | gauntlets | full_body | white_background | bangs | smile | chain | holding_weapon | red_cape | battle_axe | black_thighhighs | elbow_gloves | huge_weapon | looking_at_viewer | simple_background | hetero | solo_focus | penis | 1boy | nipples | sex | blush | vaginal | thighhighs | cum_in_pussy | nude | large_breasts | medium_breasts | mosaic_censoring | sweat | spread_legs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-------------|:-----------------|:-----------------|:------------|:------------|:-------------------|:--------|:--------|:--------|:-----------------|:-----------|:-------------|:-------------------|:---------------|:--------------|:--------------------|:--------------------|:---------|:-------------|:--------|:-------|:----------|:------|:--------|:----------|:-------------|:---------------|:-------|:----------------|:-----------------|:-------------------|:--------|:--------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 21 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/amelia_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:04:59+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:17:14+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of amelia (Fire Emblem)
===============================
This is the dataset of amelia (Fire Emblem), containing 63 images and their tags.
The core tags of this character are 'blonde\_hair, short\_hair, green\_eyes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
9ebc0dddceb5e86b6f58f7541124f0bc3c315d0f |
# Dataset of sigrun (Fire Emblem)
This is the dataset of sigrun (Fire Emblem), containing 66 images and their tags.
The core tags of this character are `long_hair, green_hair, green_eyes, breasts, large_breasts, aqua_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 66 | 56.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sigrun_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 66 | 38.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sigrun_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 132 | 70.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sigrun_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 66 | 52.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sigrun_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 132 | 88.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sigrun_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sigrun_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, breastplate, cape, elbow_gloves, low-tied_long_hair, shoulder_armor, solo, aqua_eyes, belt, boots, full_body, hair_ornament, holding_weapon, pantyhose, shiny_hair, arm_guards, headpiece, open_mouth, polearm, simple_background, smile, turtleneck, white_background, white_footwear |
| 1 | 5 |  |  |  |  |  | 1girl, armor, solo, elbow_gloves, smile, blush, cape, fingerless_gloves, nipples |
| 2 | 15 |  |  |  |  |  | 1girl, hetero, nipples, solo_focus, blush, vaginal, mosaic_censoring, multiple_boys, multiple_penises, open_mouth, 1boy, cum_in_pussy, cum_on_breasts, nude, gangbang, straddling |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | breastplate | cape | elbow_gloves | low-tied_long_hair | shoulder_armor | solo | aqua_eyes | belt | boots | full_body | hair_ornament | holding_weapon | pantyhose | shiny_hair | arm_guards | headpiece | open_mouth | polearm | simple_background | smile | turtleneck | white_background | white_footwear | armor | blush | fingerless_gloves | nipples | hetero | solo_focus | vaginal | mosaic_censoring | multiple_boys | multiple_penises | 1boy | cum_in_pussy | cum_on_breasts | nude | gangbang | straddling |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-------|:---------------|:---------------------|:-----------------|:-------|:------------|:-------|:--------|:------------|:----------------|:-----------------|:------------|:-------------|:-------------|:------------|:-------------|:----------|:--------------------|:--------|:-------------|:-------------------|:-----------------|:--------|:--------|:--------------------|:----------|:---------|:-------------|:----------|:-------------------|:----------------|:-------------------|:-------|:---------------|:-----------------|:-------|:-----------|:-------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | X | X | | | X | | | | | | | | | | | | | | X | | | | X | X | X | X | | | | | | | | | | | | |
| 2 | 15 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | X | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/sigrun_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:05:02+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:19:09+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of sigrun (Fire Emblem)
===============================
This is the dataset of sigrun (Fire Emblem), containing 66 images and their tags.
The core tags of this character are 'long\_hair, green\_hair, green\_eyes, breasts, large\_breasts, aqua\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
6a83bb6bd3c927e7bc5f36ae29f43c29db1b752e |
# Dataset of eitri (Fire Emblem)
This is the dataset of eitri (Fire Emblem), containing 49 images and their tags.
The core tags of this character are `blonde_hair, long_hair, bangs, hat, red_eyes, breasts, witch_hat, earrings, blunt_bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 49 | 76.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eitri_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 49 | 40.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eitri_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 124 | 89.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eitri_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 49 | 67.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eitri_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 124 | 128.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eitri_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/eitri_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 29 |  |  |  |  |  | 1girl, solo, looking_at_viewer, smile, black_gloves, cape, jewelry, simple_background |
| 1 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, navel, solo, white_bikini, day, hat_flower, jewelry, ocean, outdoors, sky, sun_hat, very_long_hair, armpits, arms_up, grin, medium_breasts, pelvic_curtain |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | smile | black_gloves | cape | jewelry | simple_background | navel | white_bikini | day | hat_flower | ocean | outdoors | sky | sun_hat | very_long_hair | armpits | arms_up | grin | medium_breasts | pelvic_curtain |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:---------------|:-------|:----------|:--------------------|:--------|:---------------|:------|:-------------|:--------|:-----------|:------|:----------|:-----------------|:----------|:----------|:-------|:-----------------|:-----------------|
| 0 | 29 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/eitri_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:05:05+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:17:16+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of eitri (Fire Emblem)
==============================
This is the dataset of eitri (Fire Emblem), containing 49 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, bangs, hat, red\_eyes, breasts, witch\_hat, earrings, blunt\_bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
8ec25f7051901a37c4ce2e17d02b00cf6f4746d0 |
# Dataset of mitama (Fire Emblem)
This is the dataset of mitama (Fire Emblem), containing 57 images and their tags.
The core tags of this character are `pink_hair, long_hair, symbol-shaped_pupils, star-shaped_pupils, twintails, low_twintails, bangs, blunt_bangs, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 57 | 61.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mitama_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 57 | 36.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mitama_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 120 | 71.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mitama_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 57 | 54.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mitama_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 120 | 102.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mitama_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mitama_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, solo, star_(symbol), fingerless_gloves, japanese_clothes, smile, simple_background, white_background, calligraphy_brush, elbow_gloves, looking_at_viewer |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | star_(symbol) | fingerless_gloves | japanese_clothes | smile | simple_background | white_background | calligraphy_brush | elbow_gloves | looking_at_viewer |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------------|:--------------------|:-------------------|:--------|:--------------------|:-------------------|:--------------------|:---------------|:--------------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/mitama_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:05:59+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:18:03+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of mitama (Fire Emblem)
===============================
This is the dataset of mitama (Fire Emblem), containing 57 images and their tags.
The core tags of this character are 'pink\_hair, long\_hair, symbol-shaped\_pupils, star-shaped\_pupils, twintails, low\_twintails, bangs, blunt\_bangs, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
975ba04960eb349e93092cf0296bba7c4da8a940 | This dataset is used to adapt [DeepLabCut](https://www.mackenziemathislab.org/deeplabcut) for Human motion tracking.
### Structure of the dataset
- `videos` contains 100+ videos of 4 candidates recorded during a game of darts.
- `labeled-data` contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under `labeled-data` there are 2 folders for every video.
- `video_name` has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.
- `video_name`_labeled has the overlay of the labels for the frames in `video_name`. | GT-Neuronext/human-motion-tracking-deeplabcut | [
"region:us"
] | 2024-01-17T22:06:17+00:00 | {} | 2024-01-29T21:18:32+00:00 | [] | [] | TAGS
#region-us
| This dataset is used to adapt DeepLabCut for Human motion tracking.
### Structure of the dataset
- 'videos' contains 100+ videos of 4 candidates recorded during a game of darts.
- 'labeled-data' contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under 'labeled-data' there are 2 folders for every video.
- 'video_name' has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.
- 'video_name'_labeled has the overlay of the labels for the frames in 'video_name'. | [
"### Structure of the dataset\n- 'videos' contains 100+ videos of 4 candidates recorded during a game of darts.\n- 'labeled-data' contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under 'labeled-data' there are 2 folders for every video.\n - 'video_name' has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.\n - 'video_name'_labeled has the overlay of the labels for the frames in 'video_name'."
] | [
"TAGS\n#region-us \n",
"### Structure of the dataset\n- 'videos' contains 100+ videos of 4 candidates recorded during a game of darts.\n- 'labeled-data' contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under 'labeled-data' there are 2 folders for every video.\n - 'video_name' has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.\n - 'video_name'_labeled has the overlay of the labels for the frames in 'video_name'."
] |
3b64e780a66af584f65c51ab6ea9b6805e18b707 | # lilac/dolphin
This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/cognitivecomputations/dolphin](https://huggingface.co/datasets/cognitivecomputations/dolphin)
To download the dataset to a local directory:
```bash
lilac download lilacai/lilac-dolphin
```
or from python with:
```py
ll.download("lilacai/lilac-dolphin")
```
| lilacai/lilac-dolphin | [
"Lilac",
"region:us"
] | 2024-01-17T22:13:50+00:00 | {"tags": ["Lilac"]} | 2024-01-26T15:03:12+00:00 | [] | [] | TAGS
#Lilac #region-us
| # lilac/dolphin
This dataset is a Lilac processed dataset. Original dataset: URL
To download the dataset to a local directory:
or from python with:
| [
"# lilac/dolphin\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:"
] | [
"TAGS\n#Lilac #region-us \n",
"# lilac/dolphin\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:"
] |
eea276dc58c52eab33e9476acb137ff5530b78e9 | # Dataset Card for "SciMMIR_dataset"
## SciMMIR
This is the repo for the paper [SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval](https://arxiv.org/abs/2401.13478).

In this paper, we propose a novel SciMMIR benchmark and a corresponding dataset designed to address the gap in evaluating multi-modal information retrieval (MMIR) models in the scientific domain.
It is worth mentioning that we define a data hierarchical architecture of "Two subsets, Five subcategories" and use human-created keywords to classify the data (as shown in the table below).

As shown in the table below, we conducted extensive baselines (both fine-tuning and zero-shot) within various subsets and subcategories.

For more detailed experimental results and analysis, please refer to our paper [SciMMIR](https://arxiv.org/abs/2401.13478).
## Dataset
Our SciMMIR benchmark dataset used in this paper contains 537K scientific image-text pairs which are extracted from the latest 6 months' papers in Arxiv (2023.05 to 2023.10), and we will continue to expand this data by extracting data from more papers in Arxiv and provide larger versions of the dataset.
The datasets can be obtained from huggingface Datasets [m-a-p/SciMMIR](https://huggingface.co/datasets/m-a-p/SciMMIR), and the following codes show how to use it:
```python
import datasets
ds_remote = datasets.load_dataset("m-a-p/SciMMIR")
test_data = ds_remote['test']
caption = test_data[0]['text']
image_type = test_data[0]['class']
image = test_data[0]['image']
```
## Codes
The codes of this paper can be found in our [Github](https://github.com/Wusiwei0410/SciMMIR)
## Potential TODOs before ACL
**TODO**: case study table
**TODO**: statistics of the paper fields (perhaps in appendix)
**TODO**: See if it's possible to further divide the "Figure Results" subsets.
## Citation
```
@misc{wu2024scimmir,
title={SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval},
author={Siwei Wu and Yizhi Li and Kang Zhu and Ge Zhang and Yiming Liang and Kaijing Ma and Chenghao Xiao and Haoran Zhang and Bohao Yang and Wenhu Chen and Wenhao Huang and Noura Al Moubayed and Jie Fu and Chenghua Lin},
year={2024},
eprint={2401.13478},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | m-a-p/SciMMIR | [
"arxiv:2401.13478",
"region:us"
] | 2024-01-17T22:14:33+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "file_name_index", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "class", "dtype": "string"}, {"name": "super_class", "dtype": "string"}, {"name": "sub_class", "dtype": "string"}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59242453844.635, "num_examples": 498279}, {"name": "validation", "num_bytes": 1783636593.843, "num_examples": 16433}, {"name": "test", "num_bytes": 1874022111.346, "num_examples": 16263}], "download_size": 63729889852, "dataset_size": 62900112549.824005}} | 2024-01-25T13:07:19+00:00 | [
"2401.13478"
] | [] | TAGS
#arxiv-2401.13478 #region-us
| # Dataset Card for "SciMMIR_dataset"
## SciMMIR
This is the repo for the paper SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval.
!main_result
In this paper, we propose a novel SciMMIR benchmark and a corresponding dataset designed to address the gap in evaluating multi-modal information retrieval (MMIR) models in the scientific domain.
It is worth mentioning that we define a data hierarchical architecture of "Two subsets, Five subcategories" and use human-created keywords to classify the data (as shown in the table below).
!main_result
As shown in the table below, we conducted extensive baselines (both fine-tuning and zero-shot) within various subsets and subcategories.
!main_result
For more detailed experimental results and analysis, please refer to our paper SciMMIR.
## Dataset
Our SciMMIR benchmark dataset used in this paper contains 537K scientific image-text pairs which are extracted from the latest 6 months' papers in Arxiv (2023.05 to 2023.10), and we will continue to expand this data by extracting data from more papers in Arxiv and provide larger versions of the dataset.
The datasets can be obtained from huggingface Datasets m-a-p/SciMMIR, and the following codes show how to use it:
## Codes
The codes of this paper can be found in our Github
## Potential TODOs before ACL
TODO: case study table
TODO: statistics of the paper fields (perhaps in appendix)
TODO: See if it's possible to further divide the "Figure Results" subsets.
More Information needed | [
"# Dataset Card for \"SciMMIR_dataset\"",
"## SciMMIR\n\nThis is the repo for the paper SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval.\n\n!main_result\n\nIn this paper, we propose a novel SciMMIR benchmark and a corresponding dataset designed to address the gap in evaluating multi-modal information retrieval (MMIR) models in the scientific domain.\n\nIt is worth mentioning that we define a data hierarchical architecture of \"Two subsets, Five subcategories\" and use human-created keywords to classify the data (as shown in the table below).\n\n!main_result\n\n\nAs shown in the table below, we conducted extensive baselines (both fine-tuning and zero-shot) within various subsets and subcategories.\n\n!main_result\n\nFor more detailed experimental results and analysis, please refer to our paper SciMMIR.",
"## Dataset\n\nOur SciMMIR benchmark dataset used in this paper contains 537K scientific image-text pairs which are extracted from the latest 6 months' papers in Arxiv (2023.05 to 2023.10), and we will continue to expand this data by extracting data from more papers in Arxiv and provide larger versions of the dataset.\n\nThe datasets can be obtained from huggingface Datasets m-a-p/SciMMIR, and the following codes show how to use it:",
"## Codes\nThe codes of this paper can be found in our Github",
"## Potential TODOs before ACL\n\nTODO: case study table\n\nTODO: statistics of the paper fields (perhaps in appendix)\n\nTODO: See if it's possible to further divide the \"Figure Results\" subsets.\n\nMore Information needed"
] | [
"TAGS\n#arxiv-2401.13478 #region-us \n",
"# Dataset Card for \"SciMMIR_dataset\"",
"## SciMMIR\n\nThis is the repo for the paper SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval.\n\n!main_result\n\nIn this paper, we propose a novel SciMMIR benchmark and a corresponding dataset designed to address the gap in evaluating multi-modal information retrieval (MMIR) models in the scientific domain.\n\nIt is worth mentioning that we define a data hierarchical architecture of \"Two subsets, Five subcategories\" and use human-created keywords to classify the data (as shown in the table below).\n\n!main_result\n\n\nAs shown in the table below, we conducted extensive baselines (both fine-tuning and zero-shot) within various subsets and subcategories.\n\n!main_result\n\nFor more detailed experimental results and analysis, please refer to our paper SciMMIR.",
"## Dataset\n\nOur SciMMIR benchmark dataset used in this paper contains 537K scientific image-text pairs which are extracted from the latest 6 months' papers in Arxiv (2023.05 to 2023.10), and we will continue to expand this data by extracting data from more papers in Arxiv and provide larger versions of the dataset.\n\nThe datasets can be obtained from huggingface Datasets m-a-p/SciMMIR, and the following codes show how to use it:",
"## Codes\nThe codes of this paper can be found in our Github",
"## Potential TODOs before ACL\n\nTODO: case study table\n\nTODO: statistics of the paper fields (perhaps in appendix)\n\nTODO: See if it's possible to further divide the \"Figure Results\" subsets.\n\nMore Information needed"
] |
3d2a39f8daca5dd02986e384eb0d287110cb0adf |
# Dataset Card
This dataset is the aligned phoneme subset of the DoReCo South England dataset, split into utterances + phonetic transcriptions based on pause lengths / total length,
with the goal of creating utterances < 30s for fine-tuning speech recognition models on phoneme recognition, not one phoneme at a time, but rather for entire utterances.
It is already randomly pre-split into train/dev/test sets, with 80% in train, 10% in dev, and the final 10% in test.
---
Link to original dataset website: https://doreco.huma-num.fr/languages/sout3282
Original dataset citation:
```
@incollection{doreco-sout3282,
address = {Berlin \& Lyon},
author = {Schiborr, Nils Norman},
booktitle = {Language Documentation Reference Corpus (DoReCo) 1.2},
editor = {Seifart, Frank and Paschen, Ludger and Stave, Matthew},
publisher = {Leibniz-Zentrum Allgemeine Sprachwissenschaft \& laboratoire Dynamique Du Langage (UMR5596, CNRS \& Université Lyon 2)},
title = {English (Southern England) DoReCo dataset},
url = {https://doreco.huma-num.fr/languages/sout3282},
doi = {10.34847/nkl.9c271u5g},
urldate = {16/01/2024},
year = {2022}
}
``` | bob80333/doreco_southengland | [
"license:cc-by-4.0",
"region:us"
] | 2024-01-17T22:18:46+00:00 | {"license": "cc-by-4.0"} | 2024-01-17T22:28:14+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
|
# Dataset Card
This dataset is the aligned phoneme subset of the DoReCo South England dataset, split into utterances + phonetic transcriptions based on pause lengths / total length,
with the goal of creating utterances < 30s for fine-tuning speech recognition models on phoneme recognition, not one phoneme at a time, but rather for entire utterances.
It is already randomly pre-split into train/dev/test sets, with 80% in train, 10% in dev, and the final 10% in test.
---
Link to original dataset website: URL
Original dataset citation:
| [
"# Dataset Card\n\nThis dataset is the aligned phoneme subset of the DoReCo South England dataset, split into utterances + phonetic transcriptions based on pause lengths / total length,\nwith the goal of creating utterances < 30s for fine-tuning speech recognition models on phoneme recognition, not one phoneme at a time, but rather for entire utterances.\n\nIt is already randomly pre-split into train/dev/test sets, with 80% in train, 10% in dev, and the final 10% in test.\n\n---\n\nLink to original dataset website: URL\n\nOriginal dataset citation:"
] | [
"TAGS\n#license-cc-by-4.0 #region-us \n",
"# Dataset Card\n\nThis dataset is the aligned phoneme subset of the DoReCo South England dataset, split into utterances + phonetic transcriptions based on pause lengths / total length,\nwith the goal of creating utterances < 30s for fine-tuning speech recognition models on phoneme recognition, not one phoneme at a time, but rather for entire utterances.\n\nIt is already randomly pre-split into train/dev/test sets, with 80% in train, 10% in dev, and the final 10% in test.\n\n---\n\nLink to original dataset website: URL\n\nOriginal dataset citation:"
] |
dc6112e6f8cf11e8ed73fe9ae475550446fef833 |
# 岁己SUI TTS 训练用数据
|采样率|时长|文件数量|标注文件格式|来源|
|-|-|-|-|-|
|22.05kHz、升采样44.1kHz|1:04:41|644|CSV (Pipe)|25788785直播间(22.12.05 - 23.02.22)|
仍算不上质量很高的数据,有部分音频还是存在较小的游戏音(大部分数据来源于玩文字游戏的过程中),再筛一遍完全没有背景音的也许能出半小时左右,开摆
什么,你问我为什么是 22.05k 的,之前训练 vits 用 ffmpeg 全转了,后来打包上传,脑子一抽转码前的全删了🤗
Linux 可安装 `p7zip-full` 并使用 `7z x ./xxx.7z` 来解压
路径构造:
```
SUITTSDATA.7z
├── esd.list
└── suijiSUI
├── 25788785-20221205-201900-593_101.wav
├── ...
└── 25788785-20230222-231523-985_99.wav
``` | Miuzarte/SUITTSDATA | [
"language:zh",
"region:us"
] | 2024-01-17T22:24:15+00:00 | {"language": ["zh"]} | 2024-01-18T10:21:38+00:00 | [] | [
"zh"
] | TAGS
#language-Chinese #region-us
| 岁己SUI TTS 训练用数据
===============
仍算不上质量很高的数据,有部分音频还是存在较小的游戏音(大部分数据来源于玩文字游戏的过程中),再筛一遍完全没有背景音的也许能出半小时左右,开摆
什么,你问我为什么是 22.05k 的,之前训练 vits 用 ffmpeg 全转了,后来打包上传,脑子一抽转码前的全删了
Linux 可安装 'p7zip-full' 并使用 '7z x ./xxx.7z' 来解压
路径构造:
| [] | [
"TAGS\n#language-Chinese #region-us \n"
] |
ae383a68564cefcb2d606ff70625507f5c1f0ddc |
# Dataset of ekhidna (Fire Emblem)
This is the dataset of ekhidna (Fire Emblem), containing 20 images and their tags.
The core tags of this character are `blue_eyes, blue_hair, earrings, headband, breasts, long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 20 | 21.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ekhidna_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 20 | 13.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ekhidna_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 40 | 24.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ekhidna_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 20 | 19.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ekhidna_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 40 | 32.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ekhidna_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ekhidna_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 20 |  |  |  |  |  | 1girl, solo, shoulder_spikes, smile, fingerless_gloves, weapon, bandana, jewelry, belt, looking_at_viewer, axe, boots, pauldrons, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | shoulder_spikes | smile | fingerless_gloves | weapon | bandana | jewelry | belt | looking_at_viewer | axe | boots | pauldrons | simple_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:------------------|:--------|:--------------------|:---------|:----------|:----------|:-------|:--------------------|:------|:--------|:------------|:--------------------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/ekhidna_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:28:44+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:32:39+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of ekhidna (Fire Emblem)
================================
This is the dataset of ekhidna (Fire Emblem), containing 20 images and their tags.
The core tags of this character are 'blue\_eyes, blue\_hair, earrings, headband, breasts, long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1e4615cf3ef027e60dde9fe1d093bc3d56a15165 |
# Dataset of brunya (Fire Emblem)
This is the dataset of brunya (Fire Emblem), containing 20 images and their tags.
The core tags of this character are `breasts, large_breasts, long_hair, purple_hair, purple_eyes, earrings, lips`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 20 | 34.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brunya_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 20 | 16.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brunya_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 46 | 34.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brunya_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 20 | 28.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brunya_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 46 | 50.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brunya_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/brunya_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 20 |  |  |  |  |  | 1girl, cleavage, jewelry, circlet, elbow_gloves, lipstick, solo, cape, simple_background, dress, looking_at_viewer, thighhighs, white_background, full_body, shoulder_armor, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | jewelry | circlet | elbow_gloves | lipstick | solo | cape | simple_background | dress | looking_at_viewer | thighhighs | white_background | full_body | shoulder_armor | thighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:----------|:----------|:---------------|:-----------|:-------|:-------|:--------------------|:--------|:--------------------|:-------------|:-------------------|:------------|:-----------------|:---------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/brunya_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:28:47+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:32:49+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of brunya (Fire Emblem)
===============================
This is the dataset of brunya (Fire Emblem), containing 20 images and their tags.
The core tags of this character are 'breasts, large\_breasts, long\_hair, purple\_hair, purple\_eyes, earrings, lips', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
f60549bd9c942a48d2dd9b9ada5500bbd16879f6 |
# Dataset of eir (Fire Emblem)
This is the dataset of eir (Fire Emblem), containing 87 images and their tags.
The core tags of this character are `long_hair, ponytail, breasts, earrings, blue_eyes, grey_hair, hair_ornament, very_long_hair, large_breasts, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 87 | 115.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 87 | 66.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 206 | 135.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 87 | 102.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 206 | 185.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/eir_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, choker, cleavage, solo, wide_sleeves, black_gloves, detached_sleeves, jewelry, bare_shoulders, long_sleeves, looking_at_viewer, black_dress, closed_mouth, collarbone, holding_dagger, strapless, white_hair |
| 1 | 5 |  |  |  |  |  | 1girl, flower, jewelry, solo, wide_sleeves, alternate_costume, animal, bangs, bird, blue_butterfly, dagger, detached_sleeves, frills, full_body, holding_weapon, long_dress, parted_lips, sandals, toeless_footwear, toes, gold_trim, looking_at_viewer, shiny_hair, smile, sword, white_background, covered_collarbone, dual_wielding, hand_up, looking_away, petals, see-through, simple_background, standing, transparent_background |
| 2 | 7 |  |  |  |  |  | 1girl, open_mouth, penis, hetero, jewelry, nipples, 1boy, black_gloves, blush, solo_focus, bar_censor, cum, sex, simple_background, vaginal, white_background, nude, pussy_juice, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | choker | cleavage | solo | wide_sleeves | black_gloves | detached_sleeves | jewelry | bare_shoulders | long_sleeves | looking_at_viewer | black_dress | closed_mouth | collarbone | holding_dagger | strapless | white_hair | flower | alternate_costume | animal | bangs | bird | blue_butterfly | dagger | frills | full_body | holding_weapon | long_dress | parted_lips | sandals | toeless_footwear | toes | gold_trim | shiny_hair | smile | sword | white_background | covered_collarbone | dual_wielding | hand_up | looking_away | petals | see-through | simple_background | standing | transparent_background | open_mouth | penis | hetero | nipples | 1boy | blush | solo_focus | bar_censor | cum | sex | vaginal | nude | pussy_juice | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-----------|:-------|:---------------|:---------------|:-------------------|:----------|:-----------------|:---------------|:--------------------|:--------------|:---------------|:-------------|:-----------------|:------------|:-------------|:---------|:--------------------|:---------|:--------|:-------|:-----------------|:---------|:---------|:------------|:-----------------|:-------------|:--------------|:----------|:-------------------|:-------|:------------|:-------------|:--------|:--------|:-------------------|:---------------------|:----------------|:----------|:---------------|:---------|:--------------|:--------------------|:-----------|:-------------------------|:-------------|:--------|:---------|:----------|:-------|:--------|:-------------|:-------------|:------|:------|:----------|:-------|:--------------|:-------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | | X | X | | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/eir_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:28:57+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:48:05+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of eir (Fire Emblem)
============================
This is the dataset of eir (Fire Emblem), containing 87 images and their tags.
The core tags of this character are 'long\_hair, ponytail, breasts, earrings, blue\_eyes, grey\_hair, hair\_ornament, very\_long\_hair, large\_breasts, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
9831e0769816d282f35a9914b16b70990963bdf8 | # local/dolphin
This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/cognitivecomputations/dolphin](https://huggingface.co/datasets/cognitivecomputations/dolphin)
To download the dataset to a local directory:
```bash
lilac download lilacai/local-dolphin
```
or from python with:
```py
ll.download("lilacai/local-dolphin")
```
| lilacai/local-dolphin | [
"Lilac",
"region:us"
] | 2024-01-17T22:29:18+00:00 | {"tags": ["Lilac"]} | 2024-01-17T22:31:47+00:00 | [] | [] | TAGS
#Lilac #region-us
| # local/dolphin
This dataset is a Lilac processed dataset. Original dataset: URL
To download the dataset to a local directory:
or from python with:
| [
"# local/dolphin\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:"
] | [
"TAGS\n#Lilac #region-us \n",
"# local/dolphin\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:"
] |
839ec210a1bac08c73e39c50be6d9f360dda77ba |
# Dataset of nailah (Fire Emblem)
This is the dataset of nailah (Fire Emblem), containing 60 images and their tags.
The core tags of this character are `animal_ears, breasts, long_hair, wolf_ears, tail, eyepatch, green_eyes, wolf_tail, purple_hair, dark_skin, dark-skinned_female, large_breasts, wolf_girl`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 60 | 79.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nailah_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 60 | 46.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nailah_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 136 | 90.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nailah_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 60 | 71.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nailah_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 136 | 121.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nailah_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nailah_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, solo, tattoo, medium_breasts, simple_background, sitting, looking_at_viewer |
| 1 | 5 |  |  |  |  |  | 1girl, blush, nipples, nude, solo, spread_legs, navel, spread_pussy, sweat, tattoo, clitoris, female_pubic_hair, huge_breasts, looking_at_viewer, on_back, open_mouth, pussy_juice, smile, tongue_out, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | tattoo | medium_breasts | simple_background | sitting | looking_at_viewer | blush | nipples | nude | spread_legs | navel | spread_pussy | sweat | clitoris | female_pubic_hair | huge_breasts | on_back | open_mouth | pussy_juice | smile | tongue_out | uncensored |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------|:-----------------|:--------------------|:----------|:--------------------|:--------|:----------|:-------|:--------------|:--------|:---------------|:--------|:-----------|:--------------------|:---------------|:----------|:-------------|:--------------|:--------|:-------------|:-------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/nailah_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:29:22+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:43:45+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of nailah (Fire Emblem)
===============================
This is the dataset of nailah (Fire Emblem), containing 60 images and their tags.
The core tags of this character are 'animal\_ears, breasts, long\_hair, wolf\_ears, tail, eyepatch, green\_eyes, wolf\_tail, purple\_hair, dark\_skin, dark-skinned\_female, large\_breasts, wolf\_girl', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
6e2695429e1dce880d2392d2d463e449d93c9862 |
# Triplets for Turkic languages language models
## Description
This dataset is designed to test models for working with Next Sentence Prediction (NSP) and Sentence Order Prediction (SOP). It includes two sub-sets with triplets of texts..
## Usage
This dataset can be used to train and evaluate models capable of performing NSP and SAP tasks.
## Dataset structure:
Each entry in the dataset represents three values:
- **text**: a triplet of text;
- **flag**: a flag indicating whether the order of sentences in this triplet is correct;
- **lang**: the language of the sentence.
## The creation process
Using the functions described on [github](https://github.com/Electrotubbie/turk_langs_analyse) preprocessing and analysis of texts from [dataset](https://huggingface.co/datasets/Electrotubbie/classification_Turkic_languages) was performed and triplets are selected according to certain rules (triplets should be approximately the same length and from 30 to 100 characters).
Also, the triplets were selected in such a way that no sentence displayed in the dataset was repeated several times. | Electrotubbie/triplets_Turkic_languages | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ba",
"language:kk",
"language:ky",
"region:us"
] | 2024-01-17T22:31:10+00:00 | {"language": ["ba", "kk", "ky"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]} | 2024-01-17T22:38:52+00:00 | [] | [
"ba",
"kk",
"ky"
] | TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-Bashkir #language-Kazakh #language-Kirghiz #region-us
|
# Triplets for Turkic languages language models
## Description
This dataset is designed to test models for working with Next Sentence Prediction (NSP) and Sentence Order Prediction (SOP). It includes two sub-sets with triplets of texts..
## Usage
This dataset can be used to train and evaluate models capable of performing NSP and SAP tasks.
## Dataset structure:
Each entry in the dataset represents three values:
- text: a triplet of text;
- flag: a flag indicating whether the order of sentences in this triplet is correct;
- lang: the language of the sentence.
## The creation process
Using the functions described on github preprocessing and analysis of texts from dataset was performed and triplets are selected according to certain rules (triplets should be approximately the same length and from 30 to 100 characters).
Also, the triplets were selected in such a way that no sentence displayed in the dataset was repeated several times. | [
"# Triplets for Turkic languages language models",
"## Description\nThis dataset is designed to test models for working with Next Sentence Prediction (NSP) and Sentence Order Prediction (SOP). It includes two sub-sets with triplets of texts..",
"## Usage\nThis dataset can be used to train and evaluate models capable of performing NSP and SAP tasks.",
"## Dataset structure:\nEach entry in the dataset represents three values:\n- text: a triplet of text;\n- flag: a flag indicating whether the order of sentences in this triplet is correct;\n- lang: the language of the sentence.",
"## The creation process\nUsing the functions described on github preprocessing and analysis of texts from dataset was performed and triplets are selected according to certain rules (triplets should be approximately the same length and from 30 to 100 characters).\nAlso, the triplets were selected in such a way that no sentence displayed in the dataset was repeated several times."
] | [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Bashkir #language-Kazakh #language-Kirghiz #region-us \n",
"# Triplets for Turkic languages language models",
"## Description\nThis dataset is designed to test models for working with Next Sentence Prediction (NSP) and Sentence Order Prediction (SOP). It includes two sub-sets with triplets of texts..",
"## Usage\nThis dataset can be used to train and evaluate models capable of performing NSP and SAP tasks.",
"## Dataset structure:\nEach entry in the dataset represents three values:\n- text: a triplet of text;\n- flag: a flag indicating whether the order of sentences in this triplet is correct;\n- lang: the language of the sentence.",
"## The creation process\nUsing the functions described on github preprocessing and analysis of texts from dataset was performed and triplets are selected according to certain rules (triplets should be approximately the same length and from 30 to 100 characters).\nAlso, the triplets were selected in such a way that no sentence displayed in the dataset was repeated several times."
] |
154252c11a52e683b5ca629da51e3bf577efe911 |
# Dataset of norn (Fire Emblem)
This is the dataset of norn (Fire Emblem), containing 56 images and their tags.
The core tags of this character are `red_hair, breasts, green_eyes, hair_ornament, short_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 56 | 71.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/norn_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 56 | 42.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/norn_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 125 | 84.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/norn_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 56 | 65.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/norn_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 125 | 116.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/norn_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/norn_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, bow_(weapon), arrow_(projectile), pink_hair, quiver, solo, belt, cape, open_mouth, ponytail, smile, thigh_boots, shoulder_armor, simple_background, white_background, white_thighhighs, zettai_ryouiki |
| 1 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, bangs, blush, navel, necklace, pink_bikini, smile, solo, thigh_strap, bare_shoulders, collarbone, outdoors, bracelet, cleavage, long_hair, ocean, official_alternate_costume, short_ponytail, sitting, skirt, water |
| 2 | 7 |  |  |  |  |  | nipples, 1girl, blush, navel, open_mouth, solo, looking_at_viewer, medium_breasts, completely_nude, large_breasts, smile, pussy, sweat |
| 3 | 10 |  |  |  |  |  | 1girl, blush, open_mouth, hetero, 1boy, large_breasts, nipples, sex, solo_focus, vaginal, clothing_aside, cum, mosaic_censoring, clothes_lift, handjob, jewelry, multiple_penises, navel, on_back, pink_bikini, pussy |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bow_(weapon) | arrow_(projectile) | pink_hair | quiver | solo | belt | cape | open_mouth | ponytail | smile | thigh_boots | shoulder_armor | simple_background | white_background | white_thighhighs | zettai_ryouiki | looking_at_viewer | bangs | blush | navel | necklace | pink_bikini | thigh_strap | bare_shoulders | collarbone | outdoors | bracelet | cleavage | long_hair | ocean | official_alternate_costume | short_ponytail | sitting | skirt | water | nipples | medium_breasts | completely_nude | large_breasts | pussy | sweat | hetero | 1boy | sex | solo_focus | vaginal | clothing_aside | cum | mosaic_censoring | clothes_lift | handjob | jewelry | multiple_penises | on_back |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:---------------------|:------------|:---------|:-------|:-------|:-------|:-------------|:-----------|:--------|:--------------|:-----------------|:--------------------|:-------------------|:-------------------|:-----------------|:--------------------|:--------|:--------|:--------|:-----------|:--------------|:--------------|:-----------------|:-------------|:-----------|:-----------|:-----------|:------------|:--------|:-----------------------------|:-----------------|:----------|:--------|:--------|:----------|:-----------------|:------------------|:----------------|:--------|:--------|:---------|:-------|:------|:-------------|:----------|:-----------------|:------|:-------------------|:---------------|:----------|:----------|:-------------------|:----------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | | | | X | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | | | | X | | | X | | X | | | | | | | X | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | | | | | | | | X | | | | | | | | | | | X | X | | X | | | | | | | | | | | | | | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/norn_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:39:25+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:52:20+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of norn (Fire Emblem)
=============================
This is the dataset of norn (Fire Emblem), containing 56 images and their tags.
The core tags of this character are 'red\_hair, breasts, green\_eyes, hair\_ornament, short\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
93af2134510d47dcf951809723631238278e8b85 |
# Dataset of cynthia (Fire Emblem)
This is the dataset of cynthia (Fire Emblem), containing 60 images and their tags.
The core tags of this character are `twintails, brown_hair, brown_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 60 | 64.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cynthia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 60 | 36.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cynthia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 130 | 74.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cynthia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 60 | 57.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cynthia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 130 | 105.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cynthia_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/cynthia_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 25 |  |  |  |  |  | 1girl, smile, solo, open_mouth, gloves, looking_at_viewer, breastplate, blush, thighhighs, shoulder_armor, simple_background, garter_straps, weapon |
| 1 | 12 |  |  |  |  |  | 1girl, hetero, penis, blush, nipples, solo_focus, 1boy, thighhighs, open_mouth, sex_from_behind, vaginal, elbow_gloves, large_breasts, navel, nude, boots, cum_in_pussy, medium_breasts, spread_legs, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | open_mouth | gloves | looking_at_viewer | breastplate | blush | thighhighs | shoulder_armor | simple_background | garter_straps | weapon | hetero | penis | nipples | solo_focus | 1boy | sex_from_behind | vaginal | elbow_gloves | large_breasts | navel | nude | boots | cum_in_pussy | medium_breasts | spread_legs | uncensored |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-------------|:---------|:--------------------|:--------------|:--------|:-------------|:-----------------|:--------------------|:----------------|:---------|:---------|:--------|:----------|:-------------|:-------|:------------------|:----------|:---------------|:----------------|:--------|:-------|:--------|:---------------|:-----------------|:--------------|:-------------|
| 0 | 25 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 12 |  |  |  |  |  | X | | | X | | | | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/cynthia_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:39:36+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T23:03:59+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of cynthia (Fire Emblem)
================================
This is the dataset of cynthia (Fire Emblem), containing 60 images and their tags.
The core tags of this character are 'twintails, brown\_hair, brown\_eyes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
a024e2da2c2b5a9a7d5ea64d2dd4a938cf063f21 |
# Dataset of elfie (Fire Emblem)
This is the dataset of elfie (Fire Emblem), containing 56 images and their tags.
The core tags of this character are `green_eyes, grey_hair, hair_bun, single_hair_bun, breasts, hair_between_eyes, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 56 | 57.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elfie_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 56 | 36.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elfie_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 118 | 69.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elfie_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 56 | 52.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elfie_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 118 | 94.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elfie_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/elfie_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, solo, simple_background, braid, looking_at_viewer, upper_body, smile, artist_name, breastplate, shoulder_armor, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | simple_background | braid | looking_at_viewer | upper_body | smile | artist_name | breastplate | shoulder_armor | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:--------------------|:-------------|:--------|:--------------|:--------------|:-----------------|:-------------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/elfie_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:40:09+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:53:27+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of elfie (Fire Emblem)
==============================
This is the dataset of elfie (Fire Emblem), containing 56 images and their tags.
The core tags of this character are 'green\_eyes, grey\_hair, hair\_bun, single\_hair\_bun, breasts, hair\_between\_eyes, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
bc7ea1709965515c5c408b09f447d10cf41967f1 |
# Dataset of mirabilis (Fire Emblem)
This is the dataset of mirabilis (Fire Emblem), containing 21 images and their tags.
The core tags of this character are `long_hair, pointy_ears, pink_hair, fairy_wings, wings, purple_eyes, hair_ornament, bangs, hair_flower, very_long_hair, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 21 | 31.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirabilis_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 21 | 15.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirabilis_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 51 | 33.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirabilis_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 21 | 26.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirabilis_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 51 | 49.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mirabilis_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mirabilis_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, flower, solo, fairy, looking_at_viewer, dress, long_sleeves, simple_background, white_background, wide_sleeves, blush, open_mouth, pink_pantyhose, boots, sleeves_past_fingers |
| 1 | 7 |  |  |  |  |  | 1girl, solo, detached_sleeves, dress, fur_trim, looking_at_viewer, smile, bare_shoulders, multicolored_hair, christmas, fairy, pantyhose, sleeves_past_fingers, closed_mouth, flower, low-tied_long_hair, pom_pom_(clothes), red_footwear, santa_hat, snowman, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | flower | solo | fairy | looking_at_viewer | dress | long_sleeves | simple_background | white_background | wide_sleeves | blush | open_mouth | pink_pantyhose | boots | sleeves_past_fingers | detached_sleeves | fur_trim | smile | bare_shoulders | multicolored_hair | christmas | pantyhose | closed_mouth | low-tied_long_hair | pom_pom_(clothes) | red_footwear | santa_hat | snowman |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------|:--------|:--------------------|:--------|:---------------|:--------------------|:-------------------|:---------------|:--------|:-------------|:-----------------|:--------|:-----------------------|:-------------------|:-----------|:--------|:-----------------|:--------------------|:------------|:------------|:---------------|:---------------------|:--------------------|:---------------|:------------|:----------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/mirabilis_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-17T22:40:49+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-17T22:46:35+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of mirabilis (Fire Emblem)
==================================
This is the dataset of mirabilis (Fire Emblem), containing 21 images and their tags.
The core tags of this character are 'long\_hair, pointy\_ears, pink\_hair, fairy\_wings, wings, purple\_eyes, hair\_ornament, bangs, hair\_flower, very\_long\_hair, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.