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# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'ramsi-k/LunarLander-v2-fromscratch'
'batch_size': 512
'minibatch_size': 128}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-117.87 +/- 48.29", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ramsi-k/LunarLander-v2-fromscratch | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | 2024-02-07T09:38:01+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
51,
37
] | [
"passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mehassan/finbert-finetuned
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1000
- Train Accuracy: 0.9868
- Validation Loss: 0.2368
- Validation Accuracy: 0.9051
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.8773 | 0.6523 | 0.5521 | 0.9073 | 0 |
| 0.4449 | 0.9294 | 0.3423 | 0.9227 | 1 |
| 0.2584 | 0.9581 | 0.2543 | 0.9205 | 2 |
| 0.1562 | 0.9790 | 0.2481 | 0.9139 | 3 |
| 0.1000 | 0.9868 | 0.2368 | 0.9051 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_keras_callback"], "base_model": "ProsusAI/finbert", "model-index": [{"name": "mehassan/finbert-finetuned", "results": []}]} | text-classification | mehassan/finbert-finetuned | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:ProsusAI/finbert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T09:38:46+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-ProsusAI/finbert #autotrain_compatible #endpoints_compatible #region-us
| mehassan/finbert-finetuned
==========================
This model is a fine-tuned version of ProsusAI/finbert on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1000
* Train Accuracy: 0.9868
* Validation Loss: 0.2368
* Validation Accuracy: 0.9051
* Epoch: 4
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 1e-05, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-ProsusAI/finbert #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 1e-05, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
57,
196,
4,
31
] | [
"passage: TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-ProsusAI/finbert #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 1e-05, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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] |
null | null | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.001
'num_envs': 16
'num_steps': 64
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'ramsi-k/LunarLander-v2-fromscratch-tuned'
'batch_size': 1024
'minibatch_size': 256}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-41.86 +/- 44.17", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ramsi-k/LunarLander-v2-fromscratch-tuned | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | 2024-02-07T09:38:55+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
51,
37
] | [
"passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "258.16 +/- 20.85", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | JiajingChen/a | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T09:43:23+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Abelmoschus esculentus leaves
#### Cannabis sativa leaves
#### Crotalaria juncea leaves
#### Jatropha multifida leaves
#### Tagetes minuta leaves
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | aanaya/rare-puppers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T09:46:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Abelmoschus esculentus leaves
!Abelmoschus esculentus leaves
#### Cannabis sativa leaves
!Cannabis sativa leaves
#### Crotalaria juncea leaves
!Crotalaria juncea leaves
#### Jatropha multifida leaves
!Jatropha multifida leaves
#### Tagetes minuta leaves
!Tagetes minuta leaves | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Abelmoschus esculentus leaves\n\n!Abelmoschus esculentus leaves",
"#### Cannabis sativa leaves\n\n!Cannabis sativa leaves",
"#### Crotalaria juncea leaves\n\n!Crotalaria juncea leaves",
"#### Jatropha multifida leaves\n\n!Jatropha multifida leaves",
"#### Tagetes minuta leaves\n\n!Tagetes minuta leaves"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Abelmoschus esculentus leaves\n\n!Abelmoschus esculentus leaves",
"#### Cannabis sativa leaves\n\n!Cannabis sativa leaves",
"#### Crotalaria juncea leaves\n\n!Crotalaria juncea leaves",
"#### Jatropha multifida leaves\n\n!Jatropha multifida leaves",
"#### Tagetes minuta leaves\n\n!Tagetes minuta leaves"
] | [
54,
44,
4,
22,
15,
18,
19,
13
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### Abelmoschus esculentus leaves\n\n!Abelmoschus esculentus leaves#### Cannabis sativa leaves\n\n!Cannabis sativa leaves#### Crotalaria juncea leaves\n\n!Crotalaria juncea leaves#### Jatropha multifida leaves\n\n!Jatropha multifida leaves#### Tagetes minuta leaves\n\n!Tagetes minuta leaves"
] | [
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] |
null | null | peft |
# Model Card for Model ID
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- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-base"} | null | HeydarS/flan-t5-base_peft_v21 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-base",
"region:us"
] | 2024-02-07T09:50:31+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us
|
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## Model Card Contact
### Framework versions
- PEFT 0.7.2.dev0 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.2.dev0"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.2.dev0"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.2.dev0"
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null | null | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.001
'num_envs': 64
'num_steps': 32
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'ramsi-k/LunarLander-v2-fromscratch-tune'
'batch_size': 2048
'minibatch_size': 512}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-194.56 +/- 121.41", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ramsi-k/LunarLander-v2-fromscratch-tune | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | 2024-02-07T09:51:41+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
51,
37
] | [
"passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotions-fp16
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3314
- Accuracy: 0.9287
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 50 | 1.7532 | 0.4263 |
| No log | 2.0 | 100 | 1.4569 | 0.535 |
| No log | 3.0 | 150 | 1.3329 | 0.5262 |
| No log | 4.0 | 200 | 1.1306 | 0.6475 |
| No log | 5.0 | 250 | 1.0279 | 0.7275 |
| No log | 6.0 | 300 | 0.8815 | 0.7863 |
| No log | 7.0 | 350 | 0.7592 | 0.8337 |
| No log | 8.0 | 400 | 0.7329 | 0.785 |
| No log | 9.0 | 450 | 0.6043 | 0.875 |
| 1.1234 | 10.0 | 500 | 0.5688 | 0.8612 |
| 1.1234 | 11.0 | 550 | 0.5193 | 0.88 |
| 1.1234 | 12.0 | 600 | 0.4879 | 0.8938 |
| 1.1234 | 13.0 | 650 | 0.4170 | 0.9038 |
| 1.1234 | 14.0 | 700 | 0.4425 | 0.8912 |
| 1.1234 | 15.0 | 750 | 0.4089 | 0.905 |
| 1.1234 | 16.0 | 800 | 0.3781 | 0.9263 |
| 1.1234 | 17.0 | 850 | 0.3431 | 0.9225 |
| 1.1234 | 18.0 | 900 | 0.3388 | 0.93 |
| 1.1234 | 19.0 | 950 | 0.2973 | 0.9475 |
| 0.3972 | 20.0 | 1000 | 0.3314 | 0.9287 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-emotions-fp16", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.92875, "name": "Accuracy"}]}]}]} | image-classification | silvering/vit-emotions-classification-fp16 | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T09:52:00+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-emotions-fp16
=================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3314
* Accuracy: 0.9287
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_RTSplit0207_7
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0252
- Wer: 0.2066
- Cer: 0.1485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.937 | 1.0 | 120 | 3.6078 | 1.0 | 0.9491 |
| 1.5117 | 2.0 | 240 | 1.2167 | 0.9885 | 0.7172 |
| 0.813 | 3.0 | 360 | 0.6808 | 0.8188 | 0.5068 |
| 0.6475 | 4.0 | 480 | 0.5831 | 0.8203 | 0.5502 |
| 0.5565 | 5.0 | 600 | 0.4374 | 0.7080 | 0.4328 |
| 0.4047 | 6.0 | 720 | 0.2611 | 0.4574 | 0.2654 |
| 0.273 | 7.0 | 840 | 0.1266 | 0.2999 | 0.1602 |
| 0.2089 | 8.0 | 960 | 0.0686 | 0.2518 | 0.1503 |
| 0.1735 | 9.0 | 1080 | 0.0345 | 0.2144 | 0.1645 |
| 0.1029 | 10.0 | 1200 | 0.0252 | 0.2066 | 0.1485 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0207_7", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0207_7 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T09:54:14+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0207\_7
=======================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0252
* Wer: 0.2066
* Cer: 0.1485
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
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] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LazarusNLP/IndoNanoT5-base-Liputan6-Canonical
This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on the indonlg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1194
- Rouge1: 0.3976
- Rouge2: 0.2229
- Rougel: 0.3346
- Rougelsum: 0.3345
- Gen Len: 43.3808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.9693 | 1.0 | 24236 | 1.3245 | 0.3082 | 0.1585 | 0.2687 | 0.2688 | 18.9956 |
| 0.9338 | 2.0 | 48472 | 1.2759 | 0.3105 | 0.159 | 0.2705 | 0.2706 | 18.9985 |
| 0.8632 | 3.0 | 72708 | 1.2698 | 0.3094 | 0.1586 | 0.2701 | 0.2702 | 18.9995 |
| 0.8257 | 4.0 | 96944 | 1.2631 | 0.312 | 0.1603 | 0.2716 | 0.2715 | 18.9993 |
| 0.7789 | 5.0 | 121180 | 1.2642 | 0.3149 | 0.1625 | 0.2748 | 0.2747 | 18.9998 |
| 0.7595 | 6.0 | 145416 | 1.2587 | 0.3202 | 0.1658 | 0.279 | 0.2791 | 18.9995 |
| 0.7343 | 7.0 | 169652 | 1.2644 | 0.3183 | 0.1647 | 0.2773 | 0.2773 | 18.9996 |
| 0.7165 | 8.0 | 193888 | 1.2635 | 0.3141 | 0.1605 | 0.2732 | 0.2732 | 18.9993 |
| 0.6697 | 9.0 | 218124 | 1.2856 | 0.316 | 0.162 | 0.275 | 0.275 | 18.9998 |
| 0.6729 | 10.0 | 242360 | 1.2809 | 0.3195 | 0.164 | 0.2775 | 0.2776 | 18.9992 |
| 0.6471 | 11.0 | 266596 | 1.2833 | 0.3185 | 0.1636 | 0.2769 | 0.277 | 18.9982 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"language": ["ind"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["GEM/indonlg"], "metrics": ["rouge"], "base_model": "LazarusNLP/IndoNanoT5-base", "model-index": [{"name": "IndoNanoT5-base-Liputan6-Canonical", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "indonlg", "type": "indonlg", "config": "liputan6_canonical", "split": "test", "args": "liputan6_canonical"}, "metrics": [{"type": "rouge", "value": 0.3976, "name": "Rouge1"}, {"type": "rouge", "value": 0.2229, "name": "Rouge2"}, {"type": "rouge", "value": 0.3346, "name": "RougeL"}]}, {"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "indonlg", "type": "indonlg", "config": "liputan6_extreme", "split": "test", "args": "liputan6_extreme"}, "metrics": [{"type": "rouge", "value": 0.3323, "name": "Rouge1"}, {"type": "rouge", "value": 0.1417, "name": "Rouge2"}, {"type": "rouge", "value": 0.2621, "name": "RougeL"}]}]}]} | text2text-generation | LazarusNLP/IndoNanoT5-base-Liputan6-Canonical | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"ind",
"dataset:GEM/indonlg",
"base_model:LazarusNLP/IndoNanoT5-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T09:58:47+00:00 | [] | [
"ind"
] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #ind #dataset-GEM/indonlg #base_model-LazarusNLP/IndoNanoT5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| LazarusNLP/IndoNanoT5-base-Liputan6-Canonical
=============================================
This model is a fine-tuned version of LazarusNLP/IndoNanoT5-base on the indonlg dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1194
* Rouge1: 0.3976
* Rouge2: 0.2229
* Rougel: 0.3346
* Rougelsum: 0.3345
* Gen Len: 43.3808
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu118
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #ind #dataset-GEM/indonlg #base_model-LazarusNLP/IndoNanoT5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
101,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #ind #dataset-GEM/indonlg #base_model-LazarusNLP/IndoNanoT5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | diffusers |
# AnySomniumXL v3.5,1 Model Showcase
<p align="center">
<img src="01.png" width=70% height=70%>
</p>
`Ketengan-Diffusion/AnySomniumXL v3.5` is a SDXL model that has been fine-tuned on [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
This is enhanced version of AnySomniumXL v3
# Changelog over AnySomniumXL v3.5
* More epochs training
* Better model generalizing
* More increased concept and character accuracy
# Our Dataset Process Curation
<p align="center">
<img src="Curation.png" width=70% height=70%>
</p>
Image source: [Source1](https://danbooru.donmai.us/posts/3143351) [Source2](https://danbooru.donmai.us/posts/3272710) [Source3](https://danbooru.donmai.us/posts/3320417)
Our dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by https://github.com/christophschuhmann/improved-aesthetic-predictor, and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract
This scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted
The dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images.
# Captioning process
We using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM.
This captioning process to captioning 133k images takes about 6 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB
# Tagging Process
We simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate.
# Official Demo
You can try our AnySomniumXL v3 for free on demo.ketengan.com
# Training Process
AnySomniumXL v3.5 Technical Specifications:
Batch Size: 25
Learning rate: 2e-6
Trained with a bucket size of 1280x1280
Shuffle Caption: Yes
Clip Skip: 2
Trained with 2x NVIDIA A100 80GB
# Recommended Resolution
Because it's trained with 1280x1280 resolution, so here the best resolution to get the full power of AnySomniumXL v3
* 1280x1280
* 1472x1088
* 1152x1408
* 1536x1024
* 1856x832
* 1024x1600
You can support me:
- on [Ko-FI](https://ko-fi.com/ncaix) | {"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "SDXL", "art", "stable-diffusion-XL", "fantasy", "anime", "aiart", "ketengan", "AnySomniumXL"], "pipeline_tag": "text-to-image"} | text-to-image | Ketengan-Diffusion/AnySomniumXL-v3.5.1 | [
"diffusers",
"safetensors",
"stable-diffusion",
"SDXL",
"art",
"stable-diffusion-XL",
"fantasy",
"anime",
"aiart",
"ketengan",
"AnySomniumXL",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2024-02-07T10:01:48+00:00 | [] | [
"en"
] | TAGS
#diffusers #safetensors #stable-diffusion #SDXL #art #stable-diffusion-XL #fantasy #anime #aiart #ketengan #AnySomniumXL #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us
|
# AnySomniumXL v3.5,1 Model Showcase
<p align="center">
<img src="URL" width=70% height=70%>
</p>
'Ketengan-Diffusion/AnySomniumXL v3.5' is a SDXL model that has been fine-tuned on stabilityai/stable-diffusion-xl-base-1.0.
This is enhanced version of AnySomniumXL v3
# Changelog over AnySomniumXL v3.5
* More epochs training
* Better model generalizing
* More increased concept and character accuracy
# Our Dataset Process Curation
<p align="center">
<img src="URL" width=70% height=70%>
</p>
Image source: Source1 Source2 Source3
Our dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract
This scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted
The dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images.
# Captioning process
We using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM.
This captioning process to captioning 133k images takes about 6 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB
# Tagging Process
We simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate.
# Official Demo
You can try our AnySomniumXL v3 for free on URL
# Training Process
AnySomniumXL v3.5 Technical Specifications:
Batch Size: 25
Learning rate: 2e-6
Trained with a bucket size of 1280x1280
Shuffle Caption: Yes
Clip Skip: 2
Trained with 2x NVIDIA A100 80GB
# Recommended Resolution
Because it's trained with 1280x1280 resolution, so here the best resolution to get the full power of AnySomniumXL v3
* 1280x1280
* 1472x1088
* 1152x1408
* 1536x1024
* 1856x832
* 1024x1600
You can support me:
- on Ko-FI | [
"# AnySomniumXL v3.5,1 Model Showcase\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\n'Ketengan-Diffusion/AnySomniumXL v3.5' is a SDXL model that has been fine-tuned on stabilityai/stable-diffusion-xl-base-1.0.\n\nThis is enhanced version of AnySomniumXL v3",
"# Changelog over AnySomniumXL v3.5\n* More epochs training\n* Better model generalizing\n* More increased concept and character accuracy",
"# Our Dataset Process Curation\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\nImage source: Source1 Source2 Source3\n\nOur dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract\n\nThis scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted\n\nThe dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images.",
"# Captioning process\nWe using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM.\n\nThis captioning process to captioning 133k images takes about 6 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB",
"# Tagging Process\nWe simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate.",
"# Official Demo\nYou can try our AnySomniumXL v3 for free on URL",
"# Training Process\n\nAnySomniumXL v3.5 Technical Specifications:\n\nBatch Size: 25\n\nLearning rate: 2e-6\n\nTrained with a bucket size of 1280x1280\n\nShuffle Caption: Yes\n\nClip Skip: 2\n\nTrained with 2x NVIDIA A100 80GB",
"# Recommended Resolution\nBecause it's trained with 1280x1280 resolution, so here the best resolution to get the full power of AnySomniumXL v3\n* 1280x1280\n* 1472x1088\n* 1152x1408\n* 1536x1024\n* 1856x832\n* 1024x1600\n\nYou can support me: \n- on Ko-FI"
] | [
"TAGS\n#diffusers #safetensors #stable-diffusion #SDXL #art #stable-diffusion-XL #fantasy #anime #aiart #ketengan #AnySomniumXL #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n",
"# AnySomniumXL v3.5,1 Model Showcase\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\n'Ketengan-Diffusion/AnySomniumXL v3.5' is a SDXL model that has been fine-tuned on stabilityai/stable-diffusion-xl-base-1.0.\n\nThis is enhanced version of AnySomniumXL v3",
"# Changelog over AnySomniumXL v3.5\n* More epochs training\n* Better model generalizing\n* More increased concept and character accuracy",
"# Our Dataset Process Curation\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\nImage source: Source1 Source2 Source3\n\nOur dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract\n\nThis scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted\n\nThe dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images.",
"# Captioning process\nWe using combination of proprietary Multimodal LLM and open source multimodal LLM such as LLaVa 1.5 as the captioning process which is resulting more complex result than using normal BLIP2. Any detail like the clothes, atmosphere, situation, scene, place, gender, skin, and others is generated by LLM.\n\nThis captioning process to captioning 133k images takes about 6 Days with NVIDIA Tesla A100 80GB PCIe. We still improving our script to generate caption faster. The minimum VRAM that required for this captioning process is 24GB VRAM which is not sufficient if we using NVIDIA Tesla T4 16GB",
"# Tagging Process\nWe simply using booru tags, that retrieved from booru boards so this could be tagged by manually by human hence make this tags more accurate.",
"# Official Demo\nYou can try our AnySomniumXL v3 for free on URL",
"# Training Process\n\nAnySomniumXL v3.5 Technical Specifications:\n\nBatch Size: 25\n\nLearning rate: 2e-6\n\nTrained with a bucket size of 1280x1280\n\nShuffle Caption: Yes\n\nClip Skip: 2\n\nTrained with 2x NVIDIA A100 80GB",
"# Recommended Resolution\nBecause it's trained with 1280x1280 resolution, so here the best resolution to get the full power of AnySomniumXL v3\n* 1280x1280\n* 1472x1088\n* 1152x1408\n* 1536x1024\n* 1856x832\n* 1024x1600\n\nYou can support me: \n- on Ko-FI"
] | [
97,
101,
32,
184,
140,
38,
18,
59,
80
] | [
"passage: TAGS\n#diffusers #safetensors #stable-diffusion #SDXL #art #stable-diffusion-XL #fantasy #anime #aiart #ketengan #AnySomniumXL #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n# AnySomniumXL v3.5,1 Model Showcase\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\n'Ketengan-Diffusion/AnySomniumXL v3.5' is a SDXL model that has been fine-tuned on stabilityai/stable-diffusion-xl-base-1.0.\n\nThis is enhanced version of AnySomniumXL v3# Changelog over AnySomniumXL v3.5\n* More epochs training\n* Better model generalizing\n* More increased concept and character accuracy# Our Dataset Process Curation\n<p align=\"center\">\n <img src=\"URL\" width=70% height=70%>\n</p>\n\nImage source: Source1 Source2 Source3\n\nOur dataset is scored using Pretrained CLIP+MLP Aesthetic Scoring model by URL and We made adjusment into our script to detecting any text or watermark by utilizing OCR by pytesseract\n\nThis scoring method has scale between -1-100, we take the score threshold around 17 or 20 as minimum and 65-75 as maximum to pretain the 2D style of the dataset, Any images with text will returning -1 score. So any images with score below 17 or above 65 is deleted\n\nThe dataset curation proccess is using Nvidia T4 16GB Machine and takes about 7 days for curating 1.000.000 images."
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] |
null | null | diffusers |
# BRIA 2.2 ControlNet Canny Model Card
[***Click here for Demo***](https://huggingface.co/spaces/briaai/BRIA-2.2-ControlNets)
BRIA 2.2 ControlNet-Canny, trained on the foundation of [BRIA 2.2 Text-to-Image](https://huggingface.co/briaai/BRIA-2.2), enables the generation of high-quality images guided by a textual prompt and the extracted edge map from an input image. This allows for the creation of different variations of an image, all sharing the same geometry.
[BRIA 2.2](https://huggingface.co/briaai/BRIA-2.2) was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
### Model Description
- **Developed by:** BRIA AI
- **Model type:** [ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet) for Latent diffusion
- **License:** [bria-2.2](https://bria.ai/bria-huggingface-model-license-agreement/)
- **Model Description:** ControlNet Canny for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the edge map of the conditioned image.
- **Resources for more information:** [BRIA AI](https://bria.ai/)
### Get Access
BRIA 2.2 ControlNet-Canny requires access to BRIA 2.2 Text-to-Image. For more information, [click here](https://huggingface.co/briaai/BRIA-2.2).
### Code example using Diffusers
```
pip install diffusers
```
```py
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
import torch
controlnet = ControlNetModel.from_pretrained(
"briaai/BRIA-2.2-ControlNet-Canny",
torch_dtype=torch.float16
)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"briaai/BRIA-2.2",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.to("cuda")
prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background"
negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
# Calculate Canny image
input_image = cv2.imread('pics/singer.png')
input_image = cv2.Canny(input_image, low_threshold, high_threshold)
input_image = input_image[:, :, None]
input_image = np.concatenate([input_image, input_image, input_image], axis=2)
canny_image = Image.fromarray(image)
image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=canny_image, controlnet_conditioning_scale=1.0, height=1024, width=1024).images[0]
``` | {"license": "other", "tags": ["text-to-image", "controlnet model", "legal liability", "commercial use"], "license_name": "bria-2.2", "license_link": "https://bria.ai/customer-general-terms-and-conditions", "inference": false, "extra_gated_prompt": "This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}} | text-to-image | briaai/BRIA-2.2-ControlNet-Canny | [
"diffusers",
"text-to-image",
"controlnet model",
"legal liability",
"commercial use",
"license:other",
"has_space",
"diffusers:ControlNetModel",
"region:us"
] | 2024-02-07T10:04:03+00:00 | [] | [] | TAGS
#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us
|
# BRIA 2.2 ControlNet Canny Model Card
*Click here for Demo*
BRIA 2.2 ControlNet-Canny, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted edge map from an input image. This allows for the creation of different variations of an image, all sharing the same geometry.
BRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
!photo-4426232_collage.png
### Model Description
- Developed by: BRIA AI
- Model type: ControlNet for Latent diffusion
- License: bria-2.2
- Model Description: ControlNet Canny for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the edge map of the conditioned image.
- Resources for more information: BRIA AI
### Get Access
BRIA 2.2 ControlNet-Canny requires access to BRIA 2.2 Text-to-Image. For more information, click here.
### Code example using Diffusers
| [
"# BRIA 2.2 ControlNet Canny Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Canny, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted edge map from an input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!photo-4426232_collage.png",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Canny for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the edge map of the conditioned image.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.2 ControlNet-Canny requires access to BRIA 2.2 Text-to-Image. For more information, click here.",
"### Code example using Diffusers"
] | [
"TAGS\n#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us \n",
"# BRIA 2.2 ControlNet Canny Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Canny, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted edge map from an input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!photo-4426232_collage.png",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Canny for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the edge map of the conditioned image.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.2 ControlNet-Canny requires access to BRIA 2.2 Text-to-Image. For more information, click here.",
"### Code example using Diffusers"
] | [
46,
196,
78,
31,
8
] | [
"passage: TAGS\n#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us \n# BRIA 2.2 ControlNet Canny Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Canny, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted edge map from an input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!photo-4426232_collage.png### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Canny for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the edge map of the conditioned image.\n- Resources for more information: BRIA AI### Get Access\nBRIA 2.2 ControlNet-Canny requires access to BRIA 2.2 Text-to-Image. For more information, click here.### Code example using Diffusers"
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Arozhada -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Arozhada -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Arozhada
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "660.00 +/- 215.20", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Arozhada/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T10:07:40+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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] |
null | null | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ramsi-k/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | reinforcement-learning | ramsi-k/poca-SoccerTwos | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | 2024-02-07T10:11:56+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us
|
# poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: ramsi-k/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: ramsi-k/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n",
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: ramsi-k/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
52,
206
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: ramsi-k/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "alexsherstinsky/Mistral-7B-v0.1-sharded"} | null | sajaw/AntModel-7B-XLLM-Demo-LoRA | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:alexsherstinsky/Mistral-7B-v0.1-sharded",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-alexsherstinsky/Mistral-7B-v0.1-sharded #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | peft |
# Model Card for Model ID
DPO qlora adapter for Navarna, refer to https://huggingface.co/TokenBender/navarna_hindi_merged for SFT qlora merged model.
And final DPO adapter merged model is - https://huggingface.co/TokenBender/navaran_hindi_dpo_merged | {"library_name": "peft", "base_model": "TokenBender/navaran_hindi_merged"} | null | TokenBender/navarna_dpo_qlora | [
"peft",
"safetensors",
"base_model:TokenBender/navaran_hindi_merged",
"region:us"
] | 2024-02-07T10:15:38+00:00 | [] | [] | TAGS
#peft #safetensors #base_model-TokenBender/navaran_hindi_merged #region-us
|
# Model Card for Model ID
DPO qlora adapter for Navarna, refer to URL for SFT qlora merged model.
And final DPO adapter merged model is - URL | [
"# Model Card for Model ID\n\nDPO qlora adapter for Navarna, refer to URL for SFT qlora merged model.\n\nAnd final DPO adapter merged model is - URL"
] | [
"TAGS\n#peft #safetensors #base_model-TokenBender/navaran_hindi_merged #region-us \n",
"# Model Card for Model ID\n\nDPO qlora adapter for Navarna, refer to URL for SFT qlora merged model.\n\nAnd final DPO adapter merged model is - URL"
] | [
31,
40
] | [
"passage: TAGS\n#peft #safetensors #base_model-TokenBender/navaran_hindi_merged #region-us \n# Model Card for Model ID\n\nDPO qlora adapter for Navarna, refer to URL for SFT qlora merged model.\n\nAnd final DPO adapter merged model is - URL"
] | [
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null | null | null | In today's digital landscape, the reliability, functionality, and performance of software are paramount to business success. At https://inoxoft.com/service/qa-consulting/, we specialize in revolutionizing your approach to testing, ensuring your products meet exemplary quality standards every step of the way. Our QA consulting services are designed to enhance efficiency, elevate user experience, and propel your business toward greater heights.
As an ISO 27001 certified company and esteemed Microsoft Gold Partner, Google Cloud Partner, ISTQB Silver Partner, and recognized member of Clutch Firms that Deliver and Pangea, we bring unparalleled expertise to every project. Proud members of the Lviv IT Cluster, we are committed to setting industry standards and exceeding client expectations.
Our comprehensive suite of Quality Assurance consulting services includes:
Test Engineering:
Our seasoned software QA consultants craft and implement robust testing frameworks tailored to your project's unique requirements. From identifying and addressing defects to verifying system performance, we cover all functional and non-functional aspects with precision.
Test Management:
Ensure seamless planning, execution, and delivery of QA activities throughout your project lifecycle. Our specialists align testing processes with your company goals, objectives, and quality standards, monitoring progress, and addressing issues proactively.
Test Governance & Compliance:
Navigating industries with stringent regulations such as healthcare, finance, and government, we define policies, procedures, and guidelines to ensure compliance. Our quality control measures mitigate risks and ensure timely addressing of compliance-related challenges.
QA Audit and Improvement:
We analyze your existing QA processes to identify areas for improvement, streamlining workflows, and enhancing efficiency. Leveraging automation and continuous integration practices, we optimize your testing processes for maximum efficacy.
Pre-certification QA:
Prepare your software products for certification and compliance with industry standards and regulations. Our comprehensive assessments, gap analyses, and mock audits ensure your solution meets the necessary criteria. | {} | null | reginaldcoghlan/qa | [
"region:us"
] | 2024-02-07T10:16:17+00:00 | [] | [] | TAGS
#region-us
| In today's digital landscape, the reliability, functionality, and performance of software are paramount to business success. At URL we specialize in revolutionizing your approach to testing, ensuring your products meet exemplary quality standards every step of the way. Our QA consulting services are designed to enhance efficiency, elevate user experience, and propel your business toward greater heights.
As an ISO 27001 certified company and esteemed Microsoft Gold Partner, Google Cloud Partner, ISTQB Silver Partner, and recognized member of Clutch Firms that Deliver and Pangea, we bring unparalleled expertise to every project. Proud members of the Lviv IT Cluster, we are committed to setting industry standards and exceeding client expectations.
Our comprehensive suite of Quality Assurance consulting services includes:
Test Engineering:
Our seasoned software QA consultants craft and implement robust testing frameworks tailored to your project's unique requirements. From identifying and addressing defects to verifying system performance, we cover all functional and non-functional aspects with precision.
Test Management:
Ensure seamless planning, execution, and delivery of QA activities throughout your project lifecycle. Our specialists align testing processes with your company goals, objectives, and quality standards, monitoring progress, and addressing issues proactively.
Test Governance & Compliance:
Navigating industries with stringent regulations such as healthcare, finance, and government, we define policies, procedures, and guidelines to ensure compliance. Our quality control measures mitigate risks and ensure timely addressing of compliance-related challenges.
QA Audit and Improvement:
We analyze your existing QA processes to identify areas for improvement, streamlining workflows, and enhancing efficiency. Leveraging automation and continuous integration practices, we optimize your testing processes for maximum efficacy.
Pre-certification QA:
Prepare your software products for certification and compliance with industry standards and regulations. Our comprehensive assessments, gap analyses, and mock audits ensure your solution meets the necessary criteria. | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | hingeankit/first | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-07T10:19:22+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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[optional]
BibTeX:
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers | # Targeted Sentiment Analysis model for Norwegian text
This model is a fine-tuned version of [ltg/norbert3-large](https://huggingface.co/ltg/norbert3-large) For Targeted Sentiment Analysis (TSA) on Norwegian text. The fine-tuning script is avaiable [on github](https://github.com/egilron/seq-label.git).
In TSA, we identify sentiment targets, "That what is spoken positively or negatively about" in each sentence. Our models performs the task through sequence labeling, AKA "token classification".
The dataset used for fine-tuning is [ltg/norec_tsa](https://huggingface.co/datasets/ltg/norec_tsa), at its defaul settings, were sentiment targets are labeled as either "targ-Positive" or "targ-Negative". The norec_tsa dataset is derived from the [NoReC_fine dataset](https://github.com/ltgoslo/norec_fine).
## Quick start
You can use this model in your scripts as follows:
```>>> from transformers import pipeline
>>> origin = "ltg/norbert3-large_TSA"
>>> trust_remote = "norbert3" in origin.lower()
>>> text = "Hans hese , litt såre stemme kler bluesen , men denne platen kommer neppe til å bli blant hans største kommersielle suksesser ."
>>> if trust_remote: # Downloads configurations for norbert3
... pipe = transformers.pipeline( "token-classification",
... aggregation_strategy='first',
... model = origin,
... trust_remote_code=trust_remote,
... tokenizer = AutoTokenizer.from_pretrained(origin)
... )
... preds = pipe(text)
... for p in preds:
... print(p)
{'entity_group': 'targ-Positive', 'score': 0.6990814, 'word': ' Hans hese , litt såre stemme', 'start': 0, 'end': 28}
{'entity_group': 'targ-Negative', 'score': 0.5721016, 'word': ' platen', 'start': 53, 'end': 60}
```
## Training hyperparameters
- per_device_train_batch_size: 64
- per_device_eval_batch_size: 8
- learning_rate: 1e-05
- gradient_accumulation_steps: 1
- num_train_epochs: 24 (best epoch 18)
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
## Evaluation
``` precision recall f1-score support
targ-Negative 0.4648 0.3143 0.3750 210
targ-Positive 0.5097 0.6019 0.5520 525
micro avg 0.5013 0.5197 0.5104 735
macro avg 0.4872 0.4581 0.4635 735
weighted avg 0.4969 0.5197 0.5014 735
``` | {"language": ["no", "nb", "nn"], "license": "cc-by-4.0", "pipeline_tag": "token-classification"} | token-classification | ltg/norbert3-large_TSA | [
"transformers",
"safetensors",
"token-classification",
"custom_code",
"no",
"nb",
"nn",
"license:cc-by-4.0",
"autotrain_compatible",
"region:us"
] | 2024-02-07T10:23:15+00:00 | [] | [
"no",
"nb",
"nn"
] | TAGS
#transformers #safetensors #token-classification #custom_code #no #nb #nn #license-cc-by-4.0 #autotrain_compatible #region-us
| # Targeted Sentiment Analysis model for Norwegian text
This model is a fine-tuned version of ltg/norbert3-large For Targeted Sentiment Analysis (TSA) on Norwegian text. The fine-tuning script is avaiable on github.
In TSA, we identify sentiment targets, "That what is spoken positively or negatively about" in each sentence. Our models performs the task through sequence labeling, AKA "token classification".
The dataset used for fine-tuning is ltg/norec_tsa, at its defaul settings, were sentiment targets are labeled as either "targ-Positive" or "targ-Negative". The norec_tsa dataset is derived from the NoReC_fine dataset.
## Quick start
You can use this model in your scripts as follows:
## Training hyperparameters
- per_device_train_batch_size: 64
- per_device_eval_batch_size: 8
- learning_rate: 1e-05
- gradient_accumulation_steps: 1
- num_train_epochs: 24 (best epoch 18)
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
## Evaluation
| [
"# Targeted Sentiment Analysis model for Norwegian text\nThis model is a fine-tuned version of ltg/norbert3-large For Targeted Sentiment Analysis (TSA) on Norwegian text. The fine-tuning script is avaiable on github. \nIn TSA, we identify sentiment targets, \"That what is spoken positively or negatively about\" in each sentence. Our models performs the task through sequence labeling, AKA \"token classification\". \n\nThe dataset used for fine-tuning is ltg/norec_tsa, at its defaul settings, were sentiment targets are labeled as either \"targ-Positive\" or \"targ-Negative\". The norec_tsa dataset is derived from the NoReC_fine dataset.",
"## Quick start\nYou can use this model in your scripts as follows:",
"## Training hyperparameters\n- per_device_train_batch_size: 64\n- per_device_eval_batch_size: 8\n- learning_rate: 1e-05\n- gradient_accumulation_steps: 1\n- num_train_epochs: 24 (best epoch 18)\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08",
"## Evaluation"
] | [
"TAGS\n#transformers #safetensors #token-classification #custom_code #no #nb #nn #license-cc-by-4.0 #autotrain_compatible #region-us \n",
"# Targeted Sentiment Analysis model for Norwegian text\nThis model is a fine-tuned version of ltg/norbert3-large For Targeted Sentiment Analysis (TSA) on Norwegian text. The fine-tuning script is avaiable on github. \nIn TSA, we identify sentiment targets, \"That what is spoken positively or negatively about\" in each sentence. Our models performs the task through sequence labeling, AKA \"token classification\". \n\nThe dataset used for fine-tuning is ltg/norec_tsa, at its defaul settings, were sentiment targets are labeled as either \"targ-Positive\" or \"targ-Negative\". The norec_tsa dataset is derived from the NoReC_fine dataset.",
"## Quick start\nYou can use this model in your scripts as follows:",
"## Training hyperparameters\n- per_device_train_batch_size: 64\n- per_device_eval_batch_size: 8\n- learning_rate: 1e-05\n- gradient_accumulation_steps: 1\n- num_train_epochs: 24 (best epoch 18)\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08",
"## Evaluation"
] | [
49,
179,
16,
94,
3
] | [
"passage: TAGS\n#transformers #safetensors #token-classification #custom_code #no #nb #nn #license-cc-by-4.0 #autotrain_compatible #region-us \n# Targeted Sentiment Analysis model for Norwegian text\nThis model is a fine-tuned version of ltg/norbert3-large For Targeted Sentiment Analysis (TSA) on Norwegian text. The fine-tuning script is avaiable on github. \nIn TSA, we identify sentiment targets, \"That what is spoken positively or negatively about\" in each sentence. Our models performs the task through sequence labeling, AKA \"token classification\". \n\nThe dataset used for fine-tuning is ltg/norec_tsa, at its defaul settings, were sentiment targets are labeled as either \"targ-Positive\" or \"targ-Negative\". The norec_tsa dataset is derived from the NoReC_fine dataset.## Quick start\nYou can use this model in your scripts as follows:## Training hyperparameters\n- per_device_train_batch_size: 64\n- per_device_eval_batch_size: 8\n- learning_rate: 1e-05\n- gradient_accumulation_steps: 1\n- num_train_epochs: 24 (best epoch 18)\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08## Evaluation"
] | [
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] |
null | null | peft |
# Model Card for Model ID
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "tags": [], "base_model": "facebook/bart-large-cnn"} | null | sanjeev-bhandari01/bart-nepali-summerization-tokenizer | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:facebook/bart-large-cnn",
"region:us"
] | 2024-02-07T10:23:28+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large-cnn #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.8.2 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large-cnn #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large-cnn #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2"
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null | null | null | ## Available Models
### Anzhc's Face segmentation:
Small segmentation model aiming to create accurate masks of face for improved inpainting quality with adetailer extension.
Full showcase can be found at: https://civitai.com/models/293448/anzhcs-face-segmentation-prototype-or-yolov8-or-adetailer-model
Particularly useful at close-ups, as standard YOLO Face burns your generation.
Also it improves details better, while not inpainting area around face itself.
| {"license": "agpl-3.0", "tags": ["YOLOv8", "art"]} | null | Anzhc/Anzhcs_YOLOs | [
"YOLOv8",
"art",
"license:agpl-3.0",
"region:us"
] | 2024-02-07T10:24:20+00:00 | [] | [] | TAGS
#YOLOv8 #art #license-agpl-3.0 #region-us
| ## Available Models
### Anzhc's Face segmentation:
Small segmentation model aiming to create accurate masks of face for improved inpainting quality with adetailer extension.
Full showcase can be found at: URL
Particularly useful at close-ups, as standard YOLO Face burns your generation.
Also it improves details better, while not inpainting area around face itself.
!image/png
!image/png
| [
"## Available Models",
"### Anzhc's Face segmentation: \nSmall segmentation model aiming to create accurate masks of face for improved inpainting quality with adetailer extension. \nFull showcase can be found at: URL \nParticularly useful at close-ups, as standard YOLO Face burns your generation. \nAlso it improves details better, while not inpainting area around face itself. \n\n!image/png\n\n!image/png"
] | [
"TAGS\n#YOLOv8 #art #license-agpl-3.0 #region-us \n",
"## Available Models",
"### Anzhc's Face segmentation: \nSmall segmentation model aiming to create accurate masks of face for improved inpainting quality with adetailer extension. \nFull showcase can be found at: URL \nParticularly useful at close-ups, as standard YOLO Face burns your generation. \nAlso it improves details better, while not inpainting area around face itself. \n\n!image/png\n\n!image/png"
] | [
21,
4,
90
] | [
"passage: TAGS\n#YOLOv8 #art #license-agpl-3.0 #region-us \n## Available Models### Anzhc's Face segmentation: \nSmall segmentation model aiming to create accurate masks of face for improved inpainting quality with adetailer extension. \nFull showcase can be found at: URL \nParticularly useful at close-ups, as standard YOLO Face burns your generation. \nAlso it improves details better, while not inpainting area around face itself. \n\n!image/png\n\n!image/png"
] | [
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "results", "results": []}]} | null | erfanvaredi/results | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-07T10:24:32+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us
|
# results
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.3\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us \n",
"# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.3\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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44
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us \n# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.3\n- num_epochs: 10### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | hingeankit/second | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
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] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
null | null | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r ramsi-k/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "10.28 +/- 4.04", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ramsi-k/rl_course_vizdoom_health_gathering_supreme | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T10:27:26+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_health_gathering_supreme environment.
This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL
## Downloading the model
After installing Sample-Factory, download the model with:
## Using the model
To run the model after download, use the 'enjoy' script corresponding to this environment:
You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details
## Training with this model
To continue training with this model, use the 'train' script corresponding to this environment:
Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.
| [
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
34,
19,
59,
67
] | [
"passage: TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n## Downloading the model\n\nAfter installing Sample-Factory, download the model with:## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
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null | null | pytorch |
# PVNet2
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
This model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in [this google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing).
- **Developed by:** openclimatefix
- **Model type:** Fusion model
- **Language(s) (NLP):** en
- **License:** mit
# Training Details
## Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details.
### Preprocessing
Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [2].
## Results
The training logs for the current model can be found [here on wandb](https://wandb.ai/openclimatefix/pvnet2.1/runs/None).
The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1).
Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing)
### Hardware
Trained on a single NVIDIA Tesla T4
### Software
- [1] https://github.com/openclimatefix/PVNet
- [2] https://github.com/openclimatefix/ocf_datapipes | {"language": "en", "license": "mit", "library_name": "pytorch"} | null | openclimatefix/windnet_india | [
"pytorch",
"en",
"license:mit",
"region:us"
] | 2024-02-07T10:28:22+00:00 | [] | [
"en"
] | TAGS
#pytorch #en #license-mit #region-us
|
# PVNet2
## Model Description
This model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.
- Developed by: openclimatefix
- Model type: Fusion model
- Language(s) (NLP): en
- License: mit
# Training Details
## Data
The model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.
### Preprocessing
Data is prepared with the 'ocf_datapipes.URL' datapipe [2].
## Results
The training logs for the current model can be found here on wandb.
The training logs for all model runs of PVNet2 can be found here.
Some experimental notes can be found at in the google doc
### Hardware
Trained on a single NVIDIA Tesla T4
### Software
- [1] URL
- [2] URL | [
"# PVNet2",
"## Model Description\n\n\nThis model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.\n\n- Developed by: openclimatefix\n- Model type: Fusion model\n- Language(s) (NLP): en\n- License: mit",
"# Training Details",
"## Data\n\n\n\nThe model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.",
"### Preprocessing\n\nData is prepared with the 'ocf_datapipes.URL' datapipe [2].",
"## Results\n\nThe training logs for the current model can be found here on wandb.\n\nThe training logs for all model runs of PVNet2 can be found here.\n\nSome experimental notes can be found at in the google doc",
"### Hardware\n\nTrained on a single NVIDIA Tesla T4",
"### Software\n\n- [1] URL\n- [2] URL"
] | [
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"# PVNet2",
"## Model Description\n\n\nThis model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.\n\n- Developed by: openclimatefix\n- Model type: Fusion model\n- Language(s) (NLP): en\n- License: mit",
"# Training Details",
"## Data\n\n\n\nThe model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.",
"### Preprocessing\n\nData is prepared with the 'ocf_datapipes.URL' datapipe [2].",
"## Results\n\nThe training logs for the current model can be found here on wandb.\n\nThe training logs for all model runs of PVNet2 can be found here.\n\nSome experimental notes can be found at in the google doc",
"### Hardware\n\nTrained on a single NVIDIA Tesla T4",
"### Software\n\n- [1] URL\n- [2] URL"
] | [
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"passage: TAGS\n#pytorch #en #license-mit #region-us \n# PVNet2## Model Description\n\n\nThis model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.\n\n- Developed by: openclimatefix\n- Model type: Fusion model\n- Language(s) (NLP): en\n- License: mit# Training Details## Data\n\n\n\nThe model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.### Preprocessing\n\nData is prepared with the 'ocf_datapipes.URL' datapipe [2].## Results\n\nThe training logs for the current model can be found here on wandb.\n\nThe training logs for all model runs of PVNet2 can be found here.\n\nSome experimental notes can be found at in the google doc### Hardware\n\nTrained on a single NVIDIA Tesla T4### Software\n\n- [1] URL\n- [2] URL"
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] |
null | null | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | Jimmyhd/llama213b50RowsTimeBook | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T10:28:23+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #autotrain #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #autotrain #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
56,
29,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-base"} | null | HeydarS/flan-t5-base_peft_v22 | [
"peft",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
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[optional]
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APA:
## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers"} | text-generation | AIJUUD/juud-Mistral-7B-dpo | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T10:29:25+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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] |
null | null | transformers |
# BagelMIsteryTour-v2-8x7B 5bpw
Exllama quant of [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-3.5bpw-exl2)
## Prompt format: Alpaca
It is noted to also work with mistral
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
```
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
| {"license": "cc-by-nc-4.0", "tags": ["mergekit", "merge"], "base_model": ["mistralai/Mixtral-8x7B-v0.1", "jondurbin/bagel-dpo-8x7b-v0.2", "Sao10K/Sensualize-Mixtral-bf16", "mistralai/Mixtral-8x7B-v0.1", "Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora", "mistralai/Mixtral-8x7B-Instruct-v0.1"]} | text-generation | Kooten/BagelMIsteryTour-v2-8x7B-5bpw-exl2 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mergekit",
"merge",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"base_model:jondurbin/bagel-dpo-8x7b-v0.2",
"base_model:Sao10K/Sensualize-Mixtral-bf16",
"base_model:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T10:29:37+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-mistralai/Mixtral-8x7B-v0.1 #base_model-jondurbin/bagel-dpo-8x7b-v0.2 #base_model-Sao10K/Sensualize-Mixtral-bf16 #base_model-Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# BagelMIsteryTour-v2-8x7B 5bpw
Exllama quant of ycros/BagelMIsteryTour-v2-8x7B
## Other quants:
EXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw
## Prompt format: Alpaca
It is noted to also work with mistral
## Contact
Kooten on discord
URL if you would like to support me
| [
"# BagelMIsteryTour-v2-8x7B 5bpw\nExllama quant of ycros/BagelMIsteryTour-v2-8x7B",
"## Other quants:\n\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw",
"## Prompt format: Alpaca\nIt is noted to also work with mistral",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-mistralai/Mixtral-8x7B-v0.1 #base_model-jondurbin/bagel-dpo-8x7b-v0.2 #base_model-Sao10K/Sensualize-Mixtral-bf16 #base_model-Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# BagelMIsteryTour-v2-8x7B 5bpw\nExllama quant of ycros/BagelMIsteryTour-v2-8x7B",
"## Other quants:\n\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw",
"## Prompt format: Alpaca\nIt is noted to also work with mistral",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
175,
40,
33,
18,
14
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-mistralai/Mixtral-8x7B-v0.1 #base_model-jondurbin/bagel-dpo-8x7b-v0.2 #base_model-Sao10K/Sensualize-Mixtral-bf16 #base_model-Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# BagelMIsteryTour-v2-8x7B 5bpw\nExllama quant of ycros/BagelMIsteryTour-v2-8x7B## Other quants:\n\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw, 3.5bpw## Prompt format: Alpaca\nIt is noted to also work with mistral## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-ks-ob
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0033
- Accuracy: 0.9999
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1462 | 1.0 | 191 | 0.1376 | 0.9731 |
| 0.0317 | 2.0 | 383 | 0.0206 | 0.9969 |
| 0.0112 | 3.0 | 574 | 0.0078 | 0.9990 |
| 0.0062 | 4.0 | 766 | 0.0040 | 0.9998 |
| 0.0063 | 4.99 | 955 | 0.0033 | 0.9999 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-ks-ob", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9998775760048969, "name": "Accuracy"}]}]}]} | audio-classification | iamhack/distilhubert-finetuned-ks-ob | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:29:50+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us
| distilhubert-finetuned-ks-ob
============================
This model is a fine-tuned version of ntu-spml/distilhubert on the audiofolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0033
* Accuracy: 0.9999
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
76,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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null | null | pytorch |
# PVNet2
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
This model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in [this google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing).
- **Developed by:** openclimatefix
- **Model type:** Fusion model
- **Language(s) (NLP):** en
- **License:** mit
# Training Details
## Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details.
### Preprocessing
Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [2].
## Results
The training logs for the current model can be found [here on wandb](https://wandb.ai/openclimatefix/pvnet2.1/runs/None).
The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1).
Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing)
### Hardware
Trained on a single NVIDIA Tesla T4
### Software
- [1] https://github.com/openclimatefix/PVNet
- [2] https://github.com/openclimatefix/ocf_datapipes | {"language": "en", "license": "mit", "library_name": "pytorch"} | null | openclimatefix/pvnet_india | [
"pytorch",
"en",
"license:mit",
"region:us"
] | 2024-02-07T10:30:05+00:00 | [] | [
"en"
] | TAGS
#pytorch #en #license-mit #region-us
|
# PVNet2
## Model Description
This model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.
- Developed by: openclimatefix
- Model type: Fusion model
- Language(s) (NLP): en
- License: mit
# Training Details
## Data
The model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.
### Preprocessing
Data is prepared with the 'ocf_datapipes.URL' datapipe [2].
## Results
The training logs for the current model can be found here on wandb.
The training logs for all model runs of PVNet2 can be found here.
Some experimental notes can be found at in the google doc
### Hardware
Trained on a single NVIDIA Tesla T4
### Software
- [1] URL
- [2] URL | [
"# PVNet2",
"## Model Description\n\n\nThis model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.\n\n- Developed by: openclimatefix\n- Model type: Fusion model\n- Language(s) (NLP): en\n- License: mit",
"# Training Details",
"## Data\n\n\n\nThe model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.",
"### Preprocessing\n\nData is prepared with the 'ocf_datapipes.URL' datapipe [2].",
"## Results\n\nThe training logs for the current model can be found here on wandb.\n\nThe training logs for all model runs of PVNet2 can be found here.\n\nSome experimental notes can be found at in the google doc",
"### Hardware\n\nTrained on a single NVIDIA Tesla T4",
"### Software\n\n- [1] URL\n- [2] URL"
] | [
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"# PVNet2",
"## Model Description\n\n\nThis model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.\n\n- Developed by: openclimatefix\n- Model type: Fusion model\n- Language(s) (NLP): en\n- License: mit",
"# Training Details",
"## Data\n\n\n\nThe model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.",
"### Preprocessing\n\nData is prepared with the 'ocf_datapipes.URL' datapipe [2].",
"## Results\n\nThe training logs for the current model can be found here on wandb.\n\nThe training logs for all model runs of PVNet2 can be found here.\n\nSome experimental notes can be found at in the google doc",
"### Hardware\n\nTrained on a single NVIDIA Tesla T4",
"### Software\n\n- [1] URL\n- [2] URL"
] | [
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"passage: TAGS\n#pytorch #en #license-mit #region-us \n# PVNet2## Model Description\n\n\nThis model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in this google doc.\n\n- Developed by: openclimatefix\n- Model type: Fusion model\n- Language(s) (NLP): en\n- License: mit# Training Details## Data\n\n\n\nThe model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the the google doc for more details.### Preprocessing\n\nData is prepared with the 'ocf_datapipes.URL' datapipe [2].## Results\n\nThe training logs for the current model can be found here on wandb.\n\nThe training logs for all model runs of PVNet2 can be found here.\n\nSome experimental notes can be found at in the google doc### Hardware\n\nTrained on a single NVIDIA Tesla T4### Software\n\n- [1] URL\n- [2] URL"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-tr-cv
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2234
- Wer: 103.7020
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.189 | 0.37 | 1000 | 0.2750 | 155.0454 |
| 0.1791 | 0.73 | 2000 | 0.2457 | 115.8613 |
| 0.079 | 1.1 | 3000 | 0.2290 | 87.8336 |
| 0.078 | 1.46 | 4000 | 0.2234 | 103.7020 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "whisper-small-tr-cv", "results": []}]} | automatic-speech-recognition | tgrhn/whisper-small-tr-cv | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:30:14+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
| whisper-small-tr-cv
===================
This model is a fine-tuned version of openai/whisper-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2234
* Wer: 103.7020
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.1
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
69,
130,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ca-finetuned-phi-2-colab
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "ca-finetuned-phi-2-colab", "results": []}]} | null | lvcalucioli/ca-finetuned-phi-2-colab | [
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | 2024-02-07T10:30:18+00:00 | [] | [] | TAGS
#generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
|
# ca-finetuned-phi-2-colab
This model is a fine-tuned version of microsoft/phi-2 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.05\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.13.3"
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"TAGS\n#generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n",
"# ca-finetuned-phi-2-colab\n\nThis model is a fine-tuned version of microsoft/phi-2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.05\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.13.3"
] | [
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"passage: TAGS\n#generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n# ca-finetuned-phi-2-colab\n\nThis model is a fine-tuned version of microsoft/phi-2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.05\n- num_epochs: 10### Training results### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_all dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3095
- Mean Iou: 0.5513
- Mean Accuracy: 0.7874
- Overall Accuracy: 0.9260
- Accuracy Bg: nan
- Accuracy Fallo cohesivo: 0.9668
- Accuracy Fallo malla: 0.6808
- Accuracy Fallo adhesivo: 0.9727
- Accuracy Fallo burbuja: 0.5291
- Iou Bg: 0.0
- Iou Fallo cohesivo: 0.9167
- Iou Fallo malla: 0.6189
- Iou Fallo adhesivo: 0.7307
- Iou Fallo burbuja: 0.4903
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:|
| 0.1378 | 1.0 | 783 | 0.2677 | 0.4895 | 0.7143 | 0.9122 | nan | 0.9724 | 0.5531 | 0.9663 | 0.3654 | 0.0 | 0.9038 | 0.5327 | 0.6757 | 0.3351 |
| 0.1117 | 2.0 | 1566 | 0.2305 | 0.5289 | 0.7978 | 0.9246 | nan | 0.9507 | 0.7727 | 0.9705 | 0.4974 | 0.0 | 0.9214 | 0.6808 | 0.5876 | 0.4549 |
| 0.0881 | 3.0 | 2349 | 0.2041 | 0.5556 | 0.7867 | 0.9354 | nan | 0.9712 | 0.7391 | 0.9389 | 0.4975 | 0.0 | 0.9273 | 0.6790 | 0.7323 | 0.4394 |
| 0.0878 | 4.0 | 3132 | 0.1984 | 0.5584 | 0.8003 | 0.9346 | nan | 0.9556 | 0.8247 | 0.9602 | 0.4606 | 0.0 | 0.9261 | 0.6935 | 0.7373 | 0.4352 |
| 0.0895 | 5.0 | 3915 | 0.2841 | 0.5246 | 0.8086 | 0.9088 | nan | 0.9137 | 0.8834 | 0.9719 | 0.4652 | 0.0 | 0.8964 | 0.6309 | 0.6593 | 0.4365 |
| 0.0773 | 6.0 | 4698 | 0.2547 | 0.5652 | 0.7823 | 0.9336 | nan | 0.9775 | 0.6843 | 0.9384 | 0.5291 | 0.0 | 0.9251 | 0.6378 | 0.7820 | 0.4813 |
| 0.0667 | 7.0 | 5481 | 0.2726 | 0.5609 | 0.7932 | 0.9295 | nan | 0.9741 | 0.6609 | 0.9689 | 0.5689 | 0.0 | 0.9203 | 0.6202 | 0.7548 | 0.5093 |
| 0.0678 | 8.0 | 6264 | 0.2950 | 0.5276 | 0.8002 | 0.9175 | nan | 0.9443 | 0.7561 | 0.9713 | 0.5292 | 0.0 | 0.9089 | 0.6570 | 0.5900 | 0.4822 |
| 0.0653 | 9.0 | 7047 | 0.2712 | 0.5467 | 0.7682 | 0.9288 | nan | 0.9690 | 0.6971 | 0.9641 | 0.4425 | 0.0 | 0.9189 | 0.6330 | 0.7588 | 0.4228 |
| 0.0646 | 10.0 | 7830 | 0.2841 | 0.5499 | 0.7819 | 0.9272 | nan | 0.9681 | 0.6840 | 0.9688 | 0.5068 | 0.0 | 0.9178 | 0.6243 | 0.7345 | 0.4728 |
| 0.057 | 11.0 | 8613 | 0.3373 | 0.5257 | 0.7782 | 0.9166 | nan | 0.9593 | 0.6555 | 0.9739 | 0.5242 | 0.0 | 0.9075 | 0.6040 | 0.6319 | 0.4848 |
| 0.0591 | 12.0 | 9396 | 0.3082 | 0.5504 | 0.7900 | 0.9247 | nan | 0.9656 | 0.6776 | 0.9705 | 0.5463 | 0.0 | 0.9148 | 0.6172 | 0.7182 | 0.5019 |
| 0.053 | 12.77 | 10000 | 0.3095 | 0.5513 | 0.7874 | 0.9260 | nan | 0.9668 | 0.6808 | 0.9727 | 0.5291 | 0.0 | 0.9167 | 0.6189 | 0.7307 | 0.4903 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "model-index": [{"name": "segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps", "results": []}]} | image-segmentation | blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_all-10k-steps | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"image-segmentation",
"vision",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:32:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us
| segformer-finetuned-4ss1st3r\_s3gs3m\_24Jan\_all-10k-steps
==========================================================
This model is a fine-tuned version of nvidia/mit-b0 on the blzncz/4ss1st3r\_s3gs3m\_24Jan\_all dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3095
* Mean Iou: 0.5513
* Mean Accuracy: 0.7874
* Overall Accuracy: 0.9260
* Accuracy Bg: nan
* Accuracy Fallo cohesivo: 0.9668
* Accuracy Fallo malla: 0.6808
* Accuracy Fallo adhesivo: 0.9727
* Accuracy Fallo burbuja: 0.5291
* Iou Bg: 0.0
* Iou Fallo cohesivo: 0.9167
* Iou Fallo malla: 0.6189
* Iou Fallo adhesivo: 0.7307
* Iou Fallo burbuja: 0.4903
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000
### Training results
### Framework versions
* Transformers 4.31.0.dev0
* Pytorch 2.0.1+cpu
* Datasets 2.13.1
* Tokenizers 0.13.3
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] |
null | null | transformers | <img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1/resolve/main/llama-tr-image.jpeg"
alt="drawing" width="400"/>
## Trendyol LLM 7b base v0.1
- **Model creator:** [Trendyol](https://huggingface.co/Trendyol)
- **Original model:** [Trendyol-LLM-7b-base-v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Trendyol's Trendyol LLM 7b base v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1)
<!-- description end -->
# Quantization methods
| quantization method | bits | size | use case | recommended |
|---------------------|------|----------|-----------------------------------------------------|-------------|
| Q2_K | 2 | 2.59 GB | smallest, significant quality loss - not recommended for most purposes | ❌ |
| Q3_K_S | 3 | 3.01 GB | very small, high quality loss | ❌ |
| Q3_K_M | 3 | 3.36 GB | very small, high quality loss | ❌ |
| Q3_K_L | 3 | 3.66 GB | small, substantial quality loss | ❌ |
| Q4_0 | 4 | 3.9 GB | legacy; small, very high quality loss - prefer using Q3_K_M | ❌ |
| Q4_K_M | 4 | 4.15 GB | medium, balanced quality - recommended | ✅ |
| Q5_0 | 5 | 4.73 GB | legacy; medium, balanced quality - prefer using Q4_K_M | ❌ |
| Q5_K_S | 5 | 4.73 GB | large, low quality loss - recommended | ✅ |
| Q5_K_M | 5 | 4.86 GB | large, very low quality loss - recommended | ✅ |
| Q6_K | 6 | 5.61 GB | very large, extremely low quality loss | ❌ |
| Q8_0 | 8 | 13.7 GB | very large, extremely low quality loss - not recommended | ❌ |
| {"language": ["tr", "en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "Trendyol/Trendyol-LLM-7b-base-v0.1", "pipeline_tag": "text-generation", "model_type": "llama", "inference": false} | text-generation | sayhan/Trendyol-LLM-7b-base-v0.1-GGUF | [
"transformers",
"gguf",
"llama",
"text-generation",
"tr",
"en",
"base_model:Trendyol/Trendyol-LLM-7b-base-v0.1",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 2024-02-07T10:33:05+00:00 | [] | [
"tr",
"en"
] | TAGS
#transformers #gguf #llama #text-generation #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #text-generation-inference #region-us
| <img src="URL
alt="drawing" width="400"/>
Trendyol LLM 7b base v0.1
-------------------------
* Model creator: Trendyol
* Original model: Trendyol-LLM-7b-base-v0.1
Description
-----------
This repo contains GGUF format model files for Trendyol's Trendyol LLM 7b base v0.1
Quantization methods
====================
| [] | [
"TAGS\n#transformers #gguf #llama #text-generation #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #text-generation-inference #region-us \n"
] | [
63
] | [
"passage: TAGS\n#transformers #gguf #llama #text-generation #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #text-generation-inference #region-us \n"
] | [
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null | null | null | # Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Lag-Llama is the <b>first open-source foundation model for time series forecasting</b>!
[[Tweet Thread](https://twitter.com/arjunashok37/status/1755261111233114165)] [[Model Weights](https://huggingface.co/time-series-foundation-models/Lag-Llama)] [[Colab Demo on Zero-Shot Forecasting](https://colab.research.google.com/drive/13HHKYL_HflHBKxDWycXgIUAHSeHRR5eo?usp=sharing)] [[GitHub](https://github.com/time-series-foundation-models/lag-llama)] [[Paper](https://arxiv.org/abs/2310.08278)]
____
This HuggingFace model houses the <a href="https://huggingface.co/time-series-foundation-models/Lag-Llama/blob/main/lag-llama.ckpt" target="_blank">pretrained checkpoint</a> of Lag-Llama.
____
* **Coming Next**: Fine-tuning scripts with examples on real-world datasets and best practices in using Lag-Llama!🚀
<b>Updates</b>:
* **17-Feb-2024**: We have released a new updated [Colab Demo](https://colab.research.google.com/drive/1XxrLW9VGPlZDw3efTvUi0hQimgJOwQG6?usp=sharing) for zero-shot forecasting that shows how one can load time series of different formats.
* **7-Feb-2024**: We released Lag-Llama, with open-source model checkpoints and a Colab Demo for zero-shot forecasting.
____
<b>Current Features:</b>
💫 <b>Zero-shot forecasting</b> on a dataset of <b>any frequency</b> for <b>any prediction length</b>, using the <a href="https://colab.research.google.com/drive/13HHKYL_HflHBKxDWycXgIUAHSeHRR5eo?usp=sharing" target="_blank">Colab Demo.</a><br/>
____
Coming Soon:
⭐ An <b>online gradio demo</b> where you can upload time series and get zero-shot predictions and perform finetuning.
⭐ Features for <b>finetuning</b> the foundation model
⭐ Features for <b>pretraining</b> Lag-Llama on your own large-scale data
⭐ Scripts to <b>reproduce</b> all results in the paper.
____
Stay Tuned!🦙 | {"license": "apache-2.0", "tags": ["time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series"]} | null | time-series-foundation-models/Lag-Llama | [
"safetensors",
"time series",
"forecasting",
"pretrained models",
"foundation models",
"time series foundation models",
"time-series",
"arxiv:2310.08278",
"license:apache-2.0",
"region:us"
] | 2024-02-07T10:33:56+00:00 | [
"2310.08278"
] | [] | TAGS
#safetensors #time series #forecasting #pretrained models #foundation models #time series foundation models #time-series #arxiv-2310.08278 #license-apache-2.0 #region-us
| # Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
!lag-llama-architecture
Lag-Llama is the <b>first open-source foundation model for time series forecasting</b>!
[Tweet Thread] [Model Weights] [Colab Demo on Zero-Shot Forecasting] [GitHub] [Paper]
____
This HuggingFace model houses the <a href="URL target="_blank">pretrained checkpoint</a> of Lag-Llama.
____
* Coming Next: Fine-tuning scripts with examples on real-world datasets and best practices in using Lag-Llama!
<b>Updates</b>:
* 17-Feb-2024: We have released a new updated Colab Demo for zero-shot forecasting that shows how one can load time series of different formats.
* 7-Feb-2024: We released Lag-Llama, with open-source model checkpoints and a Colab Demo for zero-shot forecasting.
____
<b>Current Features:</b>
<b>Zero-shot forecasting</b> on a dataset of <b>any frequency</b> for <b>any prediction length</b>, using the <a href="URL target="_blank">Colab Demo.</a><br/>
____
Coming Soon:
⭐ An <b>online gradio demo</b> where you can upload time series and get zero-shot predictions and perform finetuning.
⭐ Features for <b>finetuning</b> the foundation model
⭐ Features for <b>pretraining</b> Lag-Llama on your own large-scale data
⭐ Scripts to <b>reproduce</b> all results in the paper.
____
Stay Tuned! | [
"# Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting\n\n!lag-llama-architecture\n\nLag-Llama is the <b>first open-source foundation model for time series forecasting</b>!\n\n[Tweet Thread] [Model Weights] [Colab Demo on Zero-Shot Forecasting] [GitHub] [Paper]\n\n____\nThis HuggingFace model houses the <a href=\"URL target=\"_blank\">pretrained checkpoint</a> of Lag-Llama.\n\n____\n\n* Coming Next: Fine-tuning scripts with examples on real-world datasets and best practices in using Lag-Llama! \n\n<b>Updates</b>:\n\n* 17-Feb-2024: We have released a new updated Colab Demo for zero-shot forecasting that shows how one can load time series of different formats.\n* 7-Feb-2024: We released Lag-Llama, with open-source model checkpoints and a Colab Demo for zero-shot forecasting.\n\n____\n\n<b>Current Features:</b>\n\n <b>Zero-shot forecasting</b> on a dataset of <b>any frequency</b> for <b>any prediction length</b>, using the <a href=\"URL target=\"_blank\">Colab Demo.</a><br/>\n\n____\n\nComing Soon:\n\n⭐ An <b>online gradio demo</b> where you can upload time series and get zero-shot predictions and perform finetuning.\n\n⭐ Features for <b>finetuning</b> the foundation model\n\n⭐ Features for <b>pretraining</b> Lag-Llama on your own large-scale data\n\n⭐ Scripts to <b>reproduce</b> all results in the paper.\n\n\n____\n\nStay Tuned!"
] | [
"TAGS\n#safetensors #time series #forecasting #pretrained models #foundation models #time series foundation models #time-series #arxiv-2310.08278 #license-apache-2.0 #region-us \n",
"# Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting\n\n!lag-llama-architecture\n\nLag-Llama is the <b>first open-source foundation model for time series forecasting</b>!\n\n[Tweet Thread] [Model Weights] [Colab Demo on Zero-Shot Forecasting] [GitHub] [Paper]\n\n____\nThis HuggingFace model houses the <a href=\"URL target=\"_blank\">pretrained checkpoint</a> of Lag-Llama.\n\n____\n\n* Coming Next: Fine-tuning scripts with examples on real-world datasets and best practices in using Lag-Llama! \n\n<b>Updates</b>:\n\n* 17-Feb-2024: We have released a new updated Colab Demo for zero-shot forecasting that shows how one can load time series of different formats.\n* 7-Feb-2024: We released Lag-Llama, with open-source model checkpoints and a Colab Demo for zero-shot forecasting.\n\n____\n\n<b>Current Features:</b>\n\n <b>Zero-shot forecasting</b> on a dataset of <b>any frequency</b> for <b>any prediction length</b>, using the <a href=\"URL target=\"_blank\">Colab Demo.</a><br/>\n\n____\n\nComing Soon:\n\n⭐ An <b>online gradio demo</b> where you can upload time series and get zero-shot predictions and perform finetuning.\n\n⭐ Features for <b>finetuning</b> the foundation model\n\n⭐ Features for <b>pretraining</b> Lag-Llama on your own large-scale data\n\n⭐ Scripts to <b>reproduce</b> all results in the paper.\n\n\n____\n\nStay Tuned!"
] | [
54,
415
] | [
"passage: TAGS\n#safetensors #time series #forecasting #pretrained models #foundation models #time series foundation models #time-series #arxiv-2310.08278 #license-apache-2.0 #region-us \n# Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting\n\n!lag-llama-architecture\n\nLag-Llama is the <b>first open-source foundation model for time series forecasting</b>!\n\n[Tweet Thread] [Model Weights] [Colab Demo on Zero-Shot Forecasting] [GitHub] [Paper]\n\n____\nThis HuggingFace model houses the <a href=\"URL target=\"_blank\">pretrained checkpoint</a> of Lag-Llama.\n\n____\n\n* Coming Next: Fine-tuning scripts with examples on real-world datasets and best practices in using Lag-Llama! \n\n<b>Updates</b>:\n\n* 17-Feb-2024: We have released a new updated Colab Demo for zero-shot forecasting that shows how one can load time series of different formats.\n* 7-Feb-2024: We released Lag-Llama, with open-source model checkpoints and a Colab Demo for zero-shot forecasting.\n\n____\n\n<b>Current Features:</b>\n\n <b>Zero-shot forecasting</b> on a dataset of <b>any frequency</b> for <b>any prediction length</b>, using the <a href=\"URL target=\"_blank\">Colab Demo.</a><br/>\n\n____\n\nComing Soon:\n\n⭐ An <b>online gradio demo</b> where you can upload time series and get zero-shot predictions and perform finetuning.\n\n⭐ Features for <b>finetuning</b> the foundation model\n\n⭐ Features for <b>pretraining</b> Lag-Llama on your own large-scale data\n\n⭐ Scripts to <b>reproduce</b> all results in the paper.\n\n\n____\n\nStay Tuned!"
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] |
null | null | transformers |
# Model Card for Finetuned FinBERT on Market-Based Facts
Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models.
Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis. 📈
**Outperforms FinBERT**
- 🎯 +25% precision
- 🚀 +18% recall
**Outperforms DistilRoBERTa finetuned for finance**
- 🎯 +22% precision
- 🚀 +15% recall
**Outperforms GPT-4 zero-shot learning**
- 🎯 +15% precision
- 🚀 +8.2% recall
## Validation Metrics
| Metric | Value |
|--------------------|-----------------------|
| loss | 0.9176467061042786 |
| f1_macro | 0.49749240436690023 |
| f1_micro | 0.5627105467737756 |
| f1_weighted | 0.5279720746084178 |
| precision_macro | 0.5386355574899088 |
| precision_micro | 0.5627105467737756 |
| precision_weighted | 0.5462149036191247 |
| recall_macro | 0.517542664344306 |
| recall_micro | 0.5627105467737756 |
| recall_weighted | 0.5627105467737756 |
| accuracy | 0.5627105467737756 |
| {"tags": ["finance", "finbert", "market", "text-classification"], "datasets": ["FinBERT_market_based/autotrain-data"], "widget": [{"text": "Asian Stocks Set to Decline Amidst Growth Worries", "output": [{"label": "POSITIVE", "score": 0.14}, {"label": "INDECISIVE", "score": 0.25}, {"label": "NEGATIVE", "score": 0.61}]}, {"text": "High inflation expectations becoming part of the American consumers behavioral norm", "output": [{"label": "POSITIVE", "score": 0.49}, {"label": "INDECISIVE", "score": 0.3}, {"label": "NEGATIVE", "score": 0.21}]}]} | text-classification | baptle/FinBERT_market_based | [
"transformers",
"safetensors",
"bert",
"text-classification",
"finance",
"finbert",
"market",
"dataset:FinBERT_market_based/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:35:19+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #finance #finbert #market #dataset-FinBERT_market_based/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
| Model Card for Finetuned FinBERT on Market-Based Facts
======================================================
Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models.
Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis.
Outperforms FinBERT
* +25% precision
* +18% recall
Outperforms DistilRoBERTa finetuned for finance
* +22% precision
* +15% recall
Outperforms GPT-4 zero-shot learning
* +15% precision
* +8.2% recall
Validation Metrics
------------------
| [] | [
"TAGS\n#transformers #safetensors #bert #text-classification #finance #finbert #market #dataset-FinBERT_market_based/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
63
] | [
"passage: TAGS\n#transformers #safetensors #bert #text-classification #finance #finbert #market #dataset-FinBERT_market_based/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | # NlpGroup21 Model Repository
Welcome to the NlpGroup21 model repository! This repository contains the model and related files for our project
| {} | question-answering | SamiaNasrin/NlpGroup21 | [
"transformers",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:35:47+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us
| # NlpGroup21 Model Repository
Welcome to the NlpGroup21 model repository! This repository contains the model and related files for our project
| [
"# NlpGroup21 Model Repository\n\nWelcome to the NlpGroup21 model repository! This repository contains the model and related files for our project"
] | [
"TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us \n",
"# NlpGroup21 Model Repository\n\nWelcome to the NlpGroup21 model repository! This repository contains the model and related files for our project"
] | [
30,
35
] | [
"passage: TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us \n# NlpGroup21 Model Repository\n\nWelcome to the NlpGroup21 model repository! This repository contains the model and related files for our project"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SMIDS_3x_beit_large_RMSProp_lr00001_fold1
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0820
- Accuracy: 0.9149
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2685 | 1.0 | 451 | 0.3897 | 0.8848 |
| 0.1118 | 2.0 | 902 | 0.2936 | 0.8982 |
| 0.0342 | 3.0 | 1353 | 0.4483 | 0.9032 |
| 0.001 | 4.0 | 1804 | 0.5502 | 0.9048 |
| 0.0869 | 5.0 | 2255 | 0.6499 | 0.9065 |
| 0.0014 | 6.0 | 2706 | 0.8117 | 0.8865 |
| 0.0384 | 7.0 | 3157 | 0.7447 | 0.8948 |
| 0.0451 | 8.0 | 3608 | 0.6530 | 0.9082 |
| 0.0709 | 9.0 | 4059 | 0.6732 | 0.9015 |
| 0.0 | 10.0 | 4510 | 0.6148 | 0.9149 |
| 0.0228 | 11.0 | 4961 | 1.1044 | 0.8865 |
| 0.0296 | 12.0 | 5412 | 0.9094 | 0.8998 |
| 0.0204 | 13.0 | 5863 | 0.8117 | 0.9015 |
| 0.0038 | 14.0 | 6314 | 0.8884 | 0.8998 |
| 0.0012 | 15.0 | 6765 | 0.9412 | 0.9032 |
| 0.0002 | 16.0 | 7216 | 0.8562 | 0.8948 |
| 0.0032 | 17.0 | 7667 | 0.9579 | 0.9015 |
| 0.0 | 18.0 | 8118 | 0.9428 | 0.8998 |
| 0.013 | 19.0 | 8569 | 0.8898 | 0.8948 |
| 0.0064 | 20.0 | 9020 | 1.1422 | 0.8831 |
| 0.0 | 21.0 | 9471 | 1.1030 | 0.8932 |
| 0.0 | 22.0 | 9922 | 1.0542 | 0.8915 |
| 0.0699 | 23.0 | 10373 | 1.0852 | 0.8848 |
| 0.0033 | 24.0 | 10824 | 0.9609 | 0.9065 |
| 0.0 | 25.0 | 11275 | 1.0151 | 0.8965 |
| 0.0005 | 26.0 | 11726 | 1.1969 | 0.8932 |
| 0.0076 | 27.0 | 12177 | 1.1701 | 0.8915 |
| 0.0078 | 28.0 | 12628 | 0.9421 | 0.9132 |
| 0.0 | 29.0 | 13079 | 1.1759 | 0.8982 |
| 0.0 | 30.0 | 13530 | 1.0019 | 0.9015 |
| 0.0 | 31.0 | 13981 | 1.0589 | 0.9032 |
| 0.0 | 32.0 | 14432 | 1.1621 | 0.8932 |
| 0.0 | 33.0 | 14883 | 1.0855 | 0.9048 |
| 0.0091 | 34.0 | 15334 | 1.0595 | 0.8965 |
| 0.0066 | 35.0 | 15785 | 1.1524 | 0.8948 |
| 0.0 | 36.0 | 16236 | 1.2129 | 0.8948 |
| 0.0 | 37.0 | 16687 | 1.1566 | 0.8915 |
| 0.0 | 38.0 | 17138 | 1.1190 | 0.8982 |
| 0.0 | 39.0 | 17589 | 1.0349 | 0.9082 |
| 0.0 | 40.0 | 18040 | 1.0732 | 0.9048 |
| 0.0 | 41.0 | 18491 | 1.0481 | 0.9065 |
| 0.005 | 42.0 | 18942 | 1.0820 | 0.9132 |
| 0.0 | 43.0 | 19393 | 1.0916 | 0.9132 |
| 0.0 | 44.0 | 19844 | 1.0658 | 0.9132 |
| 0.0 | 45.0 | 20295 | 1.1018 | 0.9115 |
| 0.0 | 46.0 | 20746 | 1.1000 | 0.9132 |
| 0.0 | 47.0 | 21197 | 1.0952 | 0.9132 |
| 0.0 | 48.0 | 21648 | 1.0925 | 0.9149 |
| 0.0 | 49.0 | 22099 | 1.0903 | 0.9149 |
| 0.0 | 50.0 | 22550 | 1.0820 | 0.9149 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr00001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9148580968280468, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_RMSProp_lr00001_fold1 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:38:07+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_RMSProp\_lr00001\_fold1
===============================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0820
* Accuracy: 0.9149
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
"TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
81,
116,
4,
30
] | [
"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deit-base-patch16-224-finetuned-ind-14-imbalanced-multi-1900
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7622
- Accuracy: 0.7711
- Recall: 0.7711
- F1: 0.7642
- Precision: 0.7983
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.4902 | 0.99 | 47 | 1.1554 | 0.6289 | 0.6289 | 0.6064 | 0.6203 |
| 0.8353 | 2.0 | 95 | 0.8930 | 0.7526 | 0.7526 | 0.7486 | 0.7752 |
| 0.7136 | 2.97 | 141 | 0.7525 | 0.7632 | 0.7632 | 0.7538 | 0.7678 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "recall", "f1", "precision"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "deit-base-patch16-224-finetuned-ind-14-imbalanced-multi-1900", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7710526315789473, "name": "Accuracy"}, {"type": "recall", "value": 0.7710526315789473, "name": "Recall"}, {"type": "f1", "value": 0.7641690635598875, "name": "F1"}, {"type": "precision", "value": 0.7982612832899328, "name": "Precision"}]}]}]} | image-classification | TrackWizz/deit-base-patch16-224-finetuned-ind-14-imbalanced-multi-1900 | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:38:39+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| deit-base-patch16-224-finetuned-ind-14-imbalanced-multi-1900
============================================================
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7622
* Accuracy: 0.7711
* Recall: 0.7711
* F1: 0.7642
* Precision: 0.7983
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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144,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scibert_scivocab_uncased-finetuned-mol-mlm-0.3-5epochs
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5835
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8759 | 1.0 | 180 | 0.6795 |
| 0.6773 | 2.0 | 360 | 0.6306 |
| 0.6255 | 3.0 | 540 | 0.5880 |
| 0.5912 | 4.0 | 720 | 0.5707 |
| 0.5783 | 5.0 | 900 | 0.5724 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "scibert_scivocab_uncased-finetuned-mol-mlm-0.3-5epochs", "results": []}]} | fill-mask | matr1xx/scibert_scivocab_uncased-finetuned-mol-mlm-0.3-5epochs | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:38:53+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us
| scibert\_scivocab\_uncased-finetuned-mol-mlm-0.3-5epochs
========================================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5835
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.1
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
62,
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4,
30
] | [
"passage: TAGS\n#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SMIDS_3x_beit_large_SGD_lr001_fold1
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2943
- Accuracy: 0.8865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6323 | 1.0 | 451 | 0.5905 | 0.7563 |
| 0.41 | 2.0 | 902 | 0.4222 | 0.8314 |
| 0.4628 | 3.0 | 1353 | 0.3718 | 0.8598 |
| 0.3662 | 4.0 | 1804 | 0.3367 | 0.8681 |
| 0.379 | 5.0 | 2255 | 0.3148 | 0.8748 |
| 0.2639 | 6.0 | 2706 | 0.3090 | 0.8698 |
| 0.4019 | 7.0 | 3157 | 0.3080 | 0.8648 |
| 0.279 | 8.0 | 3608 | 0.2999 | 0.8715 |
| 0.3167 | 9.0 | 4059 | 0.2923 | 0.8748 |
| 0.3042 | 10.0 | 4510 | 0.2854 | 0.8815 |
| 0.231 | 11.0 | 4961 | 0.2906 | 0.8748 |
| 0.2026 | 12.0 | 5412 | 0.2826 | 0.8831 |
| 0.1483 | 13.0 | 5863 | 0.2919 | 0.8715 |
| 0.2851 | 14.0 | 6314 | 0.2780 | 0.8815 |
| 0.2041 | 15.0 | 6765 | 0.2797 | 0.8848 |
| 0.2024 | 16.0 | 7216 | 0.2836 | 0.8715 |
| 0.167 | 17.0 | 7667 | 0.2844 | 0.8748 |
| 0.2457 | 18.0 | 8118 | 0.2905 | 0.8748 |
| 0.1978 | 19.0 | 8569 | 0.2830 | 0.8815 |
| 0.2055 | 20.0 | 9020 | 0.2868 | 0.8765 |
| 0.1372 | 21.0 | 9471 | 0.2845 | 0.8831 |
| 0.1634 | 22.0 | 9922 | 0.2847 | 0.8848 |
| 0.1788 | 23.0 | 10373 | 0.2862 | 0.8848 |
| 0.2036 | 24.0 | 10824 | 0.2855 | 0.8865 |
| 0.1392 | 25.0 | 11275 | 0.2876 | 0.8881 |
| 0.2051 | 26.0 | 11726 | 0.2878 | 0.8881 |
| 0.1171 | 27.0 | 12177 | 0.2848 | 0.8865 |
| 0.1611 | 28.0 | 12628 | 0.2865 | 0.8831 |
| 0.1205 | 29.0 | 13079 | 0.2930 | 0.8815 |
| 0.2074 | 30.0 | 13530 | 0.2882 | 0.8915 |
| 0.1754 | 31.0 | 13981 | 0.2871 | 0.8932 |
| 0.1695 | 32.0 | 14432 | 0.2896 | 0.8848 |
| 0.1013 | 33.0 | 14883 | 0.2962 | 0.8831 |
| 0.1427 | 34.0 | 15334 | 0.2888 | 0.8932 |
| 0.1423 | 35.0 | 15785 | 0.2902 | 0.8831 |
| 0.2021 | 36.0 | 16236 | 0.2897 | 0.8848 |
| 0.1031 | 37.0 | 16687 | 0.2892 | 0.8932 |
| 0.1509 | 38.0 | 17138 | 0.2928 | 0.8848 |
| 0.1062 | 39.0 | 17589 | 0.2920 | 0.8815 |
| 0.1267 | 40.0 | 18040 | 0.2943 | 0.8881 |
| 0.1634 | 41.0 | 18491 | 0.2936 | 0.8831 |
| 0.1974 | 42.0 | 18942 | 0.2952 | 0.8815 |
| 0.1701 | 43.0 | 19393 | 0.2939 | 0.8881 |
| 0.0954 | 44.0 | 19844 | 0.2959 | 0.8831 |
| 0.0819 | 45.0 | 20295 | 0.2946 | 0.8815 |
| 0.1138 | 46.0 | 20746 | 0.2952 | 0.8831 |
| 0.143 | 47.0 | 21197 | 0.2946 | 0.8865 |
| 0.2061 | 48.0 | 21648 | 0.2939 | 0.8865 |
| 0.1688 | 49.0 | 22099 | 0.2941 | 0.8865 |
| 0.1463 | 50.0 | 22550 | 0.2943 | 0.8865 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_SGD_lr001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8864774624373957, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_SGD_lr001_fold1 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:39:40+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_SGD\_lr001\_fold1
=========================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2943
* Accuracy: 0.8865
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
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"### Training results",
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"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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] |
null | null | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | PranavInvenics/phi2_v3 | [
"transformers",
"safetensors",
"phi",
"text-generation",
"autotrain",
"conversational",
"custom_code",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:41:05+00:00 | [] | [] | TAGS
#transformers #safetensors #phi #text-generation #autotrain #conversational #custom_code #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #autotrain #conversational #custom_code #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
55,
29,
3
] | [
"passage: TAGS\n#transformers #safetensors #phi #text-generation #autotrain #conversational #custom_code #license-other #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | shidowake/cyber2chat-7B-base-bnb-4bit | [
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# Model Card for Model ID
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## Uses
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## How to Get Started with the Model
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## Training Details
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## Evaluation
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#### Factors
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speec T5 LT - Unai Gurbindo
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Vox Populi LT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6231 | 12.7 | 100 | 0.5834 |
| 0.5691 | 25.4 | 200 | 0.5259 |
| 0.5381 | 38.1 | 300 | 0.5030 |
| 0.5306 | 50.79 | 400 | 0.5016 |
| 0.521 | 63.49 | 500 | 0.4978 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"language": ["lt"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "speec T5 LT - Unai Gurbindo", "results": []}]} | text-to-audio | UnaiGurbindo/speecht5_finetuned_voxpopuli_es | [
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"lt",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-07T10:51:49+00:00 | [] | [
"lt"
] | TAGS
#transformers #safetensors #speecht5 #text-to-audio #generated_from_trainer #lt #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
| speec T5 LT - Unai Gurbindo
===========================
This model is a fine-tuned version of microsoft/speecht5\_tts on the Vox Populi LT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4978
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 500
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #speecht5 #text-to-audio #generated_from_trainer #lt #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
72,
158,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #speecht5 #text-to-audio #generated_from_trainer #lt #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | OctavianB/MistralRo | [
"transformers",
"safetensors",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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### Model Sources [optional]
- Repository:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
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## Technical Specifications [optional]
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null | null | transformers |
# Model Card for Model ID
Navarna 7B is an LLM fine-tuned to be good in Hindi chat performance while adding sentence retrieval (RAG) tasks capability.
## Model Details
### Model Description
Details of process and implementation -
https://docs.google.com/document/d/11gPWDazMLHIAm3kT2FuEZJndocd4B6-kX_fu0jMLukk/edit?usp=sharing
All SFT, DPO and chat inference notebooks are present in the HF repo itself.
 | {"license": "apache-2.0", "library_name": "transformers"} | text-generation | TokenBender/Navarna_v0_1_OpenHermes_Hindi | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T10:56:44+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
Navarna 7B is an LLM fine-tuned to be good in Hindi chat performance while adding sentence retrieval (RAG) tasks capability.
!image/png
## Model Details
### Model Description
Details of process and implementation -
URL
All SFT, DPO and chat inference notebooks are present in the HF repo itself.
!image/png | [
"# Model Card for Model ID\n\nNavarna 7B is an LLM fine-tuned to be good in Hindi chat performance while adding sentence retrieval (RAG) tasks capability.\n\n!image/png",
"## Model Details",
"### Model Description\n\nDetails of process and implementation - \n\nURL\n\nAll SFT, DPO and chat inference notebooks are present in the HF repo itself.\n\n\n!image/png"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\nNavarna 7B is an LLM fine-tuned to be good in Hindi chat performance while adding sentence retrieval (RAG) tasks capability.\n\n!image/png",
"## Model Details",
"### Model Description\n\nDetails of process and implementation - \n\nURL\n\nAll SFT, DPO and chat inference notebooks are present in the HF repo itself.\n\n\n!image/png"
] | [
59,
44,
3,
36
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID\n\nNavarna 7B is an LLM fine-tuned to be good in Hindi chat performance while adding sentence retrieval (RAG) tasks capability.\n\n!image/png## Model Details### Model Description\n\nDetails of process and implementation - \n\nURL\n\nAll SFT, DPO and chat inference notebooks are present in the HF repo itself.\n\n\n!image/png"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | benj3037/bert_test | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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APA:
## Glossary [optional]
## More Information [optional]
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] |
null | null | transformers |
# bert-base-uncased-finetuned-sst2-v2
BERT (`"bert-base-uncased"`) finetuned on SST-2 (Stanford Sentiment Treebank Binary).
This model pertains to the "Try it out!" exercise in section 4 of chapter 3 of the Hugging Face "NLP Course" (https://huggingface.co/learn/nlp-course/chapter3/4).
It was trained using a custom PyTorch loop without Hugging Face Accelerate.
Code: https://github.com/sambitmukherjee/hf-nlp-course-exercises/blob/main/chapter3/section4.ipynb
Experiment tracking: https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-sst2-v2
## Usage
```
from transformers import pipeline
classifier = pipeline("text-classification", model="sadhaklal/bert-base-uncased-finetuned-sst2-v2")
print(classifier("uneasy mishmash of styles and genres ."))
print(classifier("by the end of no such thing the audience , like beatrice , has a watchful affection for the monster ."))
```
## Metric
Accuracy on the `'validation'` split of SST-2: 0.9278
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["sst2"], "metrics": ["accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "this film 's relationship to actual tension is the same as what christmas-tree flocking in a spray can is to actual snow : a poor -- if durable -- imitation .", "example_title": "negative"}, {"text": "director rob marshall went out gunning to make a great one .", "example_title": "positive"}]} | text-classification | sadhaklal/bert-base-uncased-finetuned-sst2-v2 | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:07:29+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-sst2-v2
BERT ('"bert-base-uncased"') finetuned on SST-2 (Stanford Sentiment Treebank Binary).
This model pertains to the "Try it out!" exercise in section 4 of chapter 3 of the Hugging Face "NLP Course" (URL
It was trained using a custom PyTorch loop without Hugging Face Accelerate.
Code: URL
Experiment tracking: URL
## Usage
## Metric
Accuracy on the ''validation'' split of SST-2: 0.9278
| [
"# bert-base-uncased-finetuned-sst2-v2\n\nBERT ('\"bert-base-uncased\"') finetuned on SST-2 (Stanford Sentiment Treebank Binary).\n\nThis model pertains to the \"Try it out!\" exercise in section 4 of chapter 3 of the Hugging Face \"NLP Course\" (URL\n\nIt was trained using a custom PyTorch loop without Hugging Face Accelerate.\n\nCode: URL\n\nExperiment tracking: URL",
"## Usage",
"## Metric\n\nAccuracy on the ''validation'' split of SST-2: 0.9278"
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-sst2-v2\n\nBERT ('\"bert-base-uncased\"') finetuned on SST-2 (Stanford Sentiment Treebank Binary).\n\nThis model pertains to the \"Try it out!\" exercise in section 4 of chapter 3 of the Hugging Face \"NLP Course\" (URL\n\nIt was trained using a custom PyTorch loop without Hugging Face Accelerate.\n\nCode: URL\n\nExperiment tracking: URL",
"## Usage",
"## Metric\n\nAccuracy on the ''validation'' split of SST-2: 0.9278"
] | [
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] | [
"passage: TAGS\n#transformers #safetensors #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-uncased-finetuned-sst2-v2\n\nBERT ('\"bert-base-uncased\"') finetuned on SST-2 (Stanford Sentiment Treebank Binary).\n\nThis model pertains to the \"Try it out!\" exercise in section 4 of chapter 3 of the Hugging Face \"NLP Course\" (URL\n\nIt was trained using a custom PyTorch loop without Hugging Face Accelerate.\n\nCode: URL\n\nExperiment tracking: URL## Usage## Metric\n\nAccuracy on the ''validation'' split of SST-2: 0.9278"
] | [
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null | null | transformers |
# Latxa-llama-chat-7b
Latxa-llama-chat-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* HiTZ/latxa-7b-v1
* meta-llama/Llama-2-7b-chat-hf
## 🧩 Configuration
```yaml
slices:
- sources:
- model: HiTZ/latxa-7b-v1
layer_range: [0, 32]
- model: meta-llama/Llama-2-7b-chat-hf
layer_range: [0, 32]
merge_method: slerp
base_model: HiTZ/latxa-7b-v1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "airalribalta/Latxa-llama-chat-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "HiTZ/latxa-7b-v1", "meta-llama/Llama-2-7b-chat-hf"], "base_model": ["HiTZ/latxa-7b-v1", "meta-llama/Llama-2-7b-chat-hf"]} | text-generation | airalribalta/Latxa-llama-chat-7b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"HiTZ/latxa-7b-v1",
"meta-llama/Llama-2-7b-chat-hf",
"base_model:HiTZ/latxa-7b-v1",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:09:43+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-chat-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Latxa-llama-chat-7b
Latxa-llama-chat-7b is a merge of the following models using LazyMergekit:
* HiTZ/latxa-7b-v1
* meta-llama/Llama-2-7b-chat-hf
## Configuration
## Usage
| [
"# Latxa-llama-chat-7b\n\nLatxa-llama-chat-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-chat-hf",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-chat-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Latxa-llama-chat-7b\n\nLatxa-llama-chat-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-chat-hf",
"## Configuration",
"## Usage"
] | [
122,
60,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-chat-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Latxa-llama-chat-7b\n\nLatxa-llama-chat-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-chat-hf## Configuration## Usage"
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] |
null | null | diffusers |
## Warning
This is an experimental model. It works only with segmoe library!
## Experts
- source_model: Lykon/dreamshaper-8 (base)
- source_model: Lykon/AAM_AnyLora_AnimeMix
- source_model: stablediffusionapi/realistic-vision-51
## Usage
This model can be used via the [segmoe](https://github.com/segmind/segmoe) library.
Make sure to install segmoe by running
```bash
pip install segmoe
```
```python
from segmoe import SegMoEPipeline
pipeline = SegMoEPipeline("RachidAR/AFlow-SegMoe-1Bx3-v0.1", device = "cuda", safety_checker = None)
prompt = "cosmic canvas, orange city background, painting of a chubby cat"
negative_prompt = "nsfw, bad quality, worse quality"
img = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
height=1024,
width=1024,
num_inference_steps=25,
guidance_scale=7.5,
).images[0]
img.save("image.png")
```
 | {"language": ["en"], "license": "apache-2.0", "library_name": "diffusers", "tags": ["text-to-image", "stable-diffusion", "safetensors", "stable-diffusion-1.5", "moe", "segmoe"], "pipeline_tag": "text-to-image"} | text-to-image | RachidAR/AFlow-SegMoe-1Bx3-v0.1 | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-1.5",
"moe",
"segmoe",
"en",
"license:apache-2.0",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-07T11:10:40+00:00 | [] | [
"en"
] | TAGS
#diffusers #safetensors #text-to-image #stable-diffusion #stable-diffusion-1.5 #moe #segmoe #en #license-apache-2.0 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
## Warning
This is an experimental model. It works only with segmoe library!
## Experts
- source_model: Lykon/dreamshaper-8 (base)
- source_model: Lykon/AAM_AnyLora_AnimeMix
- source_model: stablediffusionapi/realistic-vision-51
## Usage
This model can be used via the segmoe library.
Make sure to install segmoe by running
!image/png
!image/png
!image/png | [
"## Warning\nThis is an experimental model. It works only with segmoe library!",
"## Experts\n - source_model: Lykon/dreamshaper-8 (base)\n - source_model: Lykon/AAM_AnyLora_AnimeMix\n - source_model: stablediffusionapi/realistic-vision-51",
"## Usage\n\nThis model can be used via the segmoe library. \n\nMake sure to install segmoe by running\n\n\n\n\n\n!image/png\n!image/png\n!image/png"
] | [
"TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #stable-diffusion-1.5 #moe #segmoe #en #license-apache-2.0 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"## Warning\nThis is an experimental model. It works only with segmoe library!",
"## Experts\n - source_model: Lykon/dreamshaper-8 (base)\n - source_model: Lykon/AAM_AnyLora_AnimeMix\n - source_model: stablediffusionapi/realistic-vision-51",
"## Usage\n\nThis model can be used via the segmoe library. \n\nMake sure to install segmoe by running\n\n\n\n\n\n!image/png\n!image/png\n!image/png"
] | [
75,
19,
56,
37
] | [
"passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #stable-diffusion-1.5 #moe #segmoe #en #license-apache-2.0 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n## Warning\nThis is an experimental model. It works only with segmoe library!## Experts\n - source_model: Lykon/dreamshaper-8 (base)\n - source_model: Lykon/AAM_AnyLora_AnimeMix\n - source_model: stablediffusionapi/realistic-vision-51## Usage\n\nThis model can be used via the segmoe library. \n\nMake sure to install segmoe by running\n\n\n\n\n\n!image/png\n!image/png\n!image/png"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-BBCNews_v2
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3170
- Rouge1: 0.1558
- Rouge2: 0.1263
- Rougel: 0.1483
- Rougelsum: 0.1496
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 75 | 0.4430 | 0.1374 | 0.098 | 0.1257 | 0.1289 |
| No log | 1.99 | 150 | 0.3657 | 0.1466 | 0.1112 | 0.1367 | 0.1388 |
| No log | 2.99 | 225 | 0.3449 | 0.1536 | 0.1222 | 0.145 | 0.147 |
| No log | 3.99 | 300 | 0.3320 | 0.1534 | 0.1226 | 0.1454 | 0.147 |
| 0.609 | 5.0 | 376 | 0.3245 | 0.1534 | 0.1229 | 0.1457 | 0.1472 |
| 0.609 | 6.0 | 451 | 0.3214 | 0.155 | 0.125 | 0.147 | 0.1486 |
| 0.609 | 6.99 | 526 | 0.3181 | 0.1555 | 0.1261 | 0.148 | 0.1496 |
| 0.609 | 7.98 | 600 | 0.3170 | 0.1558 | 0.1263 | 0.1483 | 0.1496 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "base_model": "google-t5/t5-small", "model-index": [{"name": "t5-small-finetuned-BBCNews_v2", "results": []}]} | summarization | RMWeerasinghe/t5-small-finetuned-BBCNews_v2 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:14:17+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #generated_from_trainer #base_model-google-t5/t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-BBCNews\_v2
==============================
This model is a fine-tuned version of google-t5/t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3170
* Rouge1: 0.1558
* Rouge2: 0.1263
* Rougel: 0.1483
* Rougelsum: 0.1496
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 4e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
### Training results
### Framework versions
* Transformers 4.32.0
* Pytorch 2.1.2
* Datasets 2.12.0
* Tokenizers 0.13.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.0\n* Pytorch 2.1.2\n* Datasets 2.12.0\n* Tokenizers 0.13.3"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 5\n* total\\_train\\_batch\\_size: 20\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.0\n* Pytorch 2.1.2\n* Datasets 2.12.0\n* Tokenizers 0.13.3"
] | [
81,
126,
4,
30
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"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #generated_from_trainer #base_model-google-t5/t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 5\n* total\\_train\\_batch\\_size: 20\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8### Training results### Framework versions\n\n\n* Transformers 4.32.0\n* Pytorch 2.1.2\n* Datasets 2.12.0\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5-base-gloss-to-sentence
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.37.0
- TensorFlow 2.15.0
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-base-gloss-to-sentence", "results": []}]} | text2text-generation | Krithiik/t5-base-gloss-to-sentence | [
"transformers",
"tf",
"safetensors",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:15:41+00:00 | [] | [] | TAGS
#transformers #tf #safetensors #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-gloss-to-sentence
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.37.0
- TensorFlow 2.15.0
- Datasets 2.1.0
- Tokenizers 0.15.1
| [
"# t5-base-gloss-to-sentence\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- TensorFlow 2.15.0\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #safetensors #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-gloss-to-sentence\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- TensorFlow 2.15.0\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
63,
39,
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] | [
"passage: TAGS\n#transformers #tf #safetensors #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-base-gloss-to-sentence\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32### Training results### Framework versions\n\n- Transformers 4.37.0\n- TensorFlow 2.15.0\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
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] |
null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.21 +/- 0.07", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | wahdan99/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T11:18:42+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5735
- Accuracy: 0.8913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 3 | 2.7459 | 0.1304 |
| No log | 1.85 | 6 | 2.6837 | 0.1087 |
| No log | 2.77 | 9 | 2.6583 | 0.1087 |
| 2.6599 | 4.0 | 13 | 2.6553 | 0.1087 |
| 2.6599 | 4.92 | 16 | 2.5628 | 0.1522 |
| 2.6599 | 5.85 | 19 | 2.4286 | 0.1739 |
| 2.3457 | 6.77 | 22 | 2.4705 | 0.1522 |
| 2.3457 | 8.0 | 26 | 2.2801 | 0.1522 |
| 2.3457 | 8.92 | 29 | 2.2110 | 0.2391 |
| 2.1136 | 9.85 | 32 | 2.1101 | 0.2391 |
| 2.1136 | 10.77 | 35 | 2.0434 | 0.3478 |
| 2.1136 | 12.0 | 39 | 2.2015 | 0.2609 |
| 1.8271 | 12.92 | 42 | 1.8463 | 0.2826 |
| 1.8271 | 13.85 | 45 | 1.8144 | 0.2391 |
| 1.8271 | 14.77 | 48 | 1.6712 | 0.2391 |
| 1.6517 | 16.0 | 52 | 1.6885 | 0.4348 |
| 1.6517 | 16.92 | 55 | 1.7268 | 0.4565 |
| 1.6517 | 17.85 | 58 | 1.5564 | 0.5435 |
| 1.5123 | 18.77 | 61 | 1.4261 | 0.5435 |
| 1.5123 | 20.0 | 65 | 1.2945 | 0.6739 |
| 1.5123 | 20.92 | 68 | 1.2329 | 0.6957 |
| 1.2441 | 21.85 | 71 | 1.1841 | 0.6957 |
| 1.2441 | 22.77 | 74 | 1.1297 | 0.7174 |
| 1.2441 | 24.0 | 78 | 1.0477 | 0.7826 |
| 1.0647 | 24.92 | 81 | 1.0039 | 0.7174 |
| 1.0647 | 25.85 | 84 | 0.9795 | 0.7174 |
| 1.0647 | 26.77 | 87 | 0.9619 | 0.7609 |
| 0.9374 | 28.0 | 91 | 0.8940 | 0.8043 |
| 0.9374 | 28.92 | 94 | 0.8675 | 0.8043 |
| 0.9374 | 29.85 | 97 | 0.8516 | 0.8043 |
| 0.7902 | 30.77 | 100 | 0.8203 | 0.8261 |
| 0.7902 | 32.0 | 104 | 0.7963 | 0.7609 |
| 0.7902 | 32.92 | 107 | 0.7329 | 0.8478 |
| 0.6959 | 33.85 | 110 | 0.7382 | 0.8043 |
| 0.6959 | 34.77 | 113 | 0.7205 | 0.8261 |
| 0.6959 | 36.0 | 117 | 0.6996 | 0.8043 |
| 0.6694 | 36.92 | 120 | 0.6949 | 0.8696 |
| 0.6694 | 37.85 | 123 | 0.7009 | 0.7826 |
| 0.6694 | 38.77 | 126 | 0.6502 | 0.8261 |
| 0.6226 | 40.0 | 130 | 0.5835 | 0.8478 |
| 0.6226 | 40.92 | 133 | 0.5735 | 0.8913 |
| 0.6226 | 41.85 | 136 | 0.5651 | 0.8913 |
| 0.6226 | 42.77 | 139 | 0.5624 | 0.8913 |
| 0.5746 | 44.0 | 143 | 0.5565 | 0.8913 |
| 0.5746 | 44.92 | 146 | 0.5476 | 0.8913 |
| 0.5746 | 45.85 | 149 | 0.5439 | 0.8913 |
| 0.5238 | 46.15 | 150 | 0.5435 | 0.8913 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base", "results": []}]} | audio-classification | micsell/wav2vec2-base | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:22:14+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base
=============
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5735
* Accuracy: 0.8913
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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143,
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300m-england-0207-successive-ladderside_attempt-iceberg
This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3109
- Wer: 0.2681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1227
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4873 | 1.0 | 1227 | 0.3144 | 0.3074 |
| 0.3303 | 2.0 | 2454 | 0.2784 | 0.2812 |
| 0.2983 | 3.0 | 3681 | 0.2688 | 0.2764 |
| 0.2753 | 4.0 | 4908 | 0.2577 | 0.2672 |
| 0.2554 | 5.0 | 6135 | 0.2540 | 0.2614 |
| 0.2377 | 6.0 | 7362 | 0.2522 | 0.2608 |
| 0.221 | 7.0 | 8589 | 0.2525 | 0.2609 |
| 0.2052 | 8.0 | 9816 | 0.2563 | 0.2619 |
| 0.1898 | 9.0 | 11043 | 0.2634 | 0.2654 |
| 0.1752 | 10.0 | 12270 | 0.2670 | 0.2600 |
| 0.1614 | 11.0 | 13497 | 0.2761 | 0.2618 |
| 0.1489 | 12.0 | 14724 | 0.2854 | 0.2634 |
| 0.1376 | 13.0 | 15951 | 0.2979 | 0.2655 |
| 0.1286 | 14.0 | 17178 | 0.3050 | 0.2675 |
| 0.1208 | 15.0 | 18405 | 0.3109 | 0.2681 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "vitouphy/wav2vec2-xls-r-300m-english", "model-index": [{"name": "wav2vec2-300m-england-0207-successive-ladderside_attempt-iceberg", "results": []}]} | automatic-speech-recognition | Lin25/wav2vec2-300m-england-0207-successive-ladderside_attempt-iceberg | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:vitouphy/wav2vec2-xls-r-300m-english",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:22:56+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-england-0207-successive-ladderside\_attempt-iceberg
=================================================================
This model is a fine-tuned version of vitouphy/wav2vec2-xls-r-300m-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3109
* Wer: 0.2681
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1227
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 2.1.0
* Datasets 2.14.7
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
80,
159,
4,
37
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output_dir_clean_df_10-100_noX_100_50_epoch_cluster
This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3807
- Accuracy: 0.2682
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
all AMPs from the compass dataset with the length between 10-100 AA. Only considering the 20 standard AA (NO X).
## Training procedure
50 epochs
training rate: 1 e-06
block size: 100
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 197 | 6.2811 | 0.2041 |
| No log | 2.0 | 394 | 6.1540 | 0.2116 |
| 6.3092 | 3.0 | 591 | 6.0786 | 0.2153 |
| 6.3092 | 4.0 | 788 | 6.0237 | 0.2177 |
| 6.3092 | 5.0 | 985 | 5.9779 | 0.2200 |
| 6.0762 | 6.0 | 1182 | 5.9383 | 0.2222 |
| 6.0762 | 7.0 | 1379 | 5.9011 | 0.2249 |
| 5.9715 | 8.0 | 1576 | 5.8651 | 0.2273 |
| 5.9715 | 9.0 | 1773 | 5.8307 | 0.2302 |
| 5.9715 | 10.0 | 1970 | 5.8002 | 0.2320 |
| 5.8814 | 11.0 | 2167 | 5.7723 | 0.2338 |
| 5.8814 | 12.0 | 2364 | 5.7463 | 0.2355 |
| 5.8123 | 13.0 | 2561 | 5.7220 | 0.2371 |
| 5.8123 | 14.0 | 2758 | 5.6994 | 0.2386 |
| 5.8123 | 15.0 | 2955 | 5.6781 | 0.2404 |
| 5.7544 | 16.0 | 3152 | 5.6581 | 0.2419 |
| 5.7544 | 17.0 | 3349 | 5.6391 | 0.2433 |
| 5.7009 | 18.0 | 3546 | 5.6211 | 0.2450 |
| 5.7009 | 19.0 | 3743 | 5.6044 | 0.2466 |
| 5.7009 | 20.0 | 3940 | 5.5888 | 0.2482 |
| 5.6629 | 21.0 | 4137 | 5.5735 | 0.2493 |
| 5.6629 | 22.0 | 4334 | 5.5588 | 0.2507 |
| 5.6235 | 23.0 | 4531 | 5.5451 | 0.2520 |
| 5.6235 | 24.0 | 4728 | 5.5320 | 0.2531 |
| 5.6235 | 25.0 | 4925 | 5.5197 | 0.2541 |
| 5.5865 | 26.0 | 5122 | 5.5078 | 0.2552 |
| 5.5865 | 27.0 | 5319 | 5.4969 | 0.2562 |
| 5.5649 | 28.0 | 5516 | 5.4866 | 0.2573 |
| 5.5649 | 29.0 | 5713 | 5.4765 | 0.2583 |
| 5.5649 | 30.0 | 5910 | 5.4670 | 0.2595 |
| 5.5322 | 31.0 | 6107 | 5.4582 | 0.2604 |
| 5.5322 | 32.0 | 6304 | 5.4500 | 0.2612 |
| 5.5168 | 33.0 | 6501 | 5.4424 | 0.2618 |
| 5.5168 | 34.0 | 6698 | 5.4350 | 0.2627 |
| 5.5168 | 35.0 | 6895 | 5.4283 | 0.2633 |
| 5.4984 | 36.0 | 7092 | 5.4219 | 0.2640 |
| 5.4984 | 37.0 | 7289 | 5.4161 | 0.2647 |
| 5.4984 | 38.0 | 7486 | 5.4107 | 0.2651 |
| 5.48 | 39.0 | 7683 | 5.4058 | 0.2656 |
| 5.48 | 40.0 | 7880 | 5.4014 | 0.2660 |
| 5.4665 | 41.0 | 8077 | 5.3974 | 0.2665 |
| 5.4665 | 42.0 | 8274 | 5.3936 | 0.2668 |
| 5.4665 | 43.0 | 8471 | 5.3905 | 0.2671 |
| 5.4612 | 44.0 | 8668 | 5.3878 | 0.2674 |
| 5.4612 | 45.0 | 8865 | 5.3855 | 0.2677 |
| 5.4515 | 46.0 | 9062 | 5.3836 | 0.2679 |
| 5.4515 | 47.0 | 9259 | 5.3822 | 0.2680 |
| 5.4515 | 48.0 | 9456 | 5.3812 | 0.2681 |
| 5.4453 | 49.0 | 9653 | 5.3808 | 0.2682 |
| 5.4453 | 50.0 | 9850 | 5.3807 | 0.2682 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "nferruz/ProtGPT2", "widget": [{"text": "<|endoftext|>"}], "model-index": [{"name": "output_dir_clean_df_10-100_noX_100_1_epoch_cluster", "results": []}]} | text-generation | wabu/AmpGPT2 | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:nferruz/ProtGPT2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:26:19+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-nferruz/ProtGPT2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| output\_dir\_clean\_df\_10-100\_noX\_100\_50\_epoch\_cluster
============================================================
This model is a fine-tuned version of nferruz/ProtGPT2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.3807
* Accuracy: 0.2682
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
all AMPs from the compass dataset with the length between 10-100 AA. Only considering the 20 standard AA (NO X).
Training procedure
------------------
50 epochs
training rate: 1 e-06
block size: 100
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-06
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50.0
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.2.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-nferruz/ProtGPT2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50.0### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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null | null | diffusers | ### My-CARS Dreambooth model trained by karthik678 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4JK21CV017
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | karthik678/my-cars | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-07T11:29:01+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### My-CARS Dreambooth model trained by karthik678 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4JK21CV017
Sample pictures of this concept:
!0
!1
!2
| [
"### My-CARS Dreambooth model trained by karthik678 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 4JK21CV017\n\nSample pictures of this concept:\n\n \n \n !0\n !1\n !2"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### My-CARS Dreambooth model trained by karthik678 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 4JK21CV017\n\nSample pictures of this concept:\n\n \n \n !0\n !1\n !2"
] | [
73,
57
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### My-CARS Dreambooth model trained by karthik678 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 4JK21CV017\n\nSample pictures of this concept:\n\n \n \n !0\n !1\n !2"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
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|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_RTSplit0207_8
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0163
- Wer: 0.1852
- Cer: 0.0724
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.8061 | 1.0 | 120 | 3.6322 | 1.0 | 0.9491 |
| 1.6154 | 2.0 | 240 | 1.3664 | 0.8267 | 0.6459 |
| 0.9386 | 3.0 | 360 | 0.7153 | 0.7943 | 0.4815 |
| 0.6144 | 4.0 | 480 | 0.4349 | 0.5764 | 0.2486 |
| 0.4572 | 5.0 | 600 | 0.2240 | 0.3882 | 0.1601 |
| 0.2889 | 6.0 | 720 | 0.1179 | 0.2574 | 0.0945 |
| 0.2707 | 7.0 | 840 | 0.0558 | 0.2094 | 0.0766 |
| 0.1943 | 8.0 | 960 | 0.0376 | 0.2012 | 0.0728 |
| 0.1681 | 9.0 | 1080 | 0.0261 | 0.1965 | 0.0865 |
| 0.0751 | 10.0 | 1200 | 0.0163 | 0.1852 | 0.0724 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0207_8", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0207_8 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:32:20+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0207\_8
=======================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0163
* Wer: 0.1852
* Cer: 0.0724
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | null | These are the weights of the NN used by the (https://github.com/aim-uofa/AdelaiDepth/tree/main/LeReS)[LeReS].
*DISCLAIMER*: I do not own anything, I am just making the trained weights available on a reliable platform. | {"license": "unknown"} | null | ffranchina/LeReS | [
"license:unknown",
"region:us"
] | 2024-02-07T11:32:36+00:00 | [] | [] | TAGS
#license-unknown #region-us
| These are the weights of the NN used by the (URL
*DISCLAIMER*: I do not own anything, I am just making the trained weights available on a reliable platform. | [] | [
"TAGS\n#license-unknown #region-us \n"
] | [
13
] | [
"passage: TAGS\n#license-unknown #region-us \n"
] | [
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null | null | transformers |
# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
# Model Card for Taiwan LLM 8x7B-DPO
Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.
## Model description
- **Model type:** A 8x7B parameter Mixtral MoE model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw)
- **Finetuned from model:** [yentinglin/Taiwan-LLM-MoE-alpha](https://huggingface.co/yentinglin/Taiwan-LLM-MoE-alpha)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa
- **Demo:** https://twllm.com/
## Performance
Checkout leaderboard in [Tw Chatbot Arena](https://arena.twllm.com/)
TMMLUS+ score:
- yentinglin/Taiwan-LLM-MoE-alpha: 43.93
- yentinglin/Taiwan-LLM-8x7B-DPO: TBD
## Intended uses
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# pip install transformers>=4.34
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="yentinglin/Taiwan-LLM-8x7B-DPO", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "你是一個人工智慧助理",
},
{"role": "user", "content": "東北季風如何影響台灣氣候？"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Citation
If you find Taiwan LLM useful in your work, please cite it with:
```
@misc{lin2023taiwan,
title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model},
author={Yen-Ting Lin and Yun-Nung Chen},
year={2023},
eprint={2311.17487},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# Acknowledgement
Ubitus provides valuable compute resources for the project. | {"language": ["zh"], "license": "apache-2.0", "library_name": "transformers", "widget": [{"text": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \u4f60\u597d\uff0c\u8acb\u554f\u4f60\u53ef\u4ee5\u5e6b\u6211\u5beb\u4e00\u5c01\u63a8\u85a6\u4fe1\u55ce\uff1f ASSISTANT:"}], "pipeline_tag": "text-generation", "extra_gated_heading": "Acknowledge the license to accept the repository.", "extra_gated_prompt": "Please contact the author for access.", "extra_gated_button_content": "Acknowledge license \u540c\u610f\u4ee5\u4e0a\u5167\u5bb9", "extra_gated_fields": {"Name": "text", "Mail": "text", "Organization": "text", "Country": "text", "Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author": "checkbox"}} | text-generation | yentinglin/Taiwan-LLM-8x7B-DPO | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"zh",
"arxiv:2311.17487",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:32:49+00:00 | [
"2311.17487"
] | [
"zh"
] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #zh #arxiv-2311.17487 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
!image/png
# Checkout Taiwan-LLM Demo Chat-UI
# Model Card for Taiwan LLM 8x7B-DPO
Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.
## Model description
- Model type: A 8x7B parameter Mixtral MoE model fine-tuned on a mix of publicly available, synthetic datasets.
- Language(s) (NLP): Primarily Traditional Chinese (zh-tw)
- Finetuned from model: yentinglin/Taiwan-LLM-MoE-alpha
### Model Sources
- Repository: URL
- Demo: URL
## Performance
Checkout leaderboard in Tw Chatbot Arena
TMMLUS+ score:
- yentinglin/Taiwan-LLM-MoE-alpha: 43.93
- yentinglin/Taiwan-LLM-8x7B-DPO: TBD
## Intended uses
Here's how you can run the model using the 'pipeline()' function from Transformers:
If you find Taiwan LLM useful in your work, please cite it with:
# Acknowledgement
Ubitus provides valuable compute resources for the project. | [
"# Checkout Taiwan-LLM Demo Chat-UI",
"# Model Card for Taiwan LLM 8x7B-DPO\n\nTaiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.",
"## Model description\n\n- Model type: A 8x7B parameter Mixtral MoE model fine-tuned on a mix of publicly available, synthetic datasets.\n- Language(s) (NLP): Primarily Traditional Chinese (zh-tw)\n- Finetuned from model: yentinglin/Taiwan-LLM-MoE-alpha",
"### Model Sources\n\n\n\n- Repository: URL\n- Demo: URL",
"## Performance\n\nCheckout leaderboard in Tw Chatbot Arena\n\nTMMLUS+ score: \n- yentinglin/Taiwan-LLM-MoE-alpha: 43.93\n- yentinglin/Taiwan-LLM-8x7B-DPO: TBD",
"## Intended uses\n\nHere's how you can run the model using the 'pipeline()' function from Transformers:\n\n\n\nIf you find Taiwan LLM useful in your work, please cite it with:",
"# Acknowledgement\n\nUbitus provides valuable compute resources for the project."
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #zh #arxiv-2311.17487 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Checkout Taiwan-LLM Demo Chat-UI",
"# Model Card for Taiwan LLM 8x7B-DPO\n\nTaiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.",
"## Model description\n\n- Model type: A 8x7B parameter Mixtral MoE model fine-tuned on a mix of publicly available, synthetic datasets.\n- Language(s) (NLP): Primarily Traditional Chinese (zh-tw)\n- Finetuned from model: yentinglin/Taiwan-LLM-MoE-alpha",
"### Model Sources\n\n\n\n- Repository: URL\n- Demo: URL",
"## Performance\n\nCheckout leaderboard in Tw Chatbot Arena\n\nTMMLUS+ score: \n- yentinglin/Taiwan-LLM-MoE-alpha: 43.93\n- yentinglin/Taiwan-LLM-8x7B-DPO: TBD",
"## Intended uses\n\nHere's how you can run the model using the 'pipeline()' function from Transformers:\n\n\n\nIf you find Taiwan LLM useful in your work, please cite it with:",
"# Acknowledgement\n\nUbitus provides valuable compute resources for the project."
] | [
70,
11,
43,
79,
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58,
46,
16
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #zh #arxiv-2311.17487 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Checkout Taiwan-LLM Demo Chat-UI# Model Card for Taiwan LLM 8x7B-DPO\n\nTaiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.## Model description\n\n- Model type: A 8x7B parameter Mixtral MoE model fine-tuned on a mix of publicly available, synthetic datasets.\n- Language(s) (NLP): Primarily Traditional Chinese (zh-tw)\n- Finetuned from model: yentinglin/Taiwan-LLM-MoE-alpha### Model Sources\n\n\n\n- Repository: URL\n- Demo: URL## Performance\n\nCheckout leaderboard in Tw Chatbot Arena\n\nTMMLUS+ score: \n- yentinglin/Taiwan-LLM-MoE-alpha: 43.93\n- yentinglin/Taiwan-LLM-8x7B-DPO: TBD## Intended uses\n\nHere's how you can run the model using the 'pipeline()' function from Transformers:\n\n\n\nIf you find Taiwan LLM useful in your work, please cite it with:# Acknowledgement\n\nUbitus provides valuable compute resources for the project."
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "TinyPixel/Llama-2-7B-bf16-sharded"} | null | sajaw/Llama-2-7B-XLLM-Demo-LoRA | [
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#peft #safetensors #arxiv-1910.09700 #base_model-TinyPixel/Llama-2-7B-bf16-sharded #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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APA:
## Glossary [optional]
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] |
null | null | transformers |
# SLIM-TAGS
<!-- Provide a quick summary of what the model is/does. -->
**slim-tags** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.
slim-tags has been fine-tuned for **auto-generating relevant tags and points-of-interest** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
`{"tags": ["tag1", "tag2", "tag3",...]}`
SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.
Each slim model has a 'quantized tool' version, e.g., [**'slim-tags-tool'**](https://huggingface.co/llmware/slim-tags-tool).
## Prompt format:
`function = "classify"`
`params = "tags"`
`prompt = "<human> " + {text} + "\n" + `
`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`
<details>
<summary>Transformers Script </summary>
model = AutoModelForCausalLM.from_pretrained("llmware/slim-tags")
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-tags")
function = "classify"
params = "tags"
text = "Citibank announced a reduction in its targets for economic growth in France and the UK last week "
"in light of ongoing concerns about inflation and unemployment, especially in large employers "
"such as Airbus."
prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
inputs = tokenizer(prompt, return_tensors="pt")
start_of_input = len(inputs.input_ids[0])
outputs = model.generate(
inputs.input_ids.to('cpu'),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.3,
max_new_tokens=100
)
output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)
print("output only: ", output_only)
# here's the fun part
try:
output_only = ast.literal_eval(llm_string_output)
print("success - converted to python dictionary automatically")
except:
print("fail - could not convert to python dictionary automatically - ", llm_string_output)
</details>
<details>
<summary>Using as Function Call in LLMWare</summary>
from llmware.models import ModelCatalog
slim_model = ModelCatalog().load_model("llmware/slim-tags")
response = slim_model.function_call(text,params=["tags"], function="classify")
print("llmware - llm_response: ", response)
</details>
## Model Card Contact
Darren Oberst & llmware team
[Join us on Discord](https://discord.gg/MhZn5Nc39h)
| {"license": "apache-2.0", "inference": false} | text-generation | llmware/slim-tags | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:35:53+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
# SLIM-TAGS
slim-tags is part of the SLIM ("Structured Language Instruction Model") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.
slim-tags has been fine-tuned for auto-generating relevant tags and points-of-interest function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
'{"tags": ["tag1", "tag2", "tag3",...]}'
SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.
Each slim model has a 'quantized tool' version, e.g., 'slim-tags-tool'.
## Prompt format:
'function = "classify"'
'params = "tags"'
'prompt = "<human> " + {text} + "\n" + '
'"<{function}> " + {params} + "</{function}>" + "\n<bot>:"'
<details>
<summary>Transformers Script </summary>
model = AutoModelForCausalLM.from_pretrained("llmware/slim-tags")
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-tags")
function = "classify"
params = "tags"
text = "Citibank announced a reduction in its targets for economic growth in France and the UK last week "
"in light of ongoing concerns about inflation and unemployment, especially in large employers "
"such as Airbus."
prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
inputs = tokenizer(prompt, return_tensors="pt")
start_of_input = len(inputs.input_ids[0])
outputs = model.generate(
inputs.input_ids.to('cpu'),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.3,
max_new_tokens=100
)
output_only = URL(outputs[0][start_of_input:], skip_special_tokens=True)
print("output only: ", output_only)
# here's the fun part
try:
output_only = ast.literal_eval(llm_string_output)
print("success - converted to python dictionary automatically")
except:
print("fail - could not convert to python dictionary automatically - ", llm_string_output)
</details>
<details>
<summary>Using as Function Call in LLMWare</summary>
from URL import ModelCatalog
slim_model = ModelCatalog().load_model("llmware/slim-tags")
response = slim_model.function_call(text,params=["tags"], function="classify")
print("llmware - llm_response: ", response)
</details>
## Model Card Contact
Darren Oberst & llmware team
Join us on Discord
| [
"# SLIM-TAGS\n\n\n\nslim-tags is part of the SLIM (\"Structured Language Instruction Model\") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling. \n\nslim-tags has been fine-tuned for auto-generating relevant tags and points-of-interest function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.: \n\n '{\"tags\": [\"tag1\", \"tag2\", \"tag3\",...]}'\n\n\nSLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow. \n\nEach slim model has a 'quantized tool' version, e.g., 'slim-tags-tool'.",
"## Prompt format:\n\n'function = \"classify\"' \n'params = \"tags\"' \n'prompt = \"<human> \" + {text} + \"\\n\" + ' \n '\"<{function}> \" + {params} + \"</{function}>\" + \"\\n<bot>:\"' \n\n\n<details>\n<summary>Transformers Script </summary>\n\n model = AutoModelForCausalLM.from_pretrained(\"llmware/slim-tags\")\n tokenizer = AutoTokenizer.from_pretrained(\"llmware/slim-tags\")\n\n function = \"classify\"\n params = \"tags\"\n\n text = \"Citibank announced a reduction in its targets for economic growth in France and the UK last week \" \n \"in light of ongoing concerns about inflation and unemployment, especially in large employers \" \n \"such as Airbus.\" \n \n prompt = \"<human>: \" + text + \"\\n\" + f\"<{function}> {params} </{function}>\\n<bot>:\"\n\n inputs = tokenizer(prompt, return_tensors=\"pt\")\n start_of_input = len(inputs.input_ids[0])\n\n outputs = model.generate(\n inputs.input_ids.to('cpu'),\n eos_token_id=tokenizer.eos_token_id,\n pad_token_id=tokenizer.eos_token_id,\n do_sample=True,\n temperature=0.3,\n max_new_tokens=100\n )\n\n output_only = URL(outputs[0][start_of_input:], skip_special_tokens=True)\n\n print(\"output only: \", output_only) \n\n # here's the fun part\n try:\n output_only = ast.literal_eval(llm_string_output)\n print(\"success - converted to python dictionary automatically\")\n except:\n print(\"fail - could not convert to python dictionary automatically - \", llm_string_output)\n \n </details> \n \n<details> \n\n\n\n \n<summary>Using as Function Call in LLMWare</summary>\n\n from URL import ModelCatalog\n slim_model = ModelCatalog().load_model(\"llmware/slim-tags\")\n response = slim_model.function_call(text,params=[\"tags\"], function=\"classify\")\n\n print(\"llmware - llm_response: \", response)\n\n</details>",
"## Model Card Contact\n\nDarren Oberst & llmware team \n\nJoin us on Discord"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"# SLIM-TAGS\n\n\n\nslim-tags is part of the SLIM (\"Structured Language Instruction Model\") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling. \n\nslim-tags has been fine-tuned for auto-generating relevant tags and points-of-interest function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.: \n\n '{\"tags\": [\"tag1\", \"tag2\", \"tag3\",...]}'\n\n\nSLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow. \n\nEach slim model has a 'quantized tool' version, e.g., 'slim-tags-tool'.",
"## Prompt format:\n\n'function = \"classify\"' \n'params = \"tags\"' \n'prompt = \"<human> \" + {text} + \"\\n\" + ' \n '\"<{function}> \" + {params} + \"</{function}>\" + \"\\n<bot>:\"' \n\n\n<details>\n<summary>Transformers Script </summary>\n\n model = AutoModelForCausalLM.from_pretrained(\"llmware/slim-tags\")\n tokenizer = AutoTokenizer.from_pretrained(\"llmware/slim-tags\")\n\n function = \"classify\"\n params = \"tags\"\n\n text = \"Citibank announced a reduction in its targets for economic growth in France and the UK last week \" \n \"in light of ongoing concerns about inflation and unemployment, especially in large employers \" \n \"such as Airbus.\" \n \n prompt = \"<human>: \" + text + \"\\n\" + f\"<{function}> {params} </{function}>\\n<bot>:\"\n\n inputs = tokenizer(prompt, return_tensors=\"pt\")\n start_of_input = len(inputs.input_ids[0])\n\n outputs = model.generate(\n inputs.input_ids.to('cpu'),\n eos_token_id=tokenizer.eos_token_id,\n pad_token_id=tokenizer.eos_token_id,\n do_sample=True,\n temperature=0.3,\n max_new_tokens=100\n )\n\n output_only = URL(outputs[0][start_of_input:], skip_special_tokens=True)\n\n print(\"output only: \", output_only) \n\n # here's the fun part\n try:\n output_only = ast.literal_eval(llm_string_output)\n print(\"success - converted to python dictionary automatically\")\n except:\n print(\"fail - could not convert to python dictionary automatically - \", llm_string_output)\n \n </details> \n \n<details> \n\n\n\n \n<summary>Using as Function Call in LLMWare</summary>\n\n from URL import ModelCatalog\n slim_model = ModelCatalog().load_model(\"llmware/slim-tags\")\n response = slim_model.function_call(text,params=[\"tags\"], function=\"classify\")\n\n print(\"llmware - llm_response: \", response)\n\n</details>",
"## Model Card Contact\n\nDarren Oberst & llmware team \n\nJoin us on Discord"
] | [
46,
201,
631,
18
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n# SLIM-TAGS\n\n\n\nslim-tags is part of the SLIM (\"Structured Language Instruction Model\") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling. \n\nslim-tags has been fine-tuned for auto-generating relevant tags and points-of-interest function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.: \n\n '{\"tags\": [\"tag1\", \"tag2\", \"tag3\",...]}'\n\n\nSLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow. \n\nEach slim model has a 'quantized tool' version, e.g., 'slim-tags-tool'."
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] |
null | null | transformers |
# SLIM-TAGS-TOOL
<!-- Provide a quick summary of what the model is/does. -->
**slim-tags-tool** is a 4_K_M quantized GGUF version of slim-tags, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
[**slim-tags**](https://huggingface.co/llmware/slim-tags) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/slim-tags-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
Load in your favorite GGUF inference engine, or try with llmware as follows:
from llmware.models import ModelCatalog
# to load the model and make a basic inference
model = ModelCatalog().load_model("slim-tags-tool")
response = model.function_call(text_sample)
# this one line will download the model and run a series of tests
ModelCatalog().tool_test_run("slim-tags-tool", verbose=True)
Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
from llmware.agents import LLMfx
llm_fx = LLMfx()
llm_fx.load_tool("tags")
response = llm_fx.tags(text)
Note: please review [**config.json**](https://huggingface.co/llmware/slim-tags-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
## Model Card Contact
Darren Oberst & llmware team
[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)
| {"license": "apache-2.0"} | null | llmware/slim-tags-tool | [
"transformers",
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:36:59+00:00 | [] | [] | TAGS
#transformers #gguf #llama #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
|
# SLIM-TAGS-TOOL
slim-tags-tool is a 4_K_M quantized GGUF version of slim-tags, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
slim-tags is part of the SLIM ("Structured Language Instruction Model") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/slim-tags-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
Load in your favorite GGUF inference engine, or try with llmware as follows:
from URL import ModelCatalog
# to load the model and make a basic inference
model = ModelCatalog().load_model("slim-tags-tool")
response = model.function_call(text_sample)
# this one line will download the model and run a series of tests
ModelCatalog().tool_test_run("slim-tags-tool", verbose=True)
Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
from URL import LLMfx
llm_fx = LLMfx()
llm_fx.load_tool("tags")
response = llm_fx.tags(text)
Note: please review URL in the repository for prompt wrapping information, details on the model, and full test set.
## Model Card Contact
Darren Oberst & llmware team
Any questions? Join us on Discord
| [
"# SLIM-TAGS-TOOL\n\n\n\n\nslim-tags-tool is a 4_K_M quantized GGUF version of slim-tags, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. \n\nslim-tags is part of the SLIM (\"Structured Language Instruction Model\") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.\n\nTo pull the model via API: \n\n from huggingface_hub import snapshot_download \n snapshot_download(\"llmware/slim-tags-tool\", local_dir=\"/path/on/your/machine/\", local_dir_use_symlinks=False) \n \n\nLoad in your favorite GGUF inference engine, or try with llmware as follows:\n\n from URL import ModelCatalog \n \n # to load the model and make a basic inference\n model = ModelCatalog().load_model(\"slim-tags-tool\")\n response = model.function_call(text_sample) \n\n # this one line will download the model and run a series of tests\n ModelCatalog().tool_test_run(\"slim-tags-tool\", verbose=True) \n\n\nSlim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:\n\n from URL import LLMfx\n\n llm_fx = LLMfx()\n llm_fx.load_tool(\"tags\")\n response = llm_fx.tags(text) \n\n\nNote: please review URL in the repository for prompt wrapping information, details on the model, and full test set.",
"## Model Card Contact\n\nDarren Oberst & llmware team \n\nAny questions? Join us on Discord"
] | [
"TAGS\n#transformers #gguf #llama #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n",
"# SLIM-TAGS-TOOL\n\n\n\n\nslim-tags-tool is a 4_K_M quantized GGUF version of slim-tags, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. \n\nslim-tags is part of the SLIM (\"Structured Language Instruction Model\") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.\n\nTo pull the model via API: \n\n from huggingface_hub import snapshot_download \n snapshot_download(\"llmware/slim-tags-tool\", local_dir=\"/path/on/your/machine/\", local_dir_use_symlinks=False) \n \n\nLoad in your favorite GGUF inference engine, or try with llmware as follows:\n\n from URL import ModelCatalog \n \n # to load the model and make a basic inference\n model = ModelCatalog().load_model(\"slim-tags-tool\")\n response = model.function_call(text_sample) \n\n # this one line will download the model and run a series of tests\n ModelCatalog().tool_test_run(\"slim-tags-tool\", verbose=True) \n\n\nSlim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:\n\n from URL import LLMfx\n\n llm_fx = LLMfx()\n llm_fx.load_tool(\"tags\")\n response = llm_fx.tags(text) \n\n\nNote: please review URL in the repository for prompt wrapping information, details on the model, and full test set.",
"## Model Card Contact\n\nDarren Oberst & llmware team \n\nAny questions? Join us on Discord"
] | [
40,
373,
21
] | [
"passage: TAGS\n#transformers #gguf #llama #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n# SLIM-TAGS-TOOL\n\n\n\n\nslim-tags-tool is a 4_K_M quantized GGUF version of slim-tags, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. \n\nslim-tags is part of the SLIM (\"Structured Language Instruction Model\") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.\n\nTo pull the model via API: \n\n from huggingface_hub import snapshot_download \n snapshot_download(\"llmware/slim-tags-tool\", local_dir=\"/path/on/your/machine/\", local_dir_use_symlinks=False) \n \n\nLoad in your favorite GGUF inference engine, or try with llmware as follows:\n\n from URL import ModelCatalog \n \n # to load the model and make a basic inference\n model = ModelCatalog().load_model(\"slim-tags-tool\")\n response = model.function_call(text_sample) \n\n # this one line will download the model and run a series of tests\n ModelCatalog().tool_test_run(\"slim-tags-tool\", verbose=True) \n\n\nSlim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:\n\n from URL import LLMfx\n\n llm_fx = LLMfx()\n llm_fx.load_tool(\"tags\")\n response = llm_fx.tags(text) \n\n\nNote: please review URL in the repository for prompt wrapping information, details on the model, and full test set.## Model Card Contact\n\nDarren Oberst & llmware team \n\nAny questions? Join us on Discord"
] | [
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] |
null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.24107839167118073
f1_macro: 0.9115304360143613
f1_micro: 0.9161147902869757
f1_weighted: 0.9148487660457992
precision_macro: 0.9236703332921353
precision_micro: 0.9161147902869757
precision_weighted: 0.9155415645923862
recall_macro: 0.90135285639413
recall_micro: 0.9161147902869757
recall_weighted: 0.9161147902869757
accuracy: 0.9161147902869757
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-apx4w-fe68z/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | text-classification | MoritzLaurer/autotrain-apx4w-fe68z | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"dataset:autotrain-apx4w-fe68z/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:40:03+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-apx4w-fe68z/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.24107839167118073
f1_macro: 0.9115304360143613
f1_micro: 0.9161147902869757
f1_weighted: 0.9148487660457992
precision_macro: 0.9236703332921353
precision_micro: 0.9161147902869757
precision_weighted: 0.9155415645923862
recall_macro: 0.90135285639413
recall_micro: 0.9161147902869757
recall_weighted: 0.9161147902869757
accuracy: 0.9161147902869757
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Text Classification",
"## Validation Metrics\nloss: 0.24107839167118073\n\nf1_macro: 0.9115304360143613\n\nf1_micro: 0.9161147902869757\n\nf1_weighted: 0.9148487660457992\n\nprecision_macro: 0.9236703332921353\n\nprecision_micro: 0.9161147902869757\n\nprecision_weighted: 0.9155415645923862\n\nrecall_macro: 0.90135285639413\n\nrecall_micro: 0.9161147902869757\n\nrecall_weighted: 0.9161147902869757\n\naccuracy: 0.9161147902869757"
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-apx4w-fe68z/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Text Classification",
"## Validation Metrics\nloss: 0.24107839167118073\n\nf1_macro: 0.9115304360143613\n\nf1_micro: 0.9161147902869757\n\nf1_weighted: 0.9148487660457992\n\nprecision_macro: 0.9236703332921353\n\nprecision_micro: 0.9161147902869757\n\nprecision_weighted: 0.9155415645923862\n\nrecall_macro: 0.90135285639413\n\nrecall_micro: 0.9161147902869757\n\nrecall_weighted: 0.9161147902869757\n\naccuracy: 0.9161147902869757"
] | [
64,
16,
150
] | [
"passage: TAGS\n#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-apx4w-fe68z/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Text Classification## Validation Metrics\nloss: 0.24107839167118073\n\nf1_macro: 0.9115304360143613\n\nf1_micro: 0.9161147902869757\n\nf1_weighted: 0.9148487660457992\n\nprecision_macro: 0.9236703332921353\n\nprecision_micro: 0.9161147902869757\n\nprecision_weighted: 0.9155415645923862\n\nrecall_macro: 0.90135285639413\n\nrecall_micro: 0.9161147902869757\n\nrecall_weighted: 0.9161147902869757\n\naccuracy: 0.9161147902869757"
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null | null | transformers |
Run Details : https://wandb.ai/s-haturusinghe/finetune-after_mrp-with_pipeline-updated/runs/e0cwjvvz/overview?workspace=user-haturusinghe
Run summary:
eval/f1_0 0.87064
eval/f1_1 0.81809
eval/f1_macro 0.84437
eval/f1_weighted 0.8493
eval/loss 0.43726
eval/precision_0 0.88518
eval/precision_1 0.79962
eval/precision_weighted 0.85044
eval/recall_0 0.85657
eval/recall_1 0.83744
eval/recall_weighted 0.8488
eval/runtime 74.0515
eval/samples_per_second 33.76
eval/steps_per_second 2.12
train/epoch 5.0
train/global_step 2345
train/learning_rate 0.0
train/loss 0.2158
train/total_flos 9866664576000000.0
train/train_loss 0.38705
train/train_runtime 3869.0269
train/train_samples_per_second 9.692
train/train_steps_per_second 0.606
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "transformers", "tags": []} | feature-extraction | haturusinghe/f_84.43_xlm-r-base-finetuned_prefinetuned-mrp_adamW_2e-5-16-5 | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:43:45+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #xlm-roberta #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
Run Details : URL
Run summary:
eval/f1_0 0.87064
eval/f1_1 0.81809
eval/f1_macro 0.84437
eval/f1_weighted 0.8493
eval/loss 0.43726
eval/precision_0 0.88518
eval/precision_1 0.79962
eval/precision_weighted 0.85044
eval/recall_0 0.85657
eval/recall_1 0.83744
eval/recall_weighted 0.8488
eval/runtime 74.0515
eval/samples_per_second 33.76
eval/steps_per_second 2.12
train/epoch 5.0
train/global_step 2345
train/learning_rate 0.0
train/loss 0.2158
train/total_flos 9866664576000000.0
train/train_loss 0.38705
train/train_runtime 3869.0269
train/train_samples_per_second 9.692
train/train_steps_per_second 0.606
# Model Card for Model ID
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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] |
null | null | null |
## Exllama v2 Quantizations of DeepMagic-Coder-7b-Alt
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/rombodawg/DeepMagic-Coder-7b-Alt
No GQA - VRAM requirements will be higher
| Branch | Bits | lm_head bits | Size (4k) | Size (16k) | Description |
| -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- |
| [8_0](https://huggingface.co/Bartowski/DeepMagic-Coder-7b-Alt-exl2/tree/8_0) | 8.0 | 8.0 | 9.4 GB | 15.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/Bartowski/DeepMagic-Coder-7b-Alt-exl2/tree/6_5) | 6.5 | 8.0 | 8.6 GB | 14.8 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [5_0](https://huggingface.co/Bartowski/DeepMagic-Coder-7b-Alt-exl2/tree/5_0) | 5.0 | 6.0 | 7.2 GB | 13.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. |
| [4_25](https://huggingface.co/Bartowski/DeepMagic-Coder-7b-Alt-exl2/tree/4_25) | 4.25 | 6.0 | 6.5 GB | 12.7 GB | GPTQ equivalent bits per weight. |
| [3_5](https://huggingface.co/Bartowski/DeepMagic-Coder-7b-Alt-exl2/tree/3_5) | 3.5 | 6.0 | 5.9 GB | 12.1 GB | Lower quality, not recommended. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/DeepMagic-Coder-7b-Alt-exl2 DeepMagic-Coder-7b-Alt-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `DeepMagic-Coder-7b-Alt-exl2`:
```shell
mkdir DeepMagic-Coder-7b-Alt-exl2
huggingface-cli download bartowski/DeepMagic-Coder-7b-Alt-exl2 --local-dir DeepMagic-Coder-7b-Alt-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir DeepMagic-Coder-7b-Alt-exl2-6_5
huggingface-cli download bartowski/DeepMagic-Coder-7b-Alt-exl2 --revision 6_5 --local-dir DeepMagic-Coder-7b-Alt-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir DeepMagic-Coder-7b-Alt-exl2-6.5
huggingface-cli download bartowski/DeepMagic-Coder-7b-Alt-exl2 --revision 6_5 --local-dir DeepMagic-Coder-7b-Alt-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski | {"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/DeepMagic-Coder-7b-Alt-exl2 | [
"text-generation",
"license:other",
"region:us"
] | 2024-02-07T11:43:52+00:00 | [] | [] | TAGS
#text-generation #license-other #region-us
| Exllama v2 Quantizations of DeepMagic-Coder-7b-Alt
--------------------------------------------------
Using <a href="URL ExLlamaV2 v0.0.13 for quantization.
The "main" branch only contains the URL, download one of the other branches for the model (see below)
=====================================================================================================
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
No GQA - VRAM requirements will be higher
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download the 'main' (only useful if you only care about URL) branch to a folder called 'DeepMagic-Coder-7b-Alt-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#text-generation #license-other #region-us \n"
] | [
16
] | [
"passage: TAGS\n#text-generation #license-other #region-us \n"
] | [
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-Mistral-Finetune-1
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0554
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8941 | 0.05 | 25 | 1.7724 |
| 1.8315 | 0.1 | 50 | 1.7261 |
| 1.7522 | 0.14 | 75 | 1.6971 |
| 1.6974 | 0.19 | 100 | 1.6678 |
| 1.7149 | 0.24 | 125 | 1.6430 |
| 1.6037 | 0.29 | 150 | 1.6201 |
| 1.6611 | 0.34 | 175 | 1.6057 |
| 1.7131 | 0.38 | 200 | 1.5854 |
| 1.7619 | 0.43 | 225 | 1.5696 |
| 1.6062 | 0.48 | 250 | 1.5494 |
| 1.5171 | 0.53 | 275 | 1.5284 |
| 1.6484 | 0.58 | 300 | 1.5091 |
| 1.7207 | 0.62 | 325 | 1.4958 |
| 1.6548 | 0.67 | 350 | 1.4817 |
| 1.6447 | 0.72 | 375 | 1.4746 |
| 1.5294 | 0.77 | 400 | 1.4358 |
| 1.6865 | 0.82 | 425 | 1.4269 |
| 1.4704 | 0.87 | 450 | 1.3963 |
| 1.4935 | 0.91 | 475 | 1.3714 |
| 1.4714 | 0.96 | 500 | 1.3496 |
| 1.4913 | 1.01 | 525 | 1.3327 |
| 1.3627 | 1.06 | 550 | 1.3060 |
| 1.2748 | 1.11 | 575 | 1.2857 |
| 1.1856 | 1.15 | 600 | 1.2624 |
| 1.1102 | 1.2 | 625 | 1.2413 |
| 1.2375 | 1.25 | 650 | 1.2214 |
| 1.2421 | 1.3 | 675 | 1.1989 |
| 1.1946 | 1.35 | 700 | 1.1823 |
| 1.2389 | 1.39 | 725 | 1.1674 |
| 1.2961 | 1.44 | 750 | 1.1567 |
| 1.1831 | 1.49 | 775 | 1.1566 |
| 1.2144 | 1.54 | 800 | 1.1326 |
| 1.2881 | 1.59 | 825 | 1.1279 |
| 1.2584 | 1.63 | 850 | 1.1073 |
| 1.2837 | 1.68 | 875 | 1.0878 |
| 1.1251 | 1.73 | 900 | 1.0812 |
| 1.0938 | 1.78 | 925 | 1.0706 |
| 1.0304 | 1.83 | 950 | 1.0636 |
| 1.313 | 1.88 | 975 | 1.0676 |
| 1.2245 | 1.92 | 1000 | 1.0604 |
| 1.1293 | 1.97 | 1025 | 1.0554 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-Mistral-Finetune-1", "results": []}]} | null | Maaz911/mistral-Mistral-Finetune-1 | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-07T11:44:38+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
| mistral-Mistral-Finetune-1
==========================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0554
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2.5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 2
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.0.dev0
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
45,
116,
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"passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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] |
null | null | sentence-transformers |
# liuyuweitarek/sbert-base-chinese-nli-ecommerce-all-cleared-finetuned-confusion_1
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("liuyuweitarek/sbert-base-chinese-nli-ecommerce-all-cleared-finetuned-confusion_1")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
| {"license": "apache-2.0", "tags": ["setfit", "sentence-transformers", "text-classification"], "pipeline_tag": "text-classification"} | text-classification | liuyuweitarek/sbert-base-chinese-nli-ecommerce-all-cleared-finetuned-confusion_1 | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | 2024-02-07T11:44:44+00:00 | [
"2209.11055"
] | [] | TAGS
#sentence-transformers #safetensors #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us
|
# liuyuweitarek/sbert-base-chinese-nli-ecommerce-all-cleared-finetuned-confusion_1
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
You can then run inference as follows:
## BibTeX entry and citation info
| [
"# liuyuweitarek/sbert-base-chinese-nli-ecommerce-all-cleared-finetuned-confusion_1\n\nThis is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:\n\n1. Fine-tuning a Sentence Transformer with contrastive learning.\n2. Training a classification head with features from the fine-tuned Sentence Transformer.",
"## Usage\n\nTo use this model for inference, first install the SetFit library:\n\n\n\nYou can then run inference as follows:",
"## BibTeX entry and citation info"
] | [
"TAGS\n#sentence-transformers #safetensors #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us \n",
"# liuyuweitarek/sbert-base-chinese-nli-ecommerce-all-cleared-finetuned-confusion_1\n\nThis is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:\n\n1. Fine-tuning a Sentence Transformer with contrastive learning.\n2. Training a classification head with features from the fine-tuned Sentence Transformer.",
"## Usage\n\nTo use this model for inference, first install the SetFit library:\n\n\n\nYou can then run inference as follows:",
"## BibTeX entry and citation info"
] | [
43,
100,
29,
10
] | [
"passage: TAGS\n#sentence-transformers #safetensors #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us \n# liuyuweitarek/sbert-base-chinese-nli-ecommerce-all-cleared-finetuned-confusion_1\n\nThis is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:\n\n1. Fine-tuning a Sentence Transformer with contrastive learning.\n2. Training a classification head with features from the fine-tuned Sentence Transformer.## Usage\n\nTo use this model for inference, first install the SetFit library:\n\n\n\nYou can then run inference as follows:## BibTeX entry and citation info"
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null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | alexgastev/Reinforce-CartPole-v1 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-07T11:46:43+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | transformers |
**A distilBERT based SQL Injection Detection Model**
**Model description**
This model, based on DistilBERT, is specifically tailored for the detection of SQL injection attacks.
Through fine-tuning using the Hugging Face's Trainer API, the model has been trained to identify potentially malicious SQL queries with high accuracy.
- __Architecture:__ DistilBERT
- __Fine-tuning Method:__ Trainer API
- __Performance Metrics:__
- __F1-score:__ 0.9986
- __Accuracy:__ 99.90%
- __Training Epochs:__ 6
**Dataset description**
The model was fine-tuned on the SQL Injection Dataset, curated and made available by SAJID576 on Kaggle.
This dataset comprises of 30,920 rows of SQL queries, including both benign and malicious examples, providing a comprehensive training corpus for robust model development.
- __Dataset Source:__ https://www.kaggle.com/datasets/sajid576/sql-injection-dataset/data
- __Size:__ 30,920 rows | {"license": "mit", "tags": ["SQL Injection"], "metrics": ["f1", "accuracy"], "widget": [{"text": "1 UNION SELECT username, password FROM users --", "example_title": "SQL Injection Detection"}]} | text-classification | cybersectony/sql-injection-attack-detection-distilbert | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"SQL Injection",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:50:21+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #SQL Injection #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
A distilBERT based SQL Injection Detection Model
Model description
This model, based on DistilBERT, is specifically tailored for the detection of SQL injection attacks.
Through fine-tuning using the Hugging Face's Trainer API, the model has been trained to identify potentially malicious SQL queries with high accuracy.
- __Architecture:__ DistilBERT
- __Fine-tuning Method:__ Trainer API
- __Performance Metrics:__
- __F1-score:__ 0.9986
- __Accuracy:__ 99.90%
- __Training Epochs:__ 6
Dataset description
The model was fine-tuned on the SQL Injection Dataset, curated and made available by SAJID576 on Kaggle.
This dataset comprises of 30,920 rows of SQL queries, including both benign and malicious examples, providing a comprehensive training corpus for robust model development.
- __Dataset Source:__ URL
- __Size:__ 30,920 rows | [] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #SQL Injection #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #SQL Injection #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks-open-close
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0100
- Accuracy: 0.9983
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0866 | 1.0 | 209 | 0.0388 | 0.9956 |
| 0.021 | 2.0 | 419 | 0.0162 | 0.9978 |
| 0.0172 | 3.0 | 629 | 0.0102 | 0.9985 |
| 0.0195 | 4.0 | 839 | 0.0083 | 0.9991 |
| 0.0188 | 4.98 | 1045 | 0.0100 | 0.9983 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-ks-open-close", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.998286586955712, "name": "Accuracy"}]}]}]} | audio-classification | iamhack/wav2vec2-base-finetuned-ks-open-close | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:52:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks-open-close
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0100
* Accuracy: 0.9983
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
78,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn_fine_tuned
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3711
- Rouge1: 64.5245
- Rouge2: 53.1381
- Rougel: 47.3234
- Rougelsum: 51.2042
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.4228 | 1.0 | 389 | 0.3821 | 57.8993 | 45.4774 | 41.9455 | 44.9012 |
| 0.321 | 2.0 | 778 | 0.3641 | 61.5071 | 49.6584 | 45.5774 | 48.3601 |
| 0.2764 | 3.0 | 1167 | 0.3689 | 63.7295 | 52.1907 | 46.827 | 50.3726 |
| 0.2504 | 4.0 | 1556 | 0.3711 | 64.5245 | 53.1381 | 47.3234 | 51.2042 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-large-cnn_fine_tuned", "results": []}]} | summarization | razvanfischer/bart-large-cnn_fine_tuned | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:53:40+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bart #text2text-generation #summarization #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn\_fine\_tuned
===========================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3711
* Rouge1: 64.5245
* Rouge2: 53.1381
* Rougel: 47.3234
* Rougelsum: 51.2042
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5.6e-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
73,
99,
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27
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bart #text2text-generation #summarization #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scibert_scivocab_uncased-finetuned-molstm-mlm-0.3-5epochs
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6660
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0053 | 1.0 | 512 | 0.8043 |
| 0.8263 | 2.0 | 1024 | 0.7319 |
| 0.7641 | 3.0 | 1536 | 0.6908 |
| 0.7303 | 4.0 | 2048 | 0.6738 |
| 0.7174 | 5.0 | 2560 | 0.6644 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "scibert_scivocab_uncased-finetuned-molstm-mlm-0.3-5epochs", "results": []}]} | fill-mask | matr1xx/scibert_scivocab_uncased-finetuned-molstm-mlm-0.3-5epochs | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T11:54:30+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us
| scibert\_scivocab\_uncased-finetuned-molstm-mlm-0.3-5epochs
===========================================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6660
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.1
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
62,
113,
4,
30
] | [
"passage: TAGS\n#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
# Llama-Latxa-7b
Llama-Latxa-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [HiTZ/latxa-7b-v1](https://huggingface.co/HiTZ/latxa-7b-v1)
* [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: HiTZ/latxa-7b-v1
layer_range: [0, 32]
- model: meta-llama/Llama-2-7b-hf
layer_range: [0, 32]
merge_method: slerp
base_model: HiTZ/latxa-7b-v1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "airalribalta/Llama-Latxa-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "HiTZ/latxa-7b-v1", "meta-llama/Llama-2-7b-hf"], "base_model": ["HiTZ/latxa-7b-v1", "meta-llama/Llama-2-7b-hf"]} | text-generation | airalribalta/Llama-Latxa-7b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"HiTZ/latxa-7b-v1",
"meta-llama/Llama-2-7b-hf",
"base_model:HiTZ/latxa-7b-v1",
"base_model:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T11:57:47+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Llama-Latxa-7b
Llama-Latxa-7b is a merge of the following models using LazyMergekit:
* HiTZ/latxa-7b-v1
* meta-llama/Llama-2-7b-hf
## Configuration
## Usage
| [
"# Llama-Latxa-7b\n\nLlama-Latxa-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-hf",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Llama-Latxa-7b\n\nLlama-Latxa-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-hf",
"## Configuration",
"## Usage"
] | [
118,
54,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #HiTZ/latxa-7b-v1 #meta-llama/Llama-2-7b-hf #base_model-HiTZ/latxa-7b-v1 #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Llama-Latxa-7b\n\nLlama-Latxa-7b is a merge of the following models using LazyMergekit:\n* HiTZ/latxa-7b-v1\n* meta-llama/Llama-2-7b-hf## Configuration## Usage"
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter_v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "14.50 +/- 15.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | alexgastev/Reinforce-PixelCopter_v1 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-07T12:00:12+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
41,
58
] | [
"passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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] |
null | null | diffusers |
# controlnet-kixr/model_out
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "controlnet"], "base_model": "stabilityai/stable-diffusion-2-1-base", "inference": true} | text-to-image | kixr/model_out | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"diffusers:ControlNetModel",
"region:us"
] | 2024-02-07T12:02:40+00:00 | [] | [] | TAGS
#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #controlnet #base_model-stabilityai/stable-diffusion-2-1-base #license-creativeml-openrail-m #diffusers-ControlNetModel #region-us
|
# controlnet-kixr/model_out
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning.
| [
"# controlnet-kixr/model_out\n\nThese are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning."
] | [
"TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #controlnet #base_model-stabilityai/stable-diffusion-2-1-base #license-creativeml-openrail-m #diffusers-ControlNetModel #region-us \n",
"# controlnet-kixr/model_out\n\nThese are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning."
] | [
81,
41
] | [
"passage: TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #controlnet #base_model-stabilityai/stable-diffusion-2-1-base #license-creativeml-openrail-m #diffusers-ControlNetModel #region-us \n# controlnet-kixr/model_out\n\nThese are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning."
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Fine-Tuned_Model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "Fine-Tuned_Model", "results": []}]} | image-classification | arpanl/Fine-Tuned_Model | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:03:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Fine-Tuned_Model
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 50",
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"# Fine-Tuned_Model\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 50",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
79,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Fine-Tuned_Model\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 50### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | mertllc/mms-tts-tur-twenties-male | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:05:47+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"passage: TAGS\n#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SMIDS_3x_beit_large_SGD_lr00001_fold3
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9632
- Accuracy: 0.5317
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7822 | 1.0 | 450 | 1.6462 | 0.2883 |
| 1.5926 | 2.0 | 900 | 1.5830 | 0.2933 |
| 1.5914 | 3.0 | 1350 | 1.5253 | 0.29 |
| 1.5233 | 4.0 | 1800 | 1.4725 | 0.285 |
| 1.5278 | 5.0 | 2250 | 1.4239 | 0.2783 |
| 1.3754 | 6.0 | 2700 | 1.3796 | 0.2783 |
| 1.2727 | 7.0 | 3150 | 1.3388 | 0.28 |
| 1.3145 | 8.0 | 3600 | 1.3018 | 0.2883 |
| 1.2291 | 9.0 | 4050 | 1.2682 | 0.2967 |
| 1.2148 | 10.0 | 4500 | 1.2381 | 0.305 |
| 1.2208 | 11.0 | 4950 | 1.2108 | 0.3217 |
| 1.223 | 12.0 | 5400 | 1.1867 | 0.335 |
| 1.2283 | 13.0 | 5850 | 1.1653 | 0.35 |
| 1.2057 | 14.0 | 6300 | 1.1463 | 0.3783 |
| 1.154 | 15.0 | 6750 | 1.1293 | 0.4 |
| 1.1272 | 16.0 | 7200 | 1.1141 | 0.4167 |
| 1.0506 | 17.0 | 7650 | 1.1002 | 0.4283 |
| 1.1437 | 18.0 | 8100 | 1.0878 | 0.445 |
| 1.0703 | 19.0 | 8550 | 1.0767 | 0.45 |
| 1.1566 | 20.0 | 9000 | 1.0665 | 0.455 |
| 1.0999 | 21.0 | 9450 | 1.0573 | 0.4617 |
| 1.0605 | 22.0 | 9900 | 1.0487 | 0.47 |
| 1.0662 | 23.0 | 10350 | 1.0409 | 0.4833 |
| 1.0836 | 24.0 | 10800 | 1.0338 | 0.4883 |
| 0.9979 | 25.0 | 11250 | 1.0273 | 0.4867 |
| 1.08 | 26.0 | 11700 | 1.0212 | 0.49 |
| 1.0324 | 27.0 | 12150 | 1.0156 | 0.4933 |
| 0.942 | 28.0 | 12600 | 1.0104 | 0.495 |
| 0.9871 | 29.0 | 13050 | 1.0056 | 0.5017 |
| 1.0152 | 30.0 | 13500 | 1.0011 | 0.5017 |
| 1.0006 | 31.0 | 13950 | 0.9969 | 0.5067 |
| 1.0105 | 32.0 | 14400 | 0.9931 | 0.5083 |
| 1.0465 | 33.0 | 14850 | 0.9896 | 0.5083 |
| 1.0039 | 34.0 | 15300 | 0.9863 | 0.5083 |
| 0.9997 | 35.0 | 15750 | 0.9833 | 0.5117 |
| 0.9516 | 36.0 | 16200 | 0.9805 | 0.5117 |
| 0.9946 | 37.0 | 16650 | 0.9779 | 0.5133 |
| 0.9629 | 38.0 | 17100 | 0.9756 | 0.515 |
| 0.915 | 39.0 | 17550 | 0.9735 | 0.5167 |
| 0.9972 | 40.0 | 18000 | 0.9716 | 0.5183 |
| 0.9144 | 41.0 | 18450 | 0.9699 | 0.5233 |
| 0.9793 | 42.0 | 18900 | 0.9684 | 0.5233 |
| 0.9515 | 43.0 | 19350 | 0.9671 | 0.5267 |
| 0.9971 | 44.0 | 19800 | 0.9660 | 0.5267 |
| 0.9882 | 45.0 | 20250 | 0.9651 | 0.53 |
| 0.8815 | 46.0 | 20700 | 0.9644 | 0.53 |
| 1.0148 | 47.0 | 21150 | 0.9639 | 0.53 |
| 0.9753 | 48.0 | 21600 | 0.9635 | 0.53 |
| 1.01 | 49.0 | 22050 | 0.9633 | 0.5317 |
| 0.9788 | 50.0 | 22500 | 0.9632 | 0.5317 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_SGD_lr00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5316666666666666, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_SGD_lr00001_fold3 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:06:56+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_SGD\_lr00001\_fold3
===========================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9632
* Accuracy: 0.5317
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
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"### Training results",
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] | [
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"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SMIDS_3x_beit_large_SGD_lr0001_fold3
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3694
- Accuracy: 0.8583
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2913 | 1.0 | 450 | 1.1953 | 0.3317 |
| 0.9832 | 2.0 | 900 | 0.9935 | 0.505 |
| 0.9341 | 3.0 | 1350 | 0.8935 | 0.5667 |
| 0.8139 | 4.0 | 1800 | 0.8162 | 0.6333 |
| 0.8114 | 5.0 | 2250 | 0.7493 | 0.67 |
| 0.6638 | 6.0 | 2700 | 0.6967 | 0.7117 |
| 0.6022 | 7.0 | 3150 | 0.6543 | 0.74 |
| 0.5953 | 8.0 | 3600 | 0.6194 | 0.7567 |
| 0.5236 | 9.0 | 4050 | 0.5910 | 0.7717 |
| 0.5225 | 10.0 | 4500 | 0.5665 | 0.7817 |
| 0.5719 | 11.0 | 4950 | 0.5455 | 0.7867 |
| 0.5364 | 12.0 | 5400 | 0.5275 | 0.7983 |
| 0.5129 | 13.0 | 5850 | 0.5115 | 0.8033 |
| 0.4843 | 14.0 | 6300 | 0.4973 | 0.8067 |
| 0.4301 | 15.0 | 6750 | 0.4850 | 0.8083 |
| 0.4638 | 16.0 | 7200 | 0.4740 | 0.8117 |
| 0.3812 | 17.0 | 7650 | 0.4639 | 0.8217 |
| 0.4108 | 18.0 | 8100 | 0.4553 | 0.825 |
| 0.3931 | 19.0 | 8550 | 0.4471 | 0.825 |
| 0.5137 | 20.0 | 9000 | 0.4395 | 0.8283 |
| 0.4461 | 21.0 | 9450 | 0.4326 | 0.8317 |
| 0.4144 | 22.0 | 9900 | 0.4264 | 0.8317 |
| 0.4308 | 23.0 | 10350 | 0.4210 | 0.8367 |
| 0.4889 | 24.0 | 10800 | 0.4164 | 0.8383 |
| 0.3477 | 25.0 | 11250 | 0.4118 | 0.8367 |
| 0.3919 | 26.0 | 11700 | 0.4078 | 0.8367 |
| 0.3611 | 27.0 | 12150 | 0.4039 | 0.8383 |
| 0.4086 | 28.0 | 12600 | 0.3997 | 0.8433 |
| 0.3357 | 29.0 | 13050 | 0.3968 | 0.845 |
| 0.3912 | 30.0 | 13500 | 0.3940 | 0.845 |
| 0.4212 | 31.0 | 13950 | 0.3910 | 0.8467 |
| 0.3725 | 32.0 | 14400 | 0.3887 | 0.8467 |
| 0.4054 | 33.0 | 14850 | 0.3860 | 0.8467 |
| 0.3701 | 34.0 | 15300 | 0.3836 | 0.8483 |
| 0.3718 | 35.0 | 15750 | 0.3817 | 0.8467 |
| 0.3515 | 36.0 | 16200 | 0.3800 | 0.855 |
| 0.3194 | 37.0 | 16650 | 0.3784 | 0.855 |
| 0.3277 | 38.0 | 17100 | 0.3769 | 0.8567 |
| 0.3054 | 39.0 | 17550 | 0.3757 | 0.8567 |
| 0.4203 | 40.0 | 18000 | 0.3745 | 0.8583 |
| 0.3152 | 41.0 | 18450 | 0.3735 | 0.8583 |
| 0.3807 | 42.0 | 18900 | 0.3725 | 0.8583 |
| 0.3981 | 43.0 | 19350 | 0.3717 | 0.8583 |
| 0.3342 | 44.0 | 19800 | 0.3711 | 0.8583 |
| 0.4105 | 45.0 | 20250 | 0.3705 | 0.8583 |
| 0.308 | 46.0 | 20700 | 0.3701 | 0.8583 |
| 0.3965 | 47.0 | 21150 | 0.3698 | 0.8583 |
| 0.3568 | 48.0 | 21600 | 0.3696 | 0.8583 |
| 0.4314 | 49.0 | 22050 | 0.3695 | 0.8583 |
| 0.3486 | 50.0 | 22500 | 0.3694 | 0.8583 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_SGD_lr0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8583333333333333, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_SGD_lr0001_fold3 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:07:18+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_SGD\_lr0001\_fold3
==========================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3694
* Accuracy: 0.8583
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
"TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
81,
115,
4,
30
] | [
"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-large-bn-adapter-6.34M-snli-model3
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6114
- Accuracy: 0.802
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 29
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3085 | 1.0 | 17168 | 0.2388 | 0.9152 |
| 0.2835 | 2.0 | 34336 | 0.2279 | 0.9194 |
| 0.2635 | 3.0 | 51504 | 0.2286 | 0.9223 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "t5-large", "model-index": [{"name": "t5-large-bn-adapter-6.34M-snli-model3", "results": []}]} | null | varun-v-rao/t5-large-bn-adapter-6.34M-snli-model3 | [
"tensorboard",
"generated_from_trainer",
"base_model:t5-large",
"license:apache-2.0",
"region:us"
] | 2024-02-07T12:09:43+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #base_model-t5-large #license-apache-2.0 #region-us
| t5-large-bn-adapter-6.34M-snli-model3
=====================================
This model is a fine-tuned version of t5-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6114
* Accuracy: 0.802
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 29
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 29\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 29\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
34,
98,
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"passage: TAGS\n#tensorboard #generated_from_trainer #base_model-t5-large #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 29\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-1.3b-snli-model2
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0681
- Accuracy: 0.7815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2945 | 1.0 | 4292 | 0.2489 | 0.9114 |
| 0.1849 | 2.0 | 8584 | 0.2324 | 0.9223 |
| 0.0907 | 3.0 | 12876 | 0.3191 | 0.9188 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-snli-model2", "results": []}]} | text-classification | varun-v-rao/opt-1.3b-snli-model2 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-classification",
"generated_from_trainer",
"base_model:facebook/opt-1.3b",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T12:09:52+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-1.3b #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| opt-1.3b-snli-model2
====================
This model is a fine-tuned version of facebook/opt-1.3b on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0681
* Accuracy: 0.7815
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-1.3b #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
75,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-1.3b #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | smangrul/sticker_peft_model | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:10:34+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Cloud Provider:
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- Carbon Emitted:
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-bn-adapter-3.17M-snli-model2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6172
- Accuracy: 0.8015
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 27
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3134 | 1.0 | 8584 | 0.2371 | 0.9160 |
| 0.2891 | 2.0 | 17168 | 0.2228 | 0.9224 |
| 0.2792 | 3.0 | 25752 | 0.2222 | 0.9237 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-bn-adapter-3.17M-snli-model2", "results": []}]} | null | varun-v-rao/roberta-large-bn-adapter-3.17M-snli-model2 | [
"tensorboard",
"generated_from_trainer",
"base_model:roberta-large",
"license:mit",
"region:us"
] | 2024-02-07T12:11:26+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #base_model-roberta-large #license-mit #region-us
| roberta-large-bn-adapter-3.17M-snli-model2
==========================================
This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6172
* Accuracy: 0.8015
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 27
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.1+cu121
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 27\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 27\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-large #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 27\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# new-dot-comp-v1
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "new-dot-comp-v1", "results": []}]} | text-generation | newbie-geek/new-dot-comp-v1 | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T12:13:03+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# new-dot-comp-v1
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# new-dot-comp-v1\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# new-dot-comp-v1\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
98,
43,
6,
12,
8,
3,
125,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# new-dot-comp-v1\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 12\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| {"library_name": "transformers", "tags": []} | null | Aneesha/phi2_DPO | [
"transformers",
"safetensors",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | gmonsoon/Miapuccino-Indo-v2-adapter | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | asifhaider/asif-valid-all-codellama-instruct-ft | [
"transformers",
"safetensors",
"llama",
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|
# Model Card for Model ID
## Model Details
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- Developed by:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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] |
null | null | transformers |
# updated to V4, after this version, OpenMia will be finetuned (branched) to some Indonesia local languages, such as Javanese, Sundanese, and Minang language. Stay tuned.
# MIA : (M)istral finetuned with (I)ndonesia language from (A)lpaca dataset
(formerly named Mistral-7b-Alpaca-Indonesia)
OpenMia-Indo-Mistral-7b is finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia.
Due to limited resources, this model is still in alpha stage.
Want to contribute to this project? join our organization: https://huggingface.co/indischepartij or contact me at https://twitter.com/gmonsooniii
# Modelfile/Prompt format
```markdown
SYSTEM Kamu adalah asisten AI yang cerdas dan ceria, bernama Mia.
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>
TEMPLATE <|im_start|>system {{ .System }}<|im_end|> <|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assistant
``` | {"language": ["en", "id"], "license": "cc-by-nc-4.0", "tags": ["text-generation-inference", "transformers", "mistral", "trl"], "datasets": ["MBZUAI/Bactrian-X"], "base_model": "mistral-7b"} | text-generation | indischepartij/OpenMia-Indo-Mistral-7b-v4 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"trl",
"en",
"id",
"dataset:MBZUAI/Bactrian-X",
"base_model:mistral-7b",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:21:30+00:00 | [] | [
"en",
"id"
] | TAGS
#transformers #safetensors #mistral #text-generation #text-generation-inference #trl #en #id #dataset-MBZUAI/Bactrian-X #base_model-mistral-7b #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
!image/png
# updated to V4, after this version, OpenMia will be finetuned (branched) to some Indonesia local languages, such as Javanese, Sundanese, and Minang language. Stay tuned.
# MIA : (M)istral finetuned with (I)ndonesia language from (A)lpaca dataset
(formerly named Mistral-7b-Alpaca-Indonesia)
OpenMia-Indo-Mistral-7b is finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia.
Due to limited resources, this model is still in alpha stage.
Want to contribute to this project? join our organization: URL or contact me at URL
# Modelfile/Prompt format
| [
"# updated to V4, after this version, OpenMia will be finetuned (branched) to some Indonesia local languages, such as Javanese, Sundanese, and Minang language. Stay tuned.",
"# MIA : (M)istral finetuned with (I)ndonesia language from (A)lpaca dataset\n(formerly named Mistral-7b-Alpaca-Indonesia)\n\nOpenMia-Indo-Mistral-7b is finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia.\n\nDue to limited resources, this model is still in alpha stage.\n\nWant to contribute to this project? join our organization: URL or contact me at URL",
"# Modelfile/Prompt format"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #trl #en #id #dataset-MBZUAI/Bactrian-X #base_model-mistral-7b #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# updated to V4, after this version, OpenMia will be finetuned (branched) to some Indonesia local languages, such as Javanese, Sundanese, and Minang language. Stay tuned.",
"# MIA : (M)istral finetuned with (I)ndonesia language from (A)lpaca dataset\n(formerly named Mistral-7b-Alpaca-Indonesia)\n\nOpenMia-Indo-Mistral-7b is finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia.\n\nDue to limited resources, this model is still in alpha stage.\n\nWant to contribute to this project? join our organization: URL or contact me at URL",
"# Modelfile/Prompt format"
] | [
87,
46,
108,
8
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #trl #en #id #dataset-MBZUAI/Bactrian-X #base_model-mistral-7b #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# updated to V4, after this version, OpenMia will be finetuned (branched) to some Indonesia local languages, such as Javanese, Sundanese, and Minang language. Stay tuned.# MIA : (M)istral finetuned with (I)ndonesia language from (A)lpaca dataset\n(formerly named Mistral-7b-Alpaca-Indonesia)\n\nOpenMia-Indo-Mistral-7b is finetuned model based of Mistral-7b with capability to do conversation in Bahasa Indonesia.\n\nDue to limited resources, this model is still in alpha stage.\n\nWant to contribute to this project? join our organization: URL or contact me at URL# Modelfile/Prompt format"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "facebook/mbart-large-50"} | null | SoniyaB/model | [
"peft",
"safetensors",
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"1910.09700"
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#peft #safetensors #arxiv-1910.09700 #base_model-facebook/mbart-large-50 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
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APA:
## Glossary [optional]
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null | null | diffusers |
# BRIA 2.2 ControlNet Depth Model Card
[***Click here for Demo***](https://huggingface.co/spaces/briaai/BRIA-2.2-ControlNet-Depth)
BRIA 2.2 ControlNet-Depth, trained on the foundation of [BRIA 2.2 Text-to-Image](https://huggingface.co/briaai/BRIA-2.2), enables the generation of high-quality images guided by a textual prompt and the extracted monocular depth estimation from an input image. This allows for the creation of different variations of an image, all sharing the same geometry.
[BRIA 2.2](https://huggingface.co/briaai/BRIA-2.2) was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
### Model Description
- **Developed by:** BRIA AI
- **Model type:** [ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet) for Latent diffusion
- **License:** [bria-2.2](https://bria.ai/bria-huggingface-model-license-agreement/)
- **Model Description:** ControlNet Depth for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the monocular depth estimation of the conditioned image.
- **Resources for more information:** [BRIA AI](https://bria.ai/)
### Get Access
BRIA 2.2 ControlNet-Depth requires access to BRIA 2.2 Text-to-Image. For more information, [click here](https://huggingface.co/briaai/BRIA-2.2).
### Code example using Diffusers
```
pip install diffusers
```
```py
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
import torch
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
def get_depth_map(image):
image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
with torch.no_grad(), torch.autocast("cuda"):
depth_map = depth_estimator(image).predicted_depth
image = transforms.functional.center_crop(image, min(image.shape[-2:]))
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=(1024, 1024),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = (depth_map - depth_min) / (depth_max - depth_min)
image = torch.cat([depth_map] * 3, dim=1)
image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
return image
controlnet = ControlNetModel.from_pretrained(
"briaai/BRIA-2.2-ControlNet-Depth",
torch_dtype=torch.float16
)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"briaai/BRIA-2.2",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.to("cuda")
prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background"
negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
# Calculate Depth image
input_image = cv2.imread('pics/singer.png')
depth_image = get_depth_map(input_image)
image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=depth_image, controlnet_conditioning_scale=1.0, height=1024, width=1024).images[0]
``` | {"license": "other", "tags": ["text-to-image", "controlnet model", "legal liability", "commercial use"], "license_name": "bria-2.2", "license_link": "https://bria.ai/customer-general-terms-and-conditions", "inference": false, "extra_gated_prompt": "This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}} | text-to-image | briaai/BRIA-2.2-ControlNet-Depth | [
"diffusers",
"text-to-image",
"controlnet model",
"legal liability",
"commercial use",
"license:other",
"has_space",
"diffusers:ControlNetModel",
"region:us"
] | 2024-02-07T12:21:48+00:00 | [] | [] | TAGS
#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us
|
# BRIA 2.2 ControlNet Depth Model Card
*Click here for Demo*
BRIA 2.2 ControlNet-Depth, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted monocular depth estimation from an input image. This allows for the creation of different variations of an image, all sharing the same geometry.
BRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
!controlnet_depth_showoff.png
### Model Description
- Developed by: BRIA AI
- Model type: ControlNet for Latent diffusion
- License: bria-2.2
- Model Description: ControlNet Depth for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the monocular depth estimation of the conditioned image.
- Resources for more information: BRIA AI
### Get Access
BRIA 2.2 ControlNet-Depth requires access to BRIA 2.2 Text-to-Image. For more information, click here.
### Code example using Diffusers
| [
"# BRIA 2.2 ControlNet Depth Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Depth, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted monocular depth estimation from an input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!controlnet_depth_showoff.png",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Depth for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the monocular depth estimation of the conditioned image.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.2 ControlNet-Depth requires access to BRIA 2.2 Text-to-Image. For more information, click here.",
"### Code example using Diffusers"
] | [
"TAGS\n#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us \n",
"# BRIA 2.2 ControlNet Depth Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Depth, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted monocular depth estimation from an input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!controlnet_depth_showoff.png",
"### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Depth for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the monocular depth estimation of the conditioned image.\n- Resources for more information: BRIA AI",
"### Get Access\nBRIA 2.2 ControlNet-Depth requires access to BRIA 2.2 Text-to-Image. For more information, click here.",
"### Code example using Diffusers"
] | [
46,
202,
83,
32,
8
] | [
"passage: TAGS\n#diffusers #text-to-image #controlnet model #legal liability #commercial use #license-other #has_space #diffusers-ControlNetModel #region-us \n# BRIA 2.2 ControlNet Depth Model Card\n\n\n*Click here for Demo*\n\n\nBRIA 2.2 ControlNet-Depth, trained on the foundation of BRIA 2.2 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted monocular depth estimation from an input image. This allows for the creation of different variations of an image, all sharing the same geometry. \n\n\nBRIA 2.2 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.\n\n!controlnet_depth_showoff.png### Model Description\n\n- Developed by: BRIA AI\n- Model type: ControlNet for Latent diffusion\n- License: bria-2.2\n\n- Model Description: ControlNet Depth for BRIA 2.2 Text-to-Image model. The model generates images guided by text and the monocular depth estimation of the conditioned image.\n- Resources for more information: BRIA AI### Get Access\nBRIA 2.2 ControlNet-Depth requires access to BRIA 2.2 Text-to-Image. For more information, click here.### Code example using Diffusers"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | OctavianB/MistralRoSummary | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Compute Region:
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## Technical Specifications [optional]
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null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 5.661193370819092
f1_macro: 0.014131400288297163
f1_micro: 0.03746085280655264
f1_weighted: 0.014145017633792991
precision_macro: 0.015760162960355265
precision_micro: 0.03746085280655264
precision_weighted: 0.015775349819387167
recall_macro: 0.03742478941034898
recall_micro: 0.03746085280655264
recall_weighted: 0.03746085280655264
accuracy: 0.03746085280655264
| {"tags": ["autotrain", "image-classification"], "datasets": ["footballer-recognition-2/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]} | image-classification | IsaacMwesigwa/footballer-recognition-2 | [
"transformers",
"safetensors",
"resnet",
"image-classification",
"autotrain",
"dataset:footballer-recognition-2/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:29:44+00:00 | [] | [] | TAGS
#transformers #safetensors #resnet #image-classification #autotrain #dataset-footballer-recognition-2/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 5.661193370819092
f1_macro: 0.014131400288297163
f1_micro: 0.03746085280655264
f1_weighted: 0.014145017633792991
precision_macro: 0.015760162960355265
precision_micro: 0.03746085280655264
precision_weighted: 0.015775349819387167
recall_macro: 0.03742478941034898
recall_micro: 0.03746085280655264
recall_weighted: 0.03746085280655264
accuracy: 0.03746085280655264
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 5.661193370819092\n\nf1_macro: 0.014131400288297163\n\nf1_micro: 0.03746085280655264\n\nf1_weighted: 0.014145017633792991\n\nprecision_macro: 0.015760162960355265\n\nprecision_micro: 0.03746085280655264\n\nprecision_weighted: 0.015775349819387167\n\nrecall_macro: 0.03742478941034898\n\nrecall_micro: 0.03746085280655264\n\nrecall_weighted: 0.03746085280655264\n\naccuracy: 0.03746085280655264"
] | [
"TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-footballer-recognition-2/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 5.661193370819092\n\nf1_macro: 0.014131400288297163\n\nf1_micro: 0.03746085280655264\n\nf1_weighted: 0.014145017633792991\n\nprecision_macro: 0.015760162960355265\n\nprecision_micro: 0.03746085280655264\n\nprecision_weighted: 0.015775349819387167\n\nrecall_macro: 0.03742478941034898\n\nrecall_micro: 0.03746085280655264\n\nrecall_weighted: 0.03746085280655264\n\naccuracy: 0.03746085280655264"
] | [
60,
16,
141
] | [
"passage: TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-footballer-recognition-2/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Image Classification## Validation Metricsg\nloss: 5.661193370819092\n\nf1_macro: 0.014131400288297163\n\nf1_micro: 0.03746085280655264\n\nf1_weighted: 0.014145017633792991\n\nprecision_macro: 0.015760162960355265\n\nprecision_micro: 0.03746085280655264\n\nprecision_weighted: 0.015775349819387167\n\nrecall_macro: 0.03742478941034898\n\nrecall_micro: 0.03746085280655264\n\nrecall_weighted: 0.03746085280655264\n\naccuracy: 0.03746085280655264"
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null | null | transformers |
# Books
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Fiction
#### Non-Fiction
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | Nandini0987654/Books | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:30:08+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Books
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Fiction
!Fiction
#### Non-Fiction
!Non-Fiction | [
"# Books\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Fiction\n\n!Fiction",
"#### Non-Fiction\n\n!Non-Fiction"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Books\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Fiction\n\n!Fiction",
"#### Non-Fiction\n\n!Non-Fiction"
] | [
54,
40,
4,
7,
11
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# Books\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### Fiction\n\n!Fiction#### Non-Fiction\n\n!Non-Fiction"
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] |
null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Paquique/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Paquique/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-07T12:30:08+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-th-cmv13-vanilla
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the cmv13-th-train+val dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-small", "model-index": [{"name": "whisper-small-th-cmv13-vanilla", "results": []}]} | automatic-speech-recognition | tensorops/whisper-small-th-cmv13-vanilla | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T12:30:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
|
# whisper-small-th-cmv13-vanilla
This model is a fine-tuned version of openai/whisper-small on the cmv13-th-train+val dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# whisper-small-th-cmv13-vanilla\n\nThis model is a fine-tuned version of openai/whisper-small on the cmv13-th-train+val dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 5000\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"# whisper-small-th-cmv13-vanilla\n\nThis model is a fine-tuned version of openai/whisper-small on the cmv13-th-train+val dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 5000\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n# whisper-small-th-cmv13-vanilla\n\nThis model is a fine-tuned version of openai/whisper-small on the cmv13-th-train+val dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 5000\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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] |
null | null | transformers |
# Kunocchini-7b-128k-test
Exl2 quant of [Test157t/Kunocchini-7b-128k-test](https://huggingface.co/Test157t/Kunocchini-7b-128k-test)
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) | {"library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral"], "base_model": ["SanjiWatsuki/Kunoichi-DPO-v2-7B", "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context"]} | text-generation | Kooten/Kunocchini-7b-128k-test-8bpw-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"alpaca",
"conversational",
"base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T12:30:35+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #conversational #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Kunocchini-7b-128k-test
Exl2 quant of Test157t/Kunocchini-7b-128k-test
## Contact
Kooten on discord
URL | [
"# Kunocchini-7b-128k-test\n\nExl2 quant of Test157t/Kunocchini-7b-128k-test",
"## Contact\nKooten on discord\n\nURL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #conversational #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kunocchini-7b-128k-test\n\nExl2 quant of Test157t/Kunocchini-7b-128k-test",
"## Contact\nKooten on discord\n\nURL"
] | [
113,
32,
7
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #conversational #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Kunocchini-7b-128k-test\n\nExl2 quant of Test157t/Kunocchini-7b-128k-test## Contact\nKooten on discord\n\nURL"
] | [
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] |
null | null | peft |
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<!-- 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 (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "TinyPixel/Llama-2-7B-bf16-sharded"} | null | sajaw/Llama-2-7B-XLLM-GQA10K | [
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Compute Region:
- Carbon Emitted:
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