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# 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. --> 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:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (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 <!-- This should link to a Dataset 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. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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 [More Information Needed] #### 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:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
null
Enginable/phi2_DPO-13B
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T15:28:36+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. - 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
[ "# 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 #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 #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
null
!pip install -q -U bitsandbytes
{"license": "apache-2.0"}
null
breebrenda/fineTuneMistralAI
[ "license:apache-2.0", "region:us" ]
2024-02-07T15:29:41+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
!pip install -q -U bitsandbytes
[]
[ "TAGS\n#license-apache-2.0 #region-us \n" ]
[ 14 ]
[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]
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null
null
generic
# Fork of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) for a `image-to-text` Inference endpoint. > Inspired by https://huggingface.co/sergeipetrov/blip_captioning This repository implements a `custom` task for `image-to-text` for 🤗 Inference Endpoints to allow image capturing. The code for the customized pipeline is in the handler.py. To use deploy this model an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file. ### expected Request payload Image to be labeled as binary. #### CURL ``` curl URL \ -X POST \ --data-binary @car.png \ -H "Content-Type: image/png" ``` #### Python ```python requests.post(ENDPOINT_URL, headers={"Content-Type": "image/png"}, data=open("car.png", 'rb').read()).json() ```
{"library_name": "generic", "tags": ["vision", "image-to-text", "endpoints-template"], "inference": false, "pipeline_tag": "image-to-text", "base_model": "Salesforce/blip-image-captioning-base"}
image-to-text
pimcore/IEP__image-capturing-base
[ "generic", "vision", "image-to-text", "endpoints-template", "base_model:Salesforce/blip-image-captioning-base", "endpoints_compatible", "region:us" ]
2024-02-07T15:30:01+00:00
[]
[]
TAGS #generic #vision #image-to-text #endpoints-template #base_model-Salesforce/blip-image-captioning-base #endpoints_compatible #region-us
# Fork of Salesforce/blip-image-captioning-base for a 'image-to-text' Inference endpoint. > Inspired by URL This repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. The code for the customized pipeline is in the URL. To use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file. ### expected Request payload Image to be labeled as binary. #### CURL #### Python
[ "# Fork of Salesforce/blip-image-captioning-base for a 'image-to-text' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.", "### expected Request payload\n\nImage to be labeled as binary.", "#### CURL", "#### Python" ]
[ "TAGS\n#generic #vision #image-to-text #endpoints-template #base_model-Salesforce/blip-image-captioning-base #endpoints_compatible #region-us \n", "# Fork of Salesforce/blip-image-captioning-base for a 'image-to-text' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.", "### expected Request payload\n\nImage to be labeled as binary.", "#### CURL", "#### Python" ]
[ 51, 114, 16, 4, 3 ]
[ "passage: TAGS\n#generic #vision #image-to-text #endpoints-template #base_model-Salesforce/blip-image-captioning-base #endpoints_compatible #region-us \n# Fork of Salesforce/blip-image-captioning-base for a 'image-to-text' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.### expected Request payload\n\nImage to be labeled as binary.#### CURL#### Python" ]
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null
null
transformers
(Note: From short testing, this Alt version generated much better code) Alternate version of DeepMagic-Coder-7b which can be found bellow. - https://huggingface.co/rombodawg/DeepMagic-Coder-7b ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/bO-vSlXYhA4pebcA2f1HK.jpeg) This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon. Config can be found bellow: ```yaml models: - model: deepseek-ai_deepseek-coder-6.7b-instruct parameters: weight: 1 - model: ise-uiuc_Magicoder-S-DS-6.7B parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-6.7b-base parameters: normalize: true int8_mask: true dtype: float16 ```
{"license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}
text-generation
LoneStriker/DeepMagic-Coder-7b-Alt-8.0bpw-h8-exl2
[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T15:31:58+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
(Note: From short testing, this Alt version generated much better code) Alternate version of DeepMagic-Coder-7b which can be found bellow. - URL !image/jpeg This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon. Config can be found bellow:
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 52 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
diffusers
# LoRA DreamBooth - danaleee/CL_rank10_iter800_valprompt These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks duck", "inference": true}
text-to-image
danaleee/CL_rank10_iter800_valprompt
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-07T15:35:01+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - danaleee/CL_rank10_iter800_valprompt These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.
[ "# LoRA DreamBooth - danaleee/CL_rank10_iter800_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/CL_rank10_iter800_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ 70, 107 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank10_iter800_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks duck using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
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null
null
diffusers
# LoRA DreamBooth - danaleee/CL_rank4_iter500_valprompt These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks teddybear", "inference": true}
text-to-image
danaleee/CL_rank4_iter500_valprompt
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-07T15:38:10+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - danaleee/CL_rank4_iter500_valprompt These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.
[ "# LoRA DreamBooth - danaleee/CL_rank4_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/CL_rank4_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ 70, 109 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank4_iter500_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
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null
null
ml-agents
# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** 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: tizayi/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
{"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]}
reinforcement-learning
tizayi/ppo-SnowballTarget
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
2024-02-07T15:38:12+00:00
[]
[]
TAGS #ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
# ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: tizayi/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play
[ "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\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: tizayi/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ "TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n", "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\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: tizayi/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ 50, 206 ]
[ "passage: TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\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: tizayi/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
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null
null
transformers
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
{"license": "cc-by-2.0"}
text-generation
LoneStriker/Senku-70B-Full-5.0bpw-h6-exl2
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T15:38:23+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 60 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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. --> # hubert_RTSPsplit_0208_1 This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2473 - Wer: 0.5480 - Cer: 0.3836 ## 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: 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: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 13.3667 | 1.0 | 60 | 10.9759 | 0.9650 | 0.9893 | | 6.7554 | 2.0 | 120 | 5.7617 | 0.9650 | 0.9893 | | 4.9638 | 3.0 | 180 | 4.5906 | 0.9650 | 0.9893 | | 3.8281 | 4.0 | 240 | 3.6487 | 0.9650 | 0.9893 | | 3.2137 | 5.0 | 300 | 3.0965 | 0.9650 | 0.9893 | | 2.6918 | 6.0 | 360 | 2.5698 | 0.9650 | 0.9893 | | 1.9548 | 7.0 | 420 | 1.7758 | 1.0 | 0.7781 | | 1.4576 | 8.0 | 480 | 1.2590 | 1.0 | 0.5555 | | 1.1469 | 9.0 | 540 | 1.0586 | 1.0 | 0.5343 | | 0.9577 | 10.0 | 600 | 0.8502 | 0.8095 | 0.4601 | | 0.9391 | 11.0 | 660 | 0.7605 | 0.8069 | 0.4735 | | 0.7744 | 12.0 | 720 | 0.7378 | 0.8025 | 0.5018 | | 0.7492 | 13.0 | 780 | 0.7191 | 0.7920 | 0.5526 | | 0.683 | 14.0 | 840 | 0.6538 | 0.7827 | 0.5061 | | 0.6832 | 15.0 | 900 | 0.6730 | 0.7857 | 0.4839 | | 0.6235 | 16.0 | 960 | 0.5698 | 0.7772 | 0.4543 | | 0.5675 | 17.0 | 1020 | 0.5220 | 0.6957 | 0.3223 | | 1.1877 | 18.0 | 1080 | 0.4777 | 0.7861 | 0.4389 | | 0.498 | 19.0 | 1140 | 0.4616 | 0.7150 | 0.4252 | | 0.4866 | 20.0 | 1200 | 0.4383 | 0.7180 | 0.3752 | | 0.4689 | 21.0 | 1260 | 0.4194 | 0.7232 | 0.3532 | | 0.5681 | 22.0 | 1320 | 0.4201 | 0.6734 | 0.3000 | | 0.4051 | 23.0 | 1380 | 0.3938 | 0.6845 | 0.3390 | | 0.3985 | 24.0 | 1440 | 0.3572 | 0.6607 | 0.3760 | | 0.375 | 25.0 | 1500 | 0.3432 | 0.6332 | 0.3928 | | 0.3577 | 26.0 | 1560 | 0.3152 | 0.5755 | 0.2824 | | 0.3265 | 27.0 | 1620 | 0.2835 | 0.5856 | 0.3739 | | 0.3272 | 28.0 | 1680 | 0.2650 | 0.5539 | 0.3515 | | 0.2951 | 29.0 | 1740 | 0.2527 | 0.5521 | 0.3830 | | 0.2845 | 30.0 | 1800 | 0.2473 | 0.5480 | 0.3836 | ### 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": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert_RTSPsplit_0208_1", "results": []}]}
automatic-speech-recognition
tndklab/hubert_RTSPsplit_0208_1
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:rinna/japanese-hubert-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-07T15:42:58+00:00
[]
[]
TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us
hubert\_RTSPsplit\_0208\_1 ========================== This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.2473 * Wer: 0.5480 * Cer: 0.3836 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: 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: 30 ### 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: 0.0002\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: 30", "### 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-rinna/japanese-hubert-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: 0.0002\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: 30", "### 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" ]
[ 69, 115, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-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: 0.0002\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: 30### 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
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{"library_name": "transformers", "tags": []}
null
m-newhauser/mistral_7b_guanaco_test
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T15:44:10+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. - 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
[ "# 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 #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 #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
# Lao to English Translation Model Welcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for Lao to English translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English. Built on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges. With a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders. ## How to use ### On GPU ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-translate-lao-english") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-translate-lao-english") model.cuda() src = "ຂ້ອຍ​ຮັກ​ເຈົ້າ" tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda() model.eval() translate_ids = model.generate(tokenized_text, max_length=140) output = tokenizer.decode(translate_ids[0], skip_special_tokens=True) output ``` 'I love you' ### On CPU ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-translate-lao-english") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-translate-lao-english") src = "ຂ້ອຍ​ຮັກ​ເຈົ້າ" input_ids = tokenizer(src, max_length=200, return_tensors="pt", padding="max_length", truncation=True).input_ids outputs = model.generate(input_ids=input_ids, max_new_tokens=140) output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] output ``` 'I love you' ## Author ` Phan Minh Toan `
{"language": ["en", "lo"], "license": "mit", "library_name": "transformers", "tags": ["translation"], "widget": [{"text": "\u0e82\u0ec9\u0ead\u0e8d\u0ea2\u0eb2\u0e81\u0e8a\u0eb7\u0ec9\u0e9b\u0eb6\u0ec9\u0ea1"}], "inference": {"parameters": {"max_length": 140}}, "pipeline_tag": "translation"}
translation
minhtoan/t5-translate-lao-english
[ "transformers", "pytorch", "mt5", "text2text-generation", "translation", "en", "lo", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T15:45:49+00:00
[]
[ "en", "lo" ]
TAGS #transformers #pytorch #mt5 #text2text-generation #translation #en #lo #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Lao to English Translation Model Welcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for Lao to English translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English. Built on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges. With a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders. ## How to use ### On GPU 'I love you' ### On CPU 'I love you' ## Author ' Phan Minh Toan '
[ "# Lao to English Translation Model\nWelcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for Lao to English translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.\n\nBuilt on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.\n\nWith a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.", "## How to use", "### On GPU\n\n'I love you'", "### On CPU\n\n'I love you'", "## Author\n'\nPhan Minh Toan \n'" ]
[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #en #lo #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Lao to English Translation Model\nWelcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for Lao to English translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.\n\nBuilt on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.\n\nWith a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.", "## How to use", "### On GPU\n\n'I love you'", "### On CPU\n\n'I love you'", "## Author\n'\nPhan Minh Toan \n'" ]
[ 61, 308, 4, 9, 9, 8 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #en #lo #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Lao to English Translation Model\nWelcome to the forefront of linguistic innovation with our groundbreaking T5 language model designed specifically for Lao to English translation. In a rapidly globalizing world where effective communication is paramount, our T5 model stands as a beacon of excellence, offering unparalleled accuracy, fluency, and efficiency in bridging the language gap between Lao and English.\n\nBuilt on state-of-the-art deep learning architecture and trained on vast datasets of Lao and English texts, our language model (LLM) harnesses the power of transformer-based technology to deliver seamless and precise translations. Whether you're a business expanding into Laotian markets, a researcher seeking to access Lao-language resources, or an individual connecting with Lao-speaking communities, our T5 model is your ultimate solution for unlocking linguistic barriers and fostering meaningful cross-cultural exchanges.\n\nWith a commitment to quality and innovation, our translation model not only translates words but also preserves context, tone, and cultural nuances, ensuring that the essence of the original message remains intact in every translated sentence. Whether it's documents, websites, or multimedia content, our LLM model offers unmatched versatility and reliability, empowering users to communicate effortlessly across languages and borders.## How to use### On GPU\n\n'I love you'### On CPU\n\n'I love you'## Author\n'\nPhan Minh Toan \n'" ]
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null
null
transformers
# MetricX-23 *This is not an officially supported Google product.* **GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)** This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/). The models were trained in [T5X](https://github.com/google-research/t5x) and then converted for use in PyTorch. ## Available Models There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * [MetricX-23-XXL](https://huggingface.co/google/metricx-23-large-v2p0) * [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0) * [MetricX-23-Large](https://huggingface.co/google/metricx-23-xxl-v2p0) * [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-large-v2p0) * [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0) * [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-xxl-v2p0) We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. ## Changes to the WMT'23 Submission These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/) then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. **That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score).** Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. | Failure mode | Synthetic example description | | ----------- | ----------- | | Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. | | Overtranslation | Candidate translation duplicated (with space in between). | | Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. | | Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). | | Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). | | Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. | | Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). | Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. ## Usage The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx). The repository contains example prediction scripts, described below. The `metricx23/predict.py` script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"reference"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-qe-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl \ --qe ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"source"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. ## Meta-Evaluation The `metricx23/evaluate.py` script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the [MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library. Example usage: ```bash python -m metricx23.evaluate \ --dataset wmt22 \ --lp en-de \ --input_file input.jsonl \ --output_file output.json ``` `input.jsonl` is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * `"system_id"`: The name of the system that generated the translation. * `"segment_id"`: The 0-based index of the corresponding segment in the MT Metrics Eval data. * `"label"`: The ground-truth translation quality score (with higher is better). * `"prediction"`: The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 | | MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 | | MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 | | MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 | | MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 | | MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 | English-Russian: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 | | MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 | | MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 | | MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 | | MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 | | MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 | Chinese-English: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 | | MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 | | MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 | | MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 | | MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 | | MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 | The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation score that was used to rank submissions from the [WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf). Example usage: ```bash python -m metricx23.evaluate_wmt23 \ --en_de predictions_ende.jsonl \ --he_en predictions_heen.jsonl \ --zh_en predictions_zhen.jsonl \ --output_file output.json ``` Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: | Model | Average Correlation | | ----------- | ----------- | | MetricX-23-XXL | 0.812 | | MetricX-23-XL | 0.813 | | MetricX-23-Large | 0.794 | | MetricX-23-QE-XXL | 0.797 | | MetricX-23-QE-XL | 0.767 | | MetricX-23-QE-Large | 0.762 | ## Citation If you use MetricX-23 in your research, please cite the following publication: ```bibtex @inproceedings{juraska-etal-2023-metricx, title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}}, author = "Juraska, Juraj and Finkelstein, Mara and Deutsch, Daniel and Siddhant, Aditya and Mirzazadeh, Mehdi and Freitag, Markus", editor = "Koehn, Philipp and Haddow, Barry and Kocmi, Tom and Monz, Christof", booktitle = "Proceedings of the Eighth Conference on Machine Translation", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wmt-1.63", doi = "10.18653/v1/2023.wmt-1.63", pages = "756--767", } ```
{"license": "apache-2.0"}
null
google/metricx-23-large-v2p0
[ "transformers", "pytorch", "mt5", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T15:47:41+00:00
[]
[]
TAGS #transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
MetricX-23 ========== *This is not an officially supported Google product.* GitHub repository: URL This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. Available Models ---------------- There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * MetricX-23-XXL * MetricX-23-XL * MetricX-23-Large * MetricX-23-QE-XXL * MetricX-23-QE-XL * MetricX-23-QE-Large We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. Changes to the WMT'23 Submission -------------------------------- These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with mT5 then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score). Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. Usage ----- The code for using MetricX models can be found at URL The repository contains example prediction scripts, described below. The 'metricx23/URL' script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: 'URL' is expected to have 1 serialized JSON object per line with '"reference"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: 'URL' is expected to have 1 serialized JSON object per line with '"source"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Meta-Evaluation --------------- The 'metricx23/URL' script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the MT Metrics Eval library. Example usage: 'URL' is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * '"system\_id"': The name of the system that generated the translation. * '"segment\_id"': The 0-based index of the corresponding segment in the MT Metrics Eval data. * '"label"': The ground-truth translation quality score (with higher is better). * '"prediction"': The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: English-Russian: Chinese-English: The 'metricx23/evaluate\_wmt23.py' script re-calculates the average correlation score that was used to rank submissions from the WMT'23 Shared Task. Example usage: Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: If you use MetricX-23 in your research, please cite the following publication:
[ "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ "TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ 42, 666, 111, 457 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "passage: ### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior." ]
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null
null
generic
# Fork of [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large) for a `image-to-text` Inference endpoint. > Inspired by https://huggingface.co/sergeipetrov/blip_captioning This repository implements a `custom` task for `image-to-text` for 🤗 Inference Endpoints to allow image capturing. The code for the customized pipeline is in the handler.py. To use deploy this model an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file. ### expected Request payload Image to be labeled as binary. #### CURL ``` curl URL \ -X POST \ --data-binary @car.png \ -H "Content-Type: image/png" ``` #### Python ```python requests.post(ENDPOINT_URL, headers={"Content-Type": "image/png"}, data=open("car.png", 'rb').read()).json() ```
{"library_name": "generic", "tags": ["vision", "image-to-text", "endpoints-template"], "inference": false, "pipeline_tag": "image-to-text", "base_model": "Salesforce/blip-image-captioning-large"}
image-to-text
pimcore/IEP__image-capturing-large
[ "generic", "vision", "image-to-text", "endpoints-template", "base_model:Salesforce/blip-image-captioning-large", "endpoints_compatible", "region:us" ]
2024-02-07T15:52:17+00:00
[]
[]
TAGS #generic #vision #image-to-text #endpoints-template #base_model-Salesforce/blip-image-captioning-large #endpoints_compatible #region-us
# Fork of Salesforce/blip-image-captioning-large for a 'image-to-text' Inference endpoint. > Inspired by URL This repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. The code for the customized pipeline is in the URL. To use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file. ### expected Request payload Image to be labeled as binary. #### CURL #### Python
[ "# Fork of Salesforce/blip-image-captioning-large for a 'image-to-text' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.", "### expected Request payload\n\nImage to be labeled as binary.", "#### CURL", "#### Python" ]
[ "TAGS\n#generic #vision #image-to-text #endpoints-template #base_model-Salesforce/blip-image-captioning-large #endpoints_compatible #region-us \n", "# Fork of Salesforce/blip-image-captioning-large for a 'image-to-text' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.", "### expected Request payload\n\nImage to be labeled as binary.", "#### CURL", "#### Python" ]
[ 52, 115, 16, 4, 3 ]
[ "passage: TAGS\n#generic #vision #image-to-text #endpoints-template #base_model-Salesforce/blip-image-captioning-large #endpoints_compatible #region-us \n# Fork of Salesforce/blip-image-captioning-large for a 'image-to-text' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-text' for Inference Endpoints to allow image capturing. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.### expected Request payload\n\nImage to be labeled as binary.#### CURL#### Python" ]
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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. --> # Wintersmith/LLM_generated_text_detector This model is a fine-tuned version of [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0082 - Train Accuracy: 0.9974 - Validation Loss: 0.0191 - Validation Accuracy: 0.9941 - Epoch: 1 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 3630, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, '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 | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0579 | 0.9809 | 0.0272 | 0.9920 | 0 | | 0.0082 | 0.9974 | 0.0191 | 0.9941 | 1 | ### Framework versions - Transformers 4.37.0 - TensorFlow 2.15.0 - Datasets 2.15.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert/distilbert-base-uncased-finetuned-sst-2-english", "model-index": [{"name": "Wintersmith/LLM_generated_text_detector", "results": []}]}
text-classification
Wintersmith/LLM_generated_text_detector
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased-finetuned-sst-2-english", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2024-02-07T15:55:26+00:00
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TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert/distilbert-base-uncased-finetuned-sst-2-english #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
Wintersmith/LLM\_generated\_text\_detector ========================================== This model is a fine-tuned version of distilbert/distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.0082 * Train Accuracy: 0.9974 * Validation Loss: 0.0191 * Validation Accuracy: 0.9941 * Epoch: 1 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': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 3630, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.37.0 * TensorFlow 2.15.0 * Datasets 2.15.0 * 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': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 3630, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, '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 4.37.0\n* TensorFlow 2.15.0\n* Datasets 2.15.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert/distilbert-base-uncased-finetuned-sst-2-english #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 3630, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, '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 4.37.0\n* TensorFlow 2.15.0\n* Datasets 2.15.0\n* Tokenizers 0.15.1" ]
[ 89, 305, 4, 31 ]
[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert/distilbert-base-uncased-finetuned-sst-2-english #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 3630, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, '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 4.37.0\n* TensorFlow 2.15.0\n* Datasets 2.15.0\n* Tokenizers 0.15.1" ]
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null
null
sentence-transformers
# OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1334 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 1e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 266, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
{"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}
sentence-similarity
OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2
[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "endpoints_compatible", "region:us" ]
2024-02-07T15:56:35+00:00
[]
[]
TAGS #sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us
# OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2 This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Training The model was trained with the parameters: DataLoader: 'URL.dataloader.DataLoader' of length 1334 with parameters: Loss: 'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters: Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
[ "# OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1334 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]
[ "TAGS\n#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n", "# OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1334 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]
[ 44, 71, 38, 29, 86, 5, 6 ]
[ "passage: TAGS\n#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# OmarAlsaabi/e5-base-mlqa-finetuned-arabic-for-rag-attempt-2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1334 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]
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null
null
transformers
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
{"license": "cc-by-2.0"}
text-generation
LoneStriker/Senku-70B-Full-6.0bpw-h6-exl2
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T15:57:05+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth. EQ-Bench: 84.89 Will run more benches later.
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 60 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
generic
# Fork of [caidas/swin2SR-classical-sr-x2-64](https://huggingface.co/caidas/swin2SR-classical-sr-x2-64) for a `image-to-image` Inference endpoint. > Inspired by https://huggingface.co/sergeipetrov/swin2SR-classical-sr-x2-64-IE This repository implements a `custom` task for `image-to-image` for 🤗 Inference Endpoints to allow image up scaling by doubling image resolution. The code for the customized pipeline is in the handler.py. To use deploy this model an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file. ### expected Request payload Image to be labeled as binary. #### CURL ``` curl URL \ -X POST \ --data-binary @car.png \ -H "Content-Type: image/png" ``` #### Python ```python requests.post(ENDPOINT_URL, headers={"Content-Type": "image/png"}, data=open("car.png", 'rb').read()).json() ```
{"library_name": "generic", "tags": ["vision", "image-to-image", "endpoints-template"], "inference": false, "pipeline_tag": "image-to-image", "base_model": "caidas/swin2SR-classical-sr-x2-64"}
image-to-image
pimcore/IEP__image-upscaling-2x
[ "generic", "vision", "image-to-image", "endpoints-template", "base_model:caidas/swin2SR-classical-sr-x2-64", "endpoints_compatible", "region:us" ]
2024-02-07T15:57:08+00:00
[]
[]
TAGS #generic #vision #image-to-image #endpoints-template #base_model-caidas/swin2SR-classical-sr-x2-64 #endpoints_compatible #region-us
# Fork of caidas/swin2SR-classical-sr-x2-64 for a 'image-to-image' Inference endpoint. > Inspired by URL This repository implements a 'custom' task for 'image-to-image' for Inference Endpoints to allow image up scaling by doubling image resolution. The code for the customized pipeline is in the URL. To use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file. ### expected Request payload Image to be labeled as binary. #### CURL #### Python
[ "# Fork of caidas/swin2SR-classical-sr-x2-64 for a 'image-to-image' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-image' for Inference Endpoints to allow image up scaling by doubling image resolution. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.", "### expected Request payload\n\nImage to be labeled as binary.", "#### CURL", "#### Python" ]
[ "TAGS\n#generic #vision #image-to-image #endpoints-template #base_model-caidas/swin2SR-classical-sr-x2-64 #endpoints_compatible #region-us \n", "# Fork of caidas/swin2SR-classical-sr-x2-64 for a 'image-to-image' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-image' for Inference Endpoints to allow image up scaling by doubling image resolution. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.", "### expected Request payload\n\nImage to be labeled as binary.", "#### CURL", "#### Python" ]
[ 54, 125, 16, 4, 3 ]
[ "passage: TAGS\n#generic #vision #image-to-image #endpoints-template #base_model-caidas/swin2SR-classical-sr-x2-64 #endpoints_compatible #region-us \n# Fork of caidas/swin2SR-classical-sr-x2-64 for a 'image-to-image' Inference endpoint.\n\n> Inspired by URL\n\nThis repository implements a 'custom' task for 'image-to-image' for Inference Endpoints to allow image up scaling by doubling image resolution. \nThe code for the customized pipeline is in the URL.\n\nTo use deploy this model an Inference Endpoint you have to select 'Custom' as task to use the 'URL' file.### expected Request payload\n\nImage to be labeled as binary.#### CURL#### Python" ]
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# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF ## Description Imatrix GGUF quants of [NeverSleep/MiquMaid-v2-70B](https://huggingface.co/NeverSleep/MiquMaid-v2-70B) [IQ2-XS](https://huggingface.co/Kooten/MiquMaid-v2-70B-Imatrix-GGUF/blob/main/MiquMaid-v2-70B-IQ2_XS.gguf), [IQ2-XXS](https://huggingface.co/Kooten/MiquMaid-v2-70B-Imatrix-GGUF/blob/main/MiquMaid-v2-70B-IQ2_XXS.gguf), [IQ3-XXS](https://huggingface.co/Kooten/MiquMaid-v2-70B-Imatrix-GGUF/blob/main/MiquMaid-v2-70B-IQ3_XXS.gguf) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
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Kooten/MiquMaid-v2-70B-Imatrix-GGUF
[ "gguf", "not-for-all-audiences", "nsfw", "license:cc-by-nc-4.0", "region:us" ]
2024-02-07T15:57:37+00:00
[]
[]
TAGS #gguf #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #region-us
# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF ## Description Imatrix GGUF quants of NeverSleep/MiquMaid-v2-70B IQ2-XS, IQ2-XXS, IQ3-XXS ### Custom format: ## Contact Kooten on discord URL
[ "# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF", "## Description\nImatrix GGUF quants of NeverSleep/MiquMaid-v2-70B\n\nIQ2-XS, IQ2-XXS, IQ3-XXS", "### Custom format:", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#gguf #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #region-us \n", "# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF", "## Description\nImatrix GGUF quants of NeverSleep/MiquMaid-v2-70B\n\nIQ2-XS, IQ2-XXS, IQ3-XXS", "### Custom format:", "## Contact\nKooten on discord\n\nURL" ]
[ 33, 23, 38, 5, 7 ]
[ "passage: TAGS\n#gguf #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #region-us \n# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF## Description\nImatrix GGUF quants of NeverSleep/MiquMaid-v2-70B\n\nIQ2-XS, IQ2-XXS, IQ3-XXS### Custom format:## Contact\nKooten on discord\n\nURL" ]
[ -0.06136941537261009, 0.11133108288049698, -0.006444139406085014, 0.09798911958932877, -0.02592054195702076, 0.07480200380086899, 0.2010866105556488, 0.08492279052734375, 0.12762531638145447, -0.014663360081613064, 0.0747612714767456, -0.036147940903902054, 0.06280605494976044, 0.02867213264107704, -0.01995835080742836, 0.0007472994038835168, 0.0286093782633543, 0.027168769389390945, 0.06942065060138702, 0.04823126271367073, 0.029519127681851387, 0.005569336004555225, 0.0001170270043076016, -0.012080526910722256, -0.06746985763311386, -0.11461964249610901, -0.0265667662024498, -0.0067425197921693325, 0.030714277178049088, 0.023854965344071388, -0.04005401208996773, 0.15124531090259552, -0.04231675714254379, -0.11919476836919785, 0.019010303542017937, -0.0110403373837471, -0.04148271307349205, 0.011710683815181255, -0.007002673577517271, 0.01732245273888111, 0.105200856924057, 0.09004223346710205, -0.09940118342638016, 0.0541895255446434, -0.17872413992881775, -0.07483775913715363, -0.028451943770051003, 0.1152937188744545, -0.020176542922854424, 0.10161774605512619, -0.0027587958611547947, 0.14638464152812958, -0.10285426676273346, 0.02660905010998249, 0.19199424982070923, -0.23142419755458832, 0.017223909497261047, 0.18554960191249847, -0.09956266731023788, 0.027896763756871223, -0.11917639523744583, 0.03871836140751839, -0.003879185765981674, -0.051135607063770294, -0.08212362974882126, -0.0327770933508873, 0.06693283468484879, -0.054907456040382385, -0.023647025227546692, 0.007721752859652042, 0.16595755517482758, 0.05135667696595192, -0.04907706007361412, 0.09946531057357788, 0.018001459538936615, -0.12642242014408112, -0.05902090668678284, -0.008579040877521038, 0.06235537678003311, 0.018307054415345192, 0.02529558353126049, -0.030866945162415504, -0.101947121322155, -0.055563785135746, -0.10761075466871262, 0.15846523642539978, -0.07526582479476929, 0.0364486463367939, -0.019239770248532295, -0.02754756063222885, -0.23469974100589752, -0.06765130907297134, 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-0.10313710570335388, -0.09561987966299057, 0.0179695226252079, 0.01908082701265812, -0.03152807801961899, 0.016219349578022957, 0.01894591934978962, -0.0374375581741333, -0.038761816918849945, -0.0075719887390732765, -0.13148018717765808, -0.06351203471422195, 0.08924635499715805, 0.08196493238210678, 0.09404625743627548, -0.10458914935588837, -0.014829362742602825, -0.09015262871980667, 0.02303837053477764, 0.025757521390914917, -0.011473345570266247, -0.1542350798845291, -0.024138571694493294, 0.01071260217577219, 0.004982727579772472, -0.04731391370296478, -0.062097083777189255, -0.00015251646982505918, 0.2266242504119873, -0.06456100195646286, -0.021415283903479576, 0.1383516788482666, -0.12946638464927673, -0.15179872512817383, 0.11566281318664551, 0.13984930515289307, -0.08284933120012283, 0.025759709998965263, 0.23813292384147644, -0.03887421265244484, -0.10061229765415192, -0.08646579831838608, 0.062391359359025955, -0.004066126886755228, -0.08105790615081787, 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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. --> # robust_llm_pythia-tt-410m-mz-v0 This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) on an unknown 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: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m-deduped", "model-index": [{"name": "robust_llm_pythia-tt-410m-mz-v0", "results": []}]}
text-classification
AlignmentResearch/robust_llm_pythia-tt-410m-mz-v0
[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T15:57:48+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-410m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# robust_llm_pythia-tt-410m-mz-v0 This model is a fine-tuned version of EleutherAI/pythia-410m-deduped on an unknown 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: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# robust_llm_pythia-tt-410m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-410m-deduped on an unknown 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: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-410m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# robust_llm_pythia-tt-410m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-410m-deduped on an unknown 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: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 88, 51, 6, 12, 8, 3, 90, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-410m-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# robust_llm_pythia-tt-410m-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-410m-deduped on an unknown 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: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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0.12498392164707184, -0.015355641953647137, -0.016114484518766403, -0.02349325269460678, -0.02184039168059826, -0.0304032601416111, 0.09067756682634354, 0.09671121835708618, -0.01480698212981224, -0.051716551184654236, 0.017986714839935303, -0.021421659737825394, -0.03183663636445999, -0.10746892541646957, 0.07489079236984253, -0.009950831532478333, 0.01290216576308012, -0.022614778950810432, 0.05977059155702591, 0.029490474611520767, -0.1343683898448944, 0.025229157879948616, -0.14385594427585602, -0.16676746308803558, 0.001815533614717424, 0.07475912570953369, -0.006019333843141794, 0.043641235679388046, 0.026727618649601936, 0.004402189515531063, 0.0936569944024086, -0.013680989854037762, -0.08789470791816711, -0.07886925339698792, 0.07925428450107574, -0.11596812307834625, 0.2092898041009903, -0.004428068175911903, 0.07906520366668701, 0.10981132835149765, -0.005871836561709642, -0.16449831426143646, 0.0372631810605526, 0.058991044759750366, -0.021994473412632942, 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# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF ## Description Imatrix GGUF quants of [NeverSleep/MiquMaid-v2-70B-DPO](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) [IQ2-XS](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF/blob/main/MiquMaid-v2-70B-DPO-IQ2_XS.gguf), [IQ2-XXS](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF/blob/main/MiquMaid-v2-70B-DPO-IQ2_XXS.gguf), [IQ3-XXS](https://huggingface.co/Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF/blob/main/MiquMaid-v2-70B-DPO-IQ3_XXS.gguf) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten)
{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}
null
Kooten/MiquMaid-v2-70B-DPO-Imatrix-GGUF
[ "gguf", "not-for-all-audiences", "nsfw", "license:cc-by-nc-4.0", "region:us" ]
2024-02-07T15:57:53+00:00
[]
[]
TAGS #gguf #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #region-us
# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF ## Description Imatrix GGUF quants of NeverSleep/MiquMaid-v2-70B-DPO IQ2-XS, IQ2-XXS, IQ3-XXS ### Custom format: ## Contact Kooten on discord URL
[ "# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF", "## Description\nImatrix GGUF quants of NeverSleep/MiquMaid-v2-70B-DPO\n\nIQ2-XS, IQ2-XXS, IQ3-XXS", "### Custom format:", "## Contact\nKooten on discord\n\nURL" ]
[ "TAGS\n#gguf #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #region-us \n", "# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF", "## Description\nImatrix GGUF quants of NeverSleep/MiquMaid-v2-70B-DPO\n\nIQ2-XS, IQ2-XXS, IQ3-XXS", "### Custom format:", "## Contact\nKooten on discord\n\nURL" ]
[ 33, 23, 41, 5, 7 ]
[ "passage: TAGS\n#gguf #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #region-us \n# NeverSleep/MiquMaid-v2-70B-DPO Imatrix GGUF## Description\nImatrix GGUF quants of NeverSleep/MiquMaid-v2-70B-DPO\n\nIQ2-XS, IQ2-XXS, IQ3-XXS### Custom format:## Contact\nKooten on discord\n\nURL" ]
[ -0.06958597898483276, 0.11813756078481674, -0.006708186585456133, 0.10240675508975983, -0.016641097143292427, 0.07521188259124756, 0.19953849911689758, 0.0846794918179512, 0.13095231354236603, -0.007534670177847147, 0.06944800168275833, -0.016475141048431396, 0.06974062323570251, 0.026760267093777657, -0.015653377398848534, -0.0031161634251475334, 0.02530612237751484, 0.032479893416166306, 0.07100807875394821, 0.044242143630981445, 0.031263694167137146, 0.004130910616368055, -0.0023783231154084206, -0.01543028187006712, -0.06765978038311005, -0.11257842183113098, -0.026383332908153534, -0.007048073690384626, 0.03801547735929489, 0.017782563343644142, -0.046400874853134155, 0.1494089812040329, -0.038184549659490585, -0.11209950596094131, 0.016631310805678368, -0.007444179151207209, -0.03690542280673981, 0.011049306020140648, 0.0016098138876259327, 0.022358065471053123, 0.09498269855976105, 0.09597228467464447, -0.09199508279561996, 0.05568252131342888, -0.17545755207538605, -0.08297435194253922, -0.02978583611547947, 0.1209636852145195, -0.020938878878951073, 0.10737888514995575, -0.00015959639858920127, 0.1544869840145111, -0.08866605162620544, 0.021610483527183533, 0.19204501807689667, -0.2301158308982849, 0.022287718951702118, 0.1873643547296524, -0.10230527073144913, 0.021611418575048447, -0.11224717646837234, 0.02456893026828766, -0.008020065724849701, -0.048192474991083145, -0.09368938207626343, -0.03405431658029556, 0.07487794011831284, -0.0599408820271492, -0.019157087430357933, 0.010263659060001373, 0.1689848005771637, 0.05625251308083534, -0.05893359333276749, 0.10719992965459824, 0.023116668686270714, -0.13025428354740143, -0.05813465267419815, -0.01946137100458145, 0.07200653851032257, 0.01980843022465706, 0.027863331139087677, -0.016729045659303665, -0.09910350292921066, -0.05773720145225525, -0.10628513991832733, 0.15919239819049835, -0.07604163885116577, 0.030811937525868416, -0.022340280935168266, -0.02321595512330532, -0.226840540766716, -0.06592099368572235, -0.12327763438224792, -0.06469738483428955, 0.02736750803887844, -0.044966921210289, 0.01860283501446247, 0.032505836337804794, 0.13935446739196777, 0.12316292524337769, -0.011166155338287354, 0.027666239067912102, -0.009008875116705894, 0.07709483057260513, -0.01562906615436077, -0.012039696797728539, 0.04578515514731407, 0.035912204533815384, 0.06773509830236435, -0.02690245769917965, 0.03317601606249809, -0.016591602936387062, -0.036714158952236176, -0.027284229174256325, -0.06261839717626572, 0.05227307230234146, 0.04613381251692772, 0.030103599652647972, -0.10189370810985565, -0.019384659826755524, 0.0816153734922409, 0.05380946770310402, -0.029268009588122368, 0.057904887944459915, 0.021455863490700722, 0.03456961736083031, -0.009329176507890224, 0.054953720420598984, 0.03835340589284897, 0.024834884330630302, -0.09207714349031448, 0.0517650730907917, 0.042351171374320984, 0.09903256595134735, 0.09212676435709, 0.0653764009475708, 0.0031798211857676506, -0.1105627790093422, -0.10143677145242691, 0.01343531534075737, 0.009829297661781311, -0.03366287425160408, 0.00895904004573822, 0.021313142031431198, -0.033635709434747696, -0.04944543540477753, -0.010353176854550838, -0.13480907678604126, -0.05886983498930931, 0.0974670797586441, 0.08239800482988358, 0.08606381714344025, -0.10670758038759232, -0.009479096159338951, -0.09286972880363464, 0.022064656019210815, 0.01789749786257744, -0.016618359833955765, -0.15204745531082153, -0.023432167246937752, 0.012950198724865913, 0.005285379011183977, -0.042524877935647964, -0.0620105005800724, 0.006487763021141291, 0.23425829410552979, -0.06638403236865997, -0.021153878420591354, 0.12734687328338623, -0.12580175697803497, -0.15379269421100616, 0.10712278634309769, 0.13881944119930267, -0.07119099050760269, 0.034400176256895065, 0.24148616194725037, -0.049304891377687454, -0.11663176864385605, -0.09238090366125107, 0.058658938854932785, -0.007259405683726072, -0.08913443982601166, 0.1308172345161438, -0.03619234263896942, -0.018405912443995476, 0.057488538324832916, 0.05182415619492531, 0.00041634758235886693, 0.001387989497743547, -0.11063368618488312, -0.02299039252102375, -0.05155320093035698, 0.08274596184492111, 0.044482652097940445, 0.0025835952255874872, -0.05505656823515892, 0.007313697133213282, -0.19700241088867188, 0.053226880729198456, 0.0925600603222847, -0.018899627029895782, -0.08799299597740173, 0.05471368879079819, -0.053775932639837265, -0.014339548535645008, 0.013352809473872185, -0.05176255479454994, -0.005005914252251387, -0.02503916621208191, 0.14743025600910187, 0.04731094464659691, 0.06113729625940323, -0.005408823490142822, -0.08869422972202301, 0.03942732512950897, 0.0056998711079359055, 0.0320800319314003, 0.006680487189441919, -0.1048283651471138, 0.1529587358236313, 0.013365251943469048, 0.13945114612579346, -0.20888710021972656, -0.04535660147666931, 0.07391033321619034, 0.031937405467033386, 0.00840369239449501, -0.12727589905261993, 0.11252642422914505, 0.013309312053024769, 0.037548236548900604, -0.0008245877688750625, 0.08539844304323196, 0.021075598895549774, -0.11641304939985275, 0.05073419213294983, -0.08883889764547348, 0.13205935060977936, 0.09366051852703094, 0.07581279426813126, -0.03718048334121704, 0.022589128464460373, -0.02992749772965908, 0.007448245771229267, 0.08305707573890686, 0.05834341049194336, 0.06724437326192856, -0.08881893008947372, 0.015268553979694843, -0.034770749509334564, -0.016131283715367317, 0.05016956478357315, -0.07912853360176086, -0.06938455253839493, 0.0645163282752037, 0.09699681401252747, -0.10580737888813019, 0.12482395023107529, 0.13418959081172943, -0.03919493779540062, 0.0890125259757042, -0.018510065972805023, 0.020179508253932, -0.13135211169719696, 0.02356553077697754, 0.043963462114334106, 0.204131081700325, -0.23918522894382477, 0.10704392194747925, 0.04430485516786575, 0.028414491564035416, 0.020320704206824303, -0.11256587505340576, 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null
null
null
OpenHermes-2.5-Mistral-7B by teknium converted to f16 gguf for easier tinkering; original model at https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B
{"license": "apache-2.0"}
null
interrobang/OpenHermes-2.5-Mistral-7B-GGUF-f16
[ "gguf", "license:apache-2.0", "region:us" ]
2024-02-07T16:03:14+00:00
[]
[]
TAGS #gguf #license-apache-2.0 #region-us
OpenHermes-2.5-Mistral-7B by teknium converted to f16 gguf for easier tinkering; original model at URL
[]
[ "TAGS\n#gguf #license-apache-2.0 #region-us \n" ]
[ 17 ]
[ "passage: TAGS\n#gguf #license-apache-2.0 #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. --> # robbert-2023-dutch-base-gender This model is a fine-tuned version of [DTAI-KULeuven/robbert-2023-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6591 - Precision: 0.6282 - Recall: 0.6290 - Fscore: 0.6278 - Accuracy: 0.6285 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.616 | 0.29 | 2000 | 0.6498 | 0.6295 | 0.6299 | 0.6266 | 0.6267 | | 0.6033 | 0.59 | 4000 | 0.6584 | 0.6278 | 0.6274 | 0.6228 | 0.6228 | | 0.5896 | 0.88 | 6000 | 0.6600 | 0.6285 | 0.6293 | 0.6282 | 0.6290 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy"], "base_model": "DTAI-KULeuven/robbert-2023-dutch-base", "model-index": [{"name": "robbert-2023-dutch-base-gender", "results": []}]}
text-classification
clips/robbert-2023-dutch-base-gender
[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:DTAI-KULeuven/robbert-2023-dutch-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T16:04:08+00:00
[]
[]
TAGS #transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-DTAI-KULeuven/robbert-2023-dutch-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
robbert-2023-dutch-base-gender ============================== This model is a fine-tuned version of DTAI-KULeuven/robbert-2023-dutch-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.6591 * Precision: 0.6282 * Recall: 0.6290 * Fscore: 0.6278 * Accuracy: 0.6285 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: 64 * eval\_batch\_size: 64 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.1.2+cu121 * Datasets 2.14.5 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.5\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-DTAI-KULeuven/robbert-2023-dutch-base #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: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.5\n* Tokenizers 0.15.0" ]
[ 71, 141, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-DTAI-KULeuven/robbert-2023-dutch-base #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: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.5\n* Tokenizers 0.15.0" ]
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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. --> # TesterGG/sequence_classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the 'act' classification labels in 'daily_dialog' dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3870 - Validation Loss: 0.5128 - Train Accuracy: 0.8059 - Epoch: 2 ## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9080, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.5076 | 0.5274 | 0.7987 | 0 | | 0.4112 | 0.5128 | 0.8059 | 1 | | 0.3870 | 0.5128 | 0.8059 | 2 | ### Framework versions - Transformers 4.38.0.dev0 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["daily_dialog"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "TesterGG/sequence_classification_model", "results": []}]}
text-classification
TesterGG/sequence_classification_model
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "dataset:daily_dialog", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T16:04:25+00:00
[]
[]
TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #dataset-daily_dialog #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
TesterGG/sequence\_classification\_model ======================================== This model is a fine-tuned version of distilbert-base-uncased on the 'act' classification labels in 'daily\_dialog' dataset. It achieves the following results on the evaluation set: * Train Loss: 0.3870 * Validation Loss: 0.5128 * Train Accuracy: 0.8059 * Epoch: 2 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': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 9080, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * 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': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #dataset-daily_dialog #base_model-distilbert-base-uncased #license-apache-2.0 #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': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 78, 304, 4, 36 ]
[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #dataset-daily_dialog #base_model-distilbert-base-uncased #license-apache-2.0 #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': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\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. --> # hubert_RTSPsplit_0208_2 This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1511 - Wer: 0.1981 - Cer: 0.0674 ## 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: 0.00025 - 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: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 12.328 | 1.0 | 60 | 10.3374 | 1.0 | 1.0 | | 6.4126 | 2.0 | 120 | 5.9206 | 1.0 | 1.0 | | 4.7432 | 3.0 | 180 | 4.5414 | 1.0 | 1.0 | | 3.6044 | 4.0 | 240 | 3.4580 | 1.0 | 1.0 | | 3.0143 | 5.0 | 300 | 2.9594 | 1.0 | 1.0 | | 2.2541 | 6.0 | 360 | 2.1267 | 1.0 | 0.9106 | | 1.4951 | 7.0 | 420 | 1.3833 | 1.0 | 0.5168 | | 1.1808 | 8.0 | 480 | 1.0881 | 1.0 | 0.5387 | | 0.9955 | 9.0 | 540 | 0.8620 | 0.6715 | 0.4108 | | 0.8934 | 10.0 | 600 | 0.8027 | 0.6741 | 0.4132 | | 0.8321 | 11.0 | 660 | 0.7528 | 0.7162 | 0.4529 | | 0.866 | 12.0 | 720 | 0.7082 | 0.6592 | 0.4064 | | 0.7158 | 13.0 | 780 | 0.6850 | 0.6469 | 0.4024 | | 0.6976 | 14.0 | 840 | 0.6642 | 0.6734 | 0.4207 | | 0.6829 | 15.0 | 900 | 0.6459 | 0.6689 | 0.4104 | | 0.6748 | 16.0 | 960 | 0.6891 | 0.7162 | 0.4496 | | 0.6816 | 17.0 | 1020 | 0.7691 | 0.6678 | 0.4168 | | 1.2144 | 18.0 | 1080 | 0.5982 | 0.6149 | 0.3510 | | 0.6285 | 19.0 | 1140 | 0.5844 | 0.6879 | 0.4058 | | 0.5805 | 20.0 | 1200 | 0.5947 | 0.5981 | 0.3338 | | 0.5707 | 21.0 | 1260 | 0.5202 | 0.5568 | 0.2629 | | 0.6479 | 22.0 | 1320 | 0.6423 | 0.6495 | 0.2831 | | 0.5267 | 23.0 | 1380 | 0.4946 | 0.5609 | 0.2582 | | 0.5049 | 24.0 | 1440 | 0.4816 | 0.5255 | 0.2463 | | 0.4922 | 25.0 | 1500 | 0.4518 | 0.5285 | 0.2442 | | 0.4669 | 26.0 | 1560 | 0.4594 | 0.5151 | 0.2329 | | 0.4503 | 27.0 | 1620 | 0.4018 | 0.5058 | 0.2227 | | 0.447 | 28.0 | 1680 | 0.3785 | 0.4682 | 0.1980 | | 0.3744 | 29.0 | 1740 | 0.3351 | 0.3847 | 0.1432 | | 0.3516 | 30.0 | 1800 | 0.2866 | 0.3456 | 0.1238 | | 0.335 | 31.0 | 1860 | 0.2582 | 0.3128 | 0.1152 | | 0.3282 | 32.0 | 1920 | 0.2578 | 0.2987 | 0.1063 | | 0.3113 | 33.0 | 1980 | 0.2272 | 0.2436 | 0.0850 | | 0.2812 | 34.0 | 2040 | 0.2112 | 0.2410 | 0.0846 | | 0.3105 | 35.0 | 2100 | 0.1911 | 0.2253 | 0.0774 | | 0.2225 | 36.0 | 2160 | 0.1751 | 0.2089 | 0.0719 | | 0.2351 | 37.0 | 2220 | 0.1838 | 0.2291 | 0.0781 | | 0.2028 | 38.0 | 2280 | 0.1583 | 0.2037 | 0.0686 | | 0.217 | 39.0 | 2340 | 0.1509 | 0.1918 | 0.0651 | | 0.2698 | 40.0 | 2400 | 0.1511 | 0.1981 | 0.0674 | ### 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": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert_RTSPsplit_0208_2", "results": []}]}
automatic-speech-recognition
tndklab/hubert_RTSPsplit_0208_2
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:rinna/japanese-hubert-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-07T16:04:27+00:00
[]
[]
TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us
hubert\_RTSPsplit\_0208\_2 ========================== This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.1511 * Wer: 0.1981 * Cer: 0.0674 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: 0.00025 * 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: 40 ### 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: 0.00025\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: 40", "### 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-rinna/japanese-hubert-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: 0.00025\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: 40", "### 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" ]
[ 69, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-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: 0.00025\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: 40### 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
Repository for my nlp4web model created for homework 6.
{"license": "apache-2.0"}
question-answering
Bugtus/bugtus_nlp4web
[ "transformers", "safetensors", "bert", "question-answering", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-07T16:04:52+00:00
[]
[]
TAGS #transformers #safetensors #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us
Repository for my nlp4web model created for homework 6.
[]
[ "TAGS\n#transformers #safetensors #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us \n" ]
[ 38 ]
[ "passage: TAGS\n#transformers #safetensors #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us \n" ]
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# BagelMIsteryTour-v2-8x7B 3.5bpw Imatrix GGUF quant of [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B) ## Other quants: EXL2: [5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-5bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-3.5bpw-exl2) [GGUF](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF): [IQ3_XXS](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF/blob/main/BagelMIsteryTour-v2-8x7B-IQ3_XXS.gguf), [IQ2_XS](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF/blob/main/BagelMIsteryTour-v2-8x7B-IQ2_XS.gguf), [IQ2_XXS](https://huggingface.co/Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF/blob/main/BagelMIsteryTour-v2-8x7B-IQ2_XXS.gguf) ## 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"]}
null
Kooten/BagelMIsteryTour-v2-8x7B-Imatrix-GGUF
[ "gguf", "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", "region:us" ]
2024-02-07T16:06:33+00:00
[]
[]
TAGS #gguf #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 #region-us
# BagelMIsteryTour-v2-8x7B 3.5bpw Imatrix GGUF quant of ycros/BagelMIsteryTour-v2-8x7B ## Other quants: EXL2: 5bpw, 3.5bpw GGUF: IQ3_XXS, IQ2_XS, IQ2_XXS ## 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 3.5bpw\nImatrix GGUF quant of ycros/BagelMIsteryTour-v2-8x7B", "## Other quants:\n\nEXL2: 5bpw, 3.5bpw\n\nGGUF: IQ3_XXS, IQ2_XS, IQ2_XXS", "## 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#gguf #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 #region-us \n", "# BagelMIsteryTour-v2-8x7B 3.5bpw\nImatrix GGUF quant of ycros/BagelMIsteryTour-v2-8x7B", "## Other quants:\n\nEXL2: 5bpw, 3.5bpw\n\nGGUF: IQ3_XXS, IQ2_XS, IQ2_XXS", "## Prompt format: Alpaca\nIt is noted to also work with mistral", "## Contact\nKooten on discord\n\nURL if you would like to support me" ]
[ 137, 42, 39, 18, 14 ]
[ "passage: TAGS\n#gguf #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 #region-us \n# BagelMIsteryTour-v2-8x7B 3.5bpw\nImatrix GGUF quant of ycros/BagelMIsteryTour-v2-8x7B## Other quants:\n\nEXL2: 5bpw, 3.5bpw\n\nGGUF: IQ3_XXS, IQ2_XS, IQ2_XXS## 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
# Steelskull/Etheria-55b-v0.1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/RAhrbktyyVQxOR1np-9L2.png) ## Merge Details An attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models. due to the merge it 'theoretically' should have a context of 200k but I recommend starting at 32k and moveing up, as it is unknown (at this time) what the merge has done to the context length. This is a merge of both VerA and VerB of Etheria-55b (There numbers were surprisingly good), I then created a sacrificial 55B out of the most performant yi-34b-200k Model and performed a Dare_ties merge and equalize the model into its current state. ### recommended settings and Prompt Format: Ive tested it up to 32k context using exl2 using these settings: ``` "temp": 0.7, "temperature_last": true, "top_p": 1, "top_k": 0, "top_a": 0, "tfs": 1, "epsilon_cutoff": 0, "eta_cutoff": 0, "typical_p": 1, "min_p": 0.1, "rep_pen": 1.1, "rep_pen_range": 8192, "no_repeat_ngram_size": 0, "penalty_alpha": 0, "num_beams": 1, "length_penalty": 1, "min_length": 0, "encoder_rep_pen": 1, "freq_pen": 0, "presence_pen": 0, "do_sample": true, "early_stopping": false, "add_bos_token": false, "truncation_length": 2048, "ban_eos_token": true, "skip_special_tokens": true, "streaming": true, "mirostat_mode": 0, "mirostat_tau": 5, "mirostat_eta": 0.1, ``` Prompt format that work well ``` ChatML & Alpaca ``` ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using Merged-Etheria-55b as a base. ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: Merged-Etheria-55b models: - model: Sacr-Etheria-55b parameters: weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113] density: 0.61 - model: Merged-Etheria-55b parameters: weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113] density: 0.61 merge_method: dare_ties tokenizer_source: union parameters: int8_mask: true dtype: bfloat16 ```
{"license": "apache-2.0", "tags": ["mergekit", "Etheria"], "base_model": []}
text-generation
waldie/Etheria-55b-v0.1-2.5bpw-h6-exl2
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "Etheria", "arxiv:2311.03099", "arxiv:2306.01708", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:09:57+00:00
[ "2311.03099", "2306.01708" ]
[]
TAGS #transformers #safetensors #llama #text-generation #mergekit #Etheria #arxiv-2311.03099 #arxiv-2306.01708 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Steelskull/Etheria-55b-v0.1 !image/png ## Merge Details An attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models. due to the merge it 'theoretically' should have a context of 200k but I recommend starting at 32k and moveing up, as it is unknown (at this time) what the merge has done to the context length. This is a merge of both VerA and VerB of Etheria-55b (There numbers were surprisingly good), I then created a sacrificial 55B out of the most performant yi-34b-200k Model and performed a Dare_ties merge and equalize the model into its current state. ### recommended settings and Prompt Format: Ive tested it up to 32k context using exl2 using these settings: Prompt format that work well ### Merge Method This model was merged using the DARE TIES merge method using Merged-Etheria-55b as a base. ### Configuration The following YAML configuration was used to produce this model:
[ "# Steelskull/Etheria-55b-v0.1\n\n!image/png", "## Merge Details\n\nAn attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models.\n\ndue to the merge it 'theoretically' should have a context of 200k but I recommend starting at 32k and moveing up,\nas it is unknown (at this time) what the merge has done to the context length.\n\nThis is a merge of both VerA and VerB of Etheria-55b (There numbers were surprisingly good), I then created a sacrificial 55B out of the most performant yi-34b-200k Model\nand performed a Dare_ties merge and equalize the model into its current state.", "### recommended settings and Prompt Format:\n\nIve tested it up to 32k context using exl2 using these settings:\n\n\n\nPrompt format that work well", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using Merged-Etheria-55b as a base.", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #Etheria #arxiv-2311.03099 #arxiv-2306.01708 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Steelskull/Etheria-55b-v0.1\n\n!image/png", "## Merge Details\n\nAn attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models.\n\ndue to the merge it 'theoretically' should have a context of 200k but I recommend starting at 32k and moveing up,\nas it is unknown (at this time) what the merge has done to the context length.\n\nThis is a merge of both VerA and VerB of Etheria-55b (There numbers were surprisingly good), I then created a sacrificial 55B out of the most performant yi-34b-200k Model\nand performed a Dare_ties merge and equalize the model into its current state.", "### recommended settings and Prompt Format:\n\nIve tested it up to 32k context using exl2 using these settings:\n\n\n\nPrompt format that work well", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using Merged-Etheria-55b as a base.", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 80, 17, 150, 37, 31, 17 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #Etheria #arxiv-2311.03099 #arxiv-2306.01708 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Steelskull/Etheria-55b-v0.1\n\n!image/png## Merge Details\n\nAn attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models.\n\ndue to the merge it 'theoretically' should have a context of 200k but I recommend starting at 32k and moveing up,\nas it is unknown (at this time) what the merge has done to the context length.\n\nThis is a merge of both VerA and VerB of Etheria-55b (There numbers were surprisingly good), I then created a sacrificial 55B out of the most performant yi-34b-200k Model\nand performed a Dare_ties merge and equalize the model into its current state.### recommended settings and Prompt Format:\n\nIve tested it up to 32k context using exl2 using these settings:\n\n\n\nPrompt format that work well### Merge Method\n\nThis model was merged using the DARE TIES merge method using Merged-Etheria-55b as a base.### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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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. --> # roberta-base-detect-cheapfake-combined-train-test-contradict This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4261 - Accuracy: 0.89 - F1: 0.8817 ## 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-06 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 166 | 0.4435 | 0.84 | 0.8333 | | No log | 2.0 | 332 | 0.6567 | 0.835 | 0.8374 | | No log | 3.0 | 498 | 0.3563 | 0.895 | 0.88 | | 0.2851 | 4.0 | 664 | 0.3671 | 0.895 | 0.8814 | | 0.2851 | 5.0 | 830 | 0.4261 | 0.89 | 0.8817 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-detect-cheapfake-combined-train-test-contradict", "results": []}]}
text-classification
hoanghoavienvo/roberta-base-detect-cheapfake-combined-train-test-contradict
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T16:10:05+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base-detect-cheapfake-combined-train-test-contradict ============================================================ This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4261 * Accuracy: 0.89 * F1: 0.8817 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-06 * 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: 5 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #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: 5e-06\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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ 63, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #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: 5e-06\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: 5### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
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null
null
transformers
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6459a451abdbb77c4c6d8258/uNoKlBulkRF3mCoMgetGs.png) --- Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below: - [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2-GGUF) - [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) ```yaml slices: - sources: - model: Orca2flat layer_range: [0, 16] - sources: - model: LLaMA2-13B-Psyfighter2 (FP16 not yet available) layer_range: [8, 24] - sources: - model: Orca2flat layer_range: [17, 32] - sources: - model: LLaMA2-13B-Psyfighter2 (FP16 not yet available) layer_range: [25, 40] merge_method: passthrough dtype: float16 ``` Note: while we did run an inverted merge the output was not satisfactory and will not be released. We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion. This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail. Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly. During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices. Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well. Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [[email protected]]. Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model, and to gelukuMLG for the model mascot!
{"license": "other", "tags": ["storywriting", "text adventure", "not-for-all-audiences"], "license_name": "microsoft-research-license"}
text-generation
zaq-hack/psyonic-cetacean-20B-bpw300-h6-exl2-rpcal
[ "transformers", "safetensors", "llama", "text-generation", "storywriting", "text adventure", "not-for-all-audiences", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:12:23+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #storywriting #text adventure #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
!image/png --- Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below: - KoboldAI/PsyFighter-2-13b - microsoft/Orca-2-13b Note: while we did run an inverted merge the output was not satisfactory and will not be released. We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion. This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail. Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly. During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices. Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well. Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@URL]. Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model, and to gelukuMLG for the model mascot!
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #storywriting #text adventure #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 67 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #storywriting #text adventure #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
# 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
text-generation
manche/gpt2-safeguard-zs
[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:13:18+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #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
[ "# 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 #gpt2 #text-generation #arxiv-1910.09700 #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 #gpt2 #text-generation #arxiv-1910.09700 #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
<!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2205 - Accuracy: 0.925 - F1: 0.9252 ## 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8142 | 1.0 | 250 | 0.3149 | 0.9095 | 0.9079 | | 0.2443 | 2.0 | 500 | 0.2205 | 0.925 | 0.9252 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.925, "name": "Accuracy"}, {"type": "f1", "value": 0.925235539384546, "name": "F1"}]}]}]}
text-classification
Schnatz65/distilbert-base-uncased-finetuned-emotion
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T16:18:49+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2205 * Accuracy: 0.925 * F1: 0.9252 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: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.13.0 * Pytorch 1.12.1 * Datasets 2.8.0 * Tokenizers 0.10.3
[ "### 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: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.12.1\n* Datasets 2.8.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #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: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.12.1\n* Datasets 2.8.0\n* Tokenizers 0.10.3" ]
[ 67, 98, 4, 31 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #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: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.12.1\n* Datasets 2.8.0\n* Tokenizers 0.10.3" ]
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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. --> # wav2vec2-300m-england-0208-ladderside_gate_adapter_attempt-avatar 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: 0.2758 - Wer: 0.2621 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.5967 | 1.0 | 1227 | 0.3236 | 0.3103 | | 0.3438 | 2.0 | 2454 | 0.2916 | 0.2903 | | 0.3137 | 3.0 | 3681 | 0.2763 | 0.2838 | | 0.2921 | 4.0 | 4908 | 0.2664 | 0.2740 | | 0.275 | 5.0 | 6135 | 0.2611 | 0.2677 | | 0.2601 | 6.0 | 7362 | 0.2560 | 0.2630 | | 0.2466 | 7.0 | 8589 | 0.2541 | 0.2640 | | 0.2334 | 8.0 | 9816 | 0.2565 | 0.2635 | | 0.2212 | 9.0 | 11043 | 0.2568 | 0.2655 | | 0.21 | 10.0 | 12270 | 0.2582 | 0.2617 | | 0.1991 | 11.0 | 13497 | 0.2596 | 0.2611 | | 0.1894 | 12.0 | 14724 | 0.2648 | 0.2598 | | 0.1805 | 13.0 | 15951 | 0.2726 | 0.2612 | | 0.1732 | 14.0 | 17178 | 0.2728 | 0.2631 | | 0.1669 | 15.0 | 18405 | 0.2758 | 0.2621 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 1.12.1+cu113 - 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-0208-ladderside_gate_adapter_attempt-avatar", "results": []}]}
automatic-speech-recognition
Lin25/wav2vec2-300m-england-0208-ladderside_gate_adapter_attempt-avatar
[ "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" ]
2024-02-07T16:19:51+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-0208-ladderside\_gate\_adapter\_attempt-avatar ==================================================================== 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: 0.2758 * Wer: 0.2621 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 1.12.1+cu113 * 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 1.12.1+cu113\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 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0" ]
[ 80, 159, 4, 40 ]
[ "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 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0" ]
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null
null
diffusers
# LoRA DreamBooth - danaleee/CL_rank4_iter800_valprompt These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks teddybear", "inference": true}
text-to-image
danaleee/CL_rank4_iter800_valprompt
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-07T16:20:41+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - danaleee/CL_rank4_iter800_valprompt These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.
[ "# LoRA DreamBooth - danaleee/CL_rank4_iter800_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/CL_rank4_iter800_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
[ 70, 109 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank4_iter800_valprompt\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]
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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
paulux84/autotrain-z58fs-z9tot
[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:21:47+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #mistral #text-generation #autotrain #conversational #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 #tensorboard #safetensors #mistral #text-generation #autotrain #conversational #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" ]
[ 64, 29, 3 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #autotrain #conversational #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
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. --> # gpt-finetuned-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.3613 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.7968 | 1.0 | 1123 | 6.6783 | | 6.4613 | 2.0 | 2246 | 6.4416 | | 6.3067 | 3.0 | 3369 | 6.3613 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "gpt-finetuned-wikitext2", "results": []}]}
text-generation
GavinQiangLi/gpt-finetuned-wikitext2
[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:25:16+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
gpt-finetuned-wikitext2 ======================= This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 6.3613 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: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### 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: 2e-05\n* train\\_batch\\_size: 8\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: 3.0", "### 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 #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #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: 8\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: 3.0", "### 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, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #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: 8\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: 3.0### 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
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 flipchip167 -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 flipchip167 -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 flipchip167 ``` ## 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": "758.50 +/- 201.67", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
flipchip167/dqn-SpaceInvadersNoFrameskip-v4
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-07T16:27:11+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
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": "270.54 +/- 13.03", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Scott617/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-07T16:28:45+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
**NOTE:** This is a work-in-progress model that is **not** considered finished. Keep this in mind when using this model, or continue training this model. # Model Card for mt5-small nl-en translation The mt5-small nl-en translation model is a finetuned version of [google/mt5-small](https://huggingface.co/google/mt5-small). It was finetuned on 237k rows of the [iwslt2017](https://huggingface.co/datasets/iwslt2017/viewer/iwslt2017-en-nl) dataset and roughly 38k rows of the [opus_books](https://huggingface.co/datasets/opus_books/viewer/en-nl) dataset. The model was trained for 15 epochs with a batchsize of 8 and is currently **not** considered finished. ## How to use **Install dependencies** ```bash pip install transformers ``` You can use the following code for model inference. This model was finetuned to work with an identifier when prompted that needs to be present for the best results. ```Python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig tokenizer = AutoTokenizer.from_pretrained("Michielo/mt5-small_nl-en_translation") model = AutoModelForSeq2SeqLM.from_pretrained("Michielo/mt5-small_nl-en_translation") translation_generation_config = GenerationConfig( num_beams=4, early_stopping=True, decoder_start_token_id=0, eos_token_id=model.config.eos_token_id, pad_token=model.config.pad_token_id, ) translation_generation_config.save_pretrained("/tmp", "translation_generation_config.json") generation_config = GenerationConfig.from_pretrained("/tmp", "translation_generation_config.json") inputs = tokenizer(">>en<< Your dutch text here", return_tensors="pt") outputs = model.generate(**inputs, generation_config=generation_config) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ``` ## License This project is licensed under the Apache License 2.0 - see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.
{"language": ["en", "nl"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["opus_books", "iwslt2017"], "metrics": ["sacrebleu"], "pipeline_tag": "text2text-generation", "widget": [{"text": ">>en<< Was het leuk?"}]}
text2text-generation
Michielo/mt5-small_nl-en_translation
[ "transformers", "safetensors", "mt5", "text2text-generation", "translation", "en", "nl", "dataset:opus_books", "dataset:iwslt2017", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:29:32+00:00
[]
[ "en", "nl" ]
TAGS #transformers #safetensors #mt5 #text2text-generation #translation #en #nl #dataset-opus_books #dataset-iwslt2017 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
NOTE: This is a work-in-progress model that is not considered finished. Keep this in mind when using this model, or continue training this model. # Model Card for mt5-small nl-en translation The mt5-small nl-en translation model is a finetuned version of google/mt5-small. It was finetuned on 237k rows of the iwslt2017 dataset and roughly 38k rows of the opus_books dataset. The model was trained for 15 epochs with a batchsize of 8 and is currently not considered finished. ## How to use Install dependencies You can use the following code for model inference. This model was finetuned to work with an identifier when prompted that needs to be present for the best results. ## License This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
[ "# Model Card for mt5-small nl-en translation\n\nThe mt5-small nl-en translation model is a finetuned version of google/mt5-small.\n\nIt was finetuned on 237k rows of the iwslt2017 dataset and roughly 38k rows of the opus_books dataset. The model was trained for 15 epochs with a batchsize of 8 and is currently not considered finished.", "## How to use\n\nInstall dependencies\n\n\nYou can use the following code for model inference. This model was finetuned to work with an identifier when prompted that needs to be present for the best results.", "## License\nThis project is licensed under the Apache License 2.0 - see the LICENSE file for details." ]
[ "TAGS\n#transformers #safetensors #mt5 #text2text-generation #translation #en #nl #dataset-opus_books #dataset-iwslt2017 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for mt5-small nl-en translation\n\nThe mt5-small nl-en translation model is a finetuned version of google/mt5-small.\n\nIt was finetuned on 237k rows of the iwslt2017 dataset and roughly 38k rows of the opus_books dataset. The model was trained for 15 epochs with a batchsize of 8 and is currently not considered finished.", "## How to use\n\nInstall dependencies\n\n\nYou can use the following code for model inference. This model was finetuned to work with an identifier when prompted that needs to be present for the best results.", "## License\nThis project is licensed under the Apache License 2.0 - see the LICENSE file for details." ]
[ 81, 102, 44, 23 ]
[ "passage: TAGS\n#transformers #safetensors #mt5 #text2text-generation #translation #en #nl #dataset-opus_books #dataset-iwslt2017 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for mt5-small nl-en translation\n\nThe mt5-small nl-en translation model is a finetuned version of google/mt5-small.\n\nIt was finetuned on 237k rows of the iwslt2017 dataset and roughly 38k rows of the opus_books dataset. The model was trained for 15 epochs with a batchsize of 8 and is currently not considered finished.## How to use\n\nInstall dependencies\n\n\nYou can use the following code for model inference. This model was finetuned to work with an identifier when prompted that needs to be present for the best results.## License\nThis project is licensed under the Apache License 2.0 - see the LICENSE file for details." ]
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null
null
transformers
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61bf0e11c88f3fd22f654059/FiMCITBAaEyMyxCHhfWVD.png) # Polka-1.1B-Chat `eryk-mazus/polka-1.1b-chat` **is the first Polish model trained to act as a helpful, conversational assistant that can be run locally.** The model is based on [TinyLlama-1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) with the custom, extended tokenizer for more efficient Polish text generation, that was additionally pretrained on 5.7 billion tokens. **It was then fine-tuned on around 60k synthetically generated and machine-translated multi-turn conversations with the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) performed on top of it.** Context size: 4,096 tokens In addition, we're releasing: * [polka-1.1b](https://huggingface.co/eryk-mazus/polka-1.1b) - our base model with an extended tokenizer and additional pre-training on Polish corpus sampled using [DSIR](https://github.com/p-lambda/dsir) * [polka-pretrain-en-pl-v1](https://huggingface.co/datasets/eryk-mazus/polka-pretrain-en-pl-v1) - the pre-training dataset * [polka-dpo-v1](https://huggingface.co/datasets/eryk-mazus/polka-dpo-v1) - dataset of DPO pairs * [polka-1.1b-chat-gguf](https://huggingface.co/eryk-mazus/polka-1.1b-chat-gguf) - GGUF files for the chat model ## Usage Sample code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer model_name = "eryk-mazus/polka-1.1b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16, device_map="auto" ) streamer = TextStreamer(tokenizer, skip_prompt=True) # You are a helpful assistant. system_prompt = "Jesteś pomocnym asystentem." chat = [{"role": "system", "content": system_prompt}] # Compose a short song on programming. user_input = "Napisz krótką piosenkę o programowaniu." chat.append({"role": "user", "content": user_input}) # Generate - add_generation_prompt to make sure it continues as assistant inputs = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_tensors="pt") # For multi-GPU, find the device of the first parameter of the model first_param_device = next(model.parameters()).device inputs = inputs.to(first_param_device) with torch.no_grad(): outputs = model.generate( inputs, pad_token_id=tokenizer.eos_token_id, max_new_tokens=512, temperature=0.2, repetition_penalty=1.15, top_p=0.95, do_sample=True, streamer=streamer, ) # Add just the new tokens to our chat new_tokens = outputs[0, inputs.size(1):] response = tokenizer.decode(new_tokens, skip_special_tokens=True) chat.append({"role": "assistant", "content": response}) ``` The model works seamlessly with [vLLM](https://github.com/vllm-project/vllm) as well. ## Prompt format This model uses ChatML as the prompt format: ``` <|im_start|>system Jesteś pomocnym asystentem. <|im_start|>user Jakie jest dzienne zapotrzebowanie kaloryczne dorosłej osoby?<|im_end|> <|im_start|>assistant Dla dorosłych osób zaleca się spożywanie około 2000-3000 kcal dziennie, aby utrzymać optymalne zdrowie i dobre samopoczucie.<|im_end|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method, as demonstrated in the example above. *** We've actively looking for additional compute to train better and larger models for this project. If you want to collaborate, please reach out at: eryk.mazus at gmail dot com
{"language": ["pl"], "license": "mit", "tags": ["generated_from_trainer", "conversational", "polish"], "datasets": ["eryk-mazus/polka-dpo-v1"], "pipeline_tag": "text-generation", "inference": false}
text-generation
eryk-mazus/polka-1.1b-chat
[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "polish", "pl", "dataset:eryk-mazus/polka-dpo-v1", "arxiv:2305.18290", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:33:52+00:00
[ "2305.18290" ]
[ "pl" ]
TAGS #transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #polish #pl #dataset-eryk-mazus/polka-dpo-v1 #arxiv-2305.18290 #license-mit #autotrain_compatible #text-generation-inference #region-us
!image/png # Polka-1.1B-Chat 'eryk-mazus/polka-1.1b-chat' is the first Polish model trained to act as a helpful, conversational assistant that can be run locally. The model is based on TinyLlama-1.1B with the custom, extended tokenizer for more efficient Polish text generation, that was additionally pretrained on 5.7 billion tokens. It was then fine-tuned on around 60k synthetically generated and machine-translated multi-turn conversations with the Direct Preference Optimization (DPO) performed on top of it. Context size: 4,096 tokens In addition, we're releasing: * polka-1.1b - our base model with an extended tokenizer and additional pre-training on Polish corpus sampled using DSIR * polka-pretrain-en-pl-v1 - the pre-training dataset * polka-dpo-v1 - dataset of DPO pairs * polka-1.1b-chat-gguf - GGUF files for the chat model ## Usage Sample code: The model works seamlessly with vLLM as well. ## Prompt format This model uses ChatML as the prompt format: This prompt is available as a chat template, which means you can format messages using the 'tokenizer.apply_chat_template()' method, as demonstrated in the example above. * We've actively looking for additional compute to train better and larger models for this project. If you want to collaborate, please reach out at: URL at gmail dot com
[ "# Polka-1.1B-Chat\n\n'eryk-mazus/polka-1.1b-chat' is the first Polish model trained to act as a helpful, conversational assistant that can be run locally.\n\nThe model is based on TinyLlama-1.1B with the custom, extended tokenizer for more efficient Polish text generation, that was additionally pretrained on 5.7 billion tokens. It was then fine-tuned on around 60k synthetically generated and machine-translated multi-turn conversations with the Direct Preference Optimization (DPO) performed on top of it.\n\nContext size: 4,096 tokens\n\nIn addition, we're releasing:\n* polka-1.1b - our base model with an extended tokenizer and additional pre-training on Polish corpus sampled using DSIR\n* polka-pretrain-en-pl-v1 - the pre-training dataset\n* polka-dpo-v1 - dataset of DPO pairs\n* polka-1.1b-chat-gguf - GGUF files for the chat model", "## Usage\n\nSample code:\n\n\n\nThe model works seamlessly with vLLM as well.", "## Prompt format\n\nThis model uses ChatML as the prompt format:\n\n\nThis prompt is available as a chat template, which means you can format messages using the 'tokenizer.apply_chat_template()' method, as demonstrated in the example above.\n\n*\nWe've actively looking for additional compute to train better and larger models for this project. If you want to collaborate, please reach out at: URL at gmail dot com" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #polish #pl #dataset-eryk-mazus/polka-dpo-v1 #arxiv-2305.18290 #license-mit #autotrain_compatible #text-generation-inference #region-us \n", "# Polka-1.1B-Chat\n\n'eryk-mazus/polka-1.1b-chat' is the first Polish model trained to act as a helpful, conversational assistant that can be run locally.\n\nThe model is based on TinyLlama-1.1B with the custom, extended tokenizer for more efficient Polish text generation, that was additionally pretrained on 5.7 billion tokens. It was then fine-tuned on around 60k synthetically generated and machine-translated multi-turn conversations with the Direct Preference Optimization (DPO) performed on top of it.\n\nContext size: 4,096 tokens\n\nIn addition, we're releasing:\n* polka-1.1b - our base model with an extended tokenizer and additional pre-training on Polish corpus sampled using DSIR\n* polka-pretrain-en-pl-v1 - the pre-training dataset\n* polka-dpo-v1 - dataset of DPO pairs\n* polka-1.1b-chat-gguf - GGUF files for the chat model", "## Usage\n\nSample code:\n\n\n\nThe model works seamlessly with vLLM as well.", "## Prompt format\n\nThis model uses ChatML as the prompt format:\n\n\nThis prompt is available as a chat template, which means you can format messages using the 'tokenizer.apply_chat_template()' method, as demonstrated in the example above.\n\n*\nWe've actively looking for additional compute to train better and larger models for this project. If you want to collaborate, please reach out at: URL at gmail dot com" ]
[ 87, 244, 21, 97 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #polish #pl #dataset-eryk-mazus/polka-dpo-v1 #arxiv-2305.18290 #license-mit #autotrain_compatible #text-generation-inference #region-us \n# Polka-1.1B-Chat\n\n'eryk-mazus/polka-1.1b-chat' is the first Polish model trained to act as a helpful, conversational assistant that can be run locally.\n\nThe model is based on TinyLlama-1.1B with the custom, extended tokenizer for more efficient Polish text generation, that was additionally pretrained on 5.7 billion tokens. It was then fine-tuned on around 60k synthetically generated and machine-translated multi-turn conversations with the Direct Preference Optimization (DPO) performed on top of it.\n\nContext size: 4,096 tokens\n\nIn addition, we're releasing:\n* polka-1.1b - our base model with an extended tokenizer and additional pre-training on Polish corpus sampled using DSIR\n* polka-pretrain-en-pl-v1 - the pre-training dataset\n* polka-dpo-v1 - dataset of DPO pairs\n* polka-1.1b-chat-gguf - GGUF files for the chat model## Usage\n\nSample code:\n\n\n\nThe model works seamlessly with vLLM as well.## Prompt format\n\nThis model uses ChatML as the prompt format:\n\n\nThis prompt is available as a chat template, which means you can format messages using the 'tokenizer.apply_chat_template()' method, as demonstrated in the example above.\n\n*\nWe've actively looking for additional compute to train better and larger models for this project. If you want to collaborate, please reach out at: URL at gmail dot com" ]
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null
null
transformers
# MetricX-23 *This is not an officially supported Google product.* **GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)** This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/). The models were trained in [T5X](https://github.com/google-research/t5x) and then converted for use in PyTorch. ## Available Models There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * [MetricX-23-XXL](https://huggingface.co/google/metricx-23-large-v2p0) * [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0) * [MetricX-23-Large](https://huggingface.co/google/metricx-23-xxl-v2p0) * [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-large-v2p0) * [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0) * [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-xxl-v2p0) We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. ## Changes to the WMT'23 Submission These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/) then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. **That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score).** Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. | Failure mode | Synthetic example description | | ----------- | ----------- | | Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. | | Overtranslation | Candidate translation duplicated (with space in between). | | Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. | | Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). | | Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). | | Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. | | Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). | Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. ## Usage The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx). The repository contains example prediction scripts, described below. The `metricx23/predict.py` script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"reference"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-qe-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl \ --qe ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"source"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. ## Meta-Evaluation The `metricx23/evaluate.py` script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the [MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library. Example usage: ```bash python -m metricx23.evaluate \ --dataset wmt22 \ --lp en-de \ --input_file input.jsonl \ --output_file output.json ``` `input.jsonl` is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * `"system_id"`: The name of the system that generated the translation. * `"segment_id"`: The 0-based index of the corresponding segment in the MT Metrics Eval data. * `"label"`: The ground-truth translation quality score (with higher is better). * `"prediction"`: The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 | | MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 | | MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 | | MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 | | MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 | | MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 | English-Russian: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 | | MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 | | MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 | | MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 | | MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 | | MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 | Chinese-English: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 | | MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 | | MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 | | MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 | | MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 | | MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 | The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation score that was used to rank submissions from the [WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf). Example usage: ```bash python -m metricx23.evaluate_wmt23 \ --en_de predictions_ende.jsonl \ --he_en predictions_heen.jsonl \ --zh_en predictions_zhen.jsonl \ --output_file output.json ``` Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: | Model | Average Correlation | | ----------- | ----------- | | MetricX-23-XXL | 0.812 | | MetricX-23-XL | 0.813 | | MetricX-23-Large | 0.794 | | MetricX-23-QE-XXL | 0.797 | | MetricX-23-QE-XL | 0.767 | | MetricX-23-QE-Large | 0.762 | ## Citation If you use MetricX-23 in your research, please cite the following publication: ```bibtex @inproceedings{juraska-etal-2023-metricx, title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}}, author = "Juraska, Juraj and Finkelstein, Mara and Deutsch, Daniel and Siddhant, Aditya and Mirzazadeh, Mehdi and Freitag, Markus", editor = "Koehn, Philipp and Haddow, Barry and Kocmi, Tom and Monz, Christof", booktitle = "Proceedings of the Eighth Conference on Machine Translation", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wmt-1.63", doi = "10.18653/v1/2023.wmt-1.63", pages = "756--767", } ```
{"license": "apache-2.0"}
null
google/metricx-23-xl-v2p0
[ "transformers", "pytorch", "mt5", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:34:17+00:00
[]
[]
TAGS #transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
MetricX-23 ========== *This is not an officially supported Google product.* GitHub repository: URL This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. Available Models ---------------- There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * MetricX-23-XXL * MetricX-23-XL * MetricX-23-Large * MetricX-23-QE-XXL * MetricX-23-QE-XL * MetricX-23-QE-Large We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. Changes to the WMT'23 Submission -------------------------------- These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with mT5 then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score). Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. Usage ----- The code for using MetricX models can be found at URL The repository contains example prediction scripts, described below. The 'metricx23/URL' script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: 'URL' is expected to have 1 serialized JSON object per line with '"reference"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: 'URL' is expected to have 1 serialized JSON object per line with '"source"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Meta-Evaluation --------------- The 'metricx23/URL' script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the MT Metrics Eval library. Example usage: 'URL' is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * '"system\_id"': The name of the system that generated the translation. * '"segment\_id"': The 0-based index of the corresponding segment in the MT Metrics Eval data. * '"label"': The ground-truth translation quality score (with higher is better). * '"prediction"': The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: English-Russian: Chinese-English: The 'metricx23/evaluate\_wmt23.py' script re-calculates the average correlation score that was used to rank submissions from the WMT'23 Shared Task. Example usage: Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: If you use MetricX-23 in your research, please cite the following publication:
[ "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ "TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ 42, 666, 111, 457 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "passage: ### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior." ]
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null
null
transformers
# MetricX-23 *This is not an officially supported Google product.* **GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)** This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/). The models were trained in [T5X](https://github.com/google-research/t5x) and then converted for use in PyTorch. ## Available Models There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * [MetricX-23-XXL](https://huggingface.co/google/metricx-23-large-v2p0) * [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0) * [MetricX-23-Large](https://huggingface.co/google/metricx-23-xxl-v2p0) * [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-large-v2p0) * [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0) * [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-xxl-v2p0) We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. ## Changes to the WMT'23 Submission These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/) then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. **That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score).** Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. | Failure mode | Synthetic example description | | ----------- | ----------- | | Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. | | Overtranslation | Candidate translation duplicated (with space in between). | | Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. | | Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). | | Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). | | Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. | | Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). | Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. ## Usage The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx). The repository contains example prediction scripts, described below. The `metricx23/predict.py` script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"reference"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-qe-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl \ --qe ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"source"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. ## Meta-Evaluation The `metricx23/evaluate.py` script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the [MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library. Example usage: ```bash python -m metricx23.evaluate \ --dataset wmt22 \ --lp en-de \ --input_file input.jsonl \ --output_file output.json ``` `input.jsonl` is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * `"system_id"`: The name of the system that generated the translation. * `"segment_id"`: The 0-based index of the corresponding segment in the MT Metrics Eval data. * `"label"`: The ground-truth translation quality score (with higher is better). * `"prediction"`: The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 | | MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 | | MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 | | MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 | | MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 | | MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 | English-Russian: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 | | MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 | | MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 | | MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 | | MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 | | MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 | Chinese-English: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 | | MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 | | MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 | | MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 | | MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 | | MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 | The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation score that was used to rank submissions from the [WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf). Example usage: ```bash python -m metricx23.evaluate_wmt23 \ --en_de predictions_ende.jsonl \ --he_en predictions_heen.jsonl \ --zh_en predictions_zhen.jsonl \ --output_file output.json ``` Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: | Model | Average Correlation | | ----------- | ----------- | | MetricX-23-XXL | 0.812 | | MetricX-23-XL | 0.813 | | MetricX-23-Large | 0.794 | | MetricX-23-QE-XXL | 0.797 | | MetricX-23-QE-XL | 0.767 | | MetricX-23-QE-Large | 0.762 | ## Citation If you use MetricX-23 in your research, please cite the following publication: ```bibtex @inproceedings{juraska-etal-2023-metricx, title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}}, author = "Juraska, Juraj and Finkelstein, Mara and Deutsch, Daniel and Siddhant, Aditya and Mirzazadeh, Mehdi and Freitag, Markus", editor = "Koehn, Philipp and Haddow, Barry and Kocmi, Tom and Monz, Christof", booktitle = "Proceedings of the Eighth Conference on Machine Translation", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wmt-1.63", doi = "10.18653/v1/2023.wmt-1.63", pages = "756--767", } ```
{"license": "apache-2.0"}
null
google/metricx-23-xxl-v2p0
[ "transformers", "pytorch", "mt5", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:34:37+00:00
[]
[]
TAGS #transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
MetricX-23 ========== *This is not an officially supported Google product.* GitHub repository: URL This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. Available Models ---------------- There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * MetricX-23-XXL * MetricX-23-XL * MetricX-23-Large * MetricX-23-QE-XXL * MetricX-23-QE-XL * MetricX-23-QE-Large We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. Changes to the WMT'23 Submission -------------------------------- These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with mT5 then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score). Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. Usage ----- The code for using MetricX models can be found at URL The repository contains example prediction scripts, described below. The 'metricx23/URL' script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: 'URL' is expected to have 1 serialized JSON object per line with '"reference"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: 'URL' is expected to have 1 serialized JSON object per line with '"source"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Meta-Evaluation --------------- The 'metricx23/URL' script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the MT Metrics Eval library. Example usage: 'URL' is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * '"system\_id"': The name of the system that generated the translation. * '"segment\_id"': The 0-based index of the corresponding segment in the MT Metrics Eval data. * '"label"': The ground-truth translation quality score (with higher is better). * '"prediction"': The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: English-Russian: Chinese-English: The 'metricx23/evaluate\_wmt23.py' script re-calculates the average correlation score that was used to rank submissions from the WMT'23 Shared Task. Example usage: Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: If you use MetricX-23 in your research, please cite the following publication:
[ "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ "TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ 42, 666, 111, 457 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "passage: ### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior." ]
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transformers
# MetricX-23 *This is not an officially supported Google product.* **GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)** This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/). The models were trained in [T5X](https://github.com/google-research/t5x) and then converted for use in PyTorch. ## Available Models There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * [MetricX-23-XXL](https://huggingface.co/google/metricx-23-large-v2p0) * [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0) * [MetricX-23-Large](https://huggingface.co/google/metricx-23-xxl-v2p0) * [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-large-v2p0) * [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0) * [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-xxl-v2p0) We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. ## Changes to the WMT'23 Submission These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/) then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. **That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score).** Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. | Failure mode | Synthetic example description | | ----------- | ----------- | | Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. | | Overtranslation | Candidate translation duplicated (with space in between). | | Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. | | Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). | | Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). | | Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. | | Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). | Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. ## Usage The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx). The repository contains example prediction scripts, described below. The `metricx23/predict.py` script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"reference"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-qe-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl \ --qe ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"source"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. ## Meta-Evaluation The `metricx23/evaluate.py` script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the [MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library. Example usage: ```bash python -m metricx23.evaluate \ --dataset wmt22 \ --lp en-de \ --input_file input.jsonl \ --output_file output.json ``` `input.jsonl` is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * `"system_id"`: The name of the system that generated the translation. * `"segment_id"`: The 0-based index of the corresponding segment in the MT Metrics Eval data. * `"label"`: The ground-truth translation quality score (with higher is better). * `"prediction"`: The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 | | MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 | | MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 | | MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 | | MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 | | MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 | English-Russian: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 | | MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 | | MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 | | MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 | | MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 | | MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 | Chinese-English: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 | | MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 | | MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 | | MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 | | MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 | | MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 | The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation score that was used to rank submissions from the [WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf). Example usage: ```bash python -m metricx23.evaluate_wmt23 \ --en_de predictions_ende.jsonl \ --he_en predictions_heen.jsonl \ --zh_en predictions_zhen.jsonl \ --output_file output.json ``` Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: | Model | Average Correlation | | ----------- | ----------- | | MetricX-23-XXL | 0.812 | | MetricX-23-XL | 0.813 | | MetricX-23-Large | 0.794 | | MetricX-23-QE-XXL | 0.797 | | MetricX-23-QE-XL | 0.767 | | MetricX-23-QE-Large | 0.762 | ## Citation If you use MetricX-23 in your research, please cite the following publication: ```bibtex @inproceedings{juraska-etal-2023-metricx, title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}}, author = "Juraska, Juraj and Finkelstein, Mara and Deutsch, Daniel and Siddhant, Aditya and Mirzazadeh, Mehdi and Freitag, Markus", editor = "Koehn, Philipp and Haddow, Barry and Kocmi, Tom and Monz, Christof", booktitle = "Proceedings of the Eighth Conference on Machine Translation", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wmt-1.63", doi = "10.18653/v1/2023.wmt-1.63", pages = "756--767", } ```
{"license": "apache-2.0"}
null
google/metricx-23-qe-xxl-v2p0
[ "transformers", "pytorch", "mt5", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:34:57+00:00
[]
[]
TAGS #transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
MetricX-23 ========== *This is not an officially supported Google product.* GitHub repository: URL This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. Available Models ---------------- There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * MetricX-23-XXL * MetricX-23-XL * MetricX-23-Large * MetricX-23-QE-XXL * MetricX-23-QE-XL * MetricX-23-QE-Large We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. Changes to the WMT'23 Submission -------------------------------- These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with mT5 then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score). Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. Usage ----- The code for using MetricX models can be found at URL The repository contains example prediction scripts, described below. The 'metricx23/URL' script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: 'URL' is expected to have 1 serialized JSON object per line with '"reference"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: 'URL' is expected to have 1 serialized JSON object per line with '"source"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Meta-Evaluation --------------- The 'metricx23/URL' script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the MT Metrics Eval library. Example usage: 'URL' is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * '"system\_id"': The name of the system that generated the translation. * '"segment\_id"': The 0-based index of the corresponding segment in the MT Metrics Eval data. * '"label"': The ground-truth translation quality score (with higher is better). * '"prediction"': The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: English-Russian: Chinese-English: The 'metricx23/evaluate\_wmt23.py' script re-calculates the average correlation score that was used to rank submissions from the WMT'23 Shared Task. Example usage: Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: If you use MetricX-23 in your research, please cite the following publication:
[ "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ "TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ 42, 666, 111, 457 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "passage: ### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior." ]
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null
null
transformers
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6459a451abdbb77c4c6d8258/uNoKlBulkRF3mCoMgetGs.png) --- Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below: - [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2-GGUF) - [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) ```yaml slices: - sources: - model: Orca2flat layer_range: [0, 16] - sources: - model: LLaMA2-13B-Psyfighter2 (FP16 not yet available) layer_range: [8, 24] - sources: - model: Orca2flat layer_range: [17, 32] - sources: - model: LLaMA2-13B-Psyfighter2 (FP16 not yet available) layer_range: [25, 40] merge_method: passthrough dtype: float16 ``` Note: while we did run an inverted merge the output was not satisfactory and will not be released. We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion. This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail. Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly. During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices. Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well. Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [[email protected]]. Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model, and to gelukuMLG for the model mascot!
{"license": "other", "tags": ["storywriting", "text adventure", "not-for-all-audiences"], "license_name": "microsoft-research-license"}
text-generation
zaq-hack/psyonic-cetacean-20B-bpw350-h6-exl2-rpcal
[ "transformers", "safetensors", "llama", "text-generation", "storywriting", "text adventure", "not-for-all-audiences", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:35:02+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #storywriting #text adventure #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
!image/png --- Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below: - KoboldAI/PsyFighter-2-13b - microsoft/Orca-2-13b Note: while we did run an inverted merge the output was not satisfactory and will not be released. We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion. This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail. Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly. During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices. Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well. Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@URL]. Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model, and to gelukuMLG for the model mascot!
[]
[ "TAGS\n#transformers #safetensors #llama #text-generation #storywriting #text adventure #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 67 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #storywriting #text adventure #not-for-all-audiences #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
diffusers
MeinaMix Objective is to be able to do good art with little prompting. For examples and prompts, please checkout: https://civitai.com/models/7240/meinamix I have a discord server where you can post images that you generated, discuss prompt and/or ask for help. https://discord.gg/XC9nGZNDUd If you like one of my models and want to support their updates I've made a ko-fi page; https://ko-fi.com/meina where you can pay me a coffee <3 And a Patreon page; https://www.patreon.com/MeinaMix where you can support me and get acess to beta of my models! You may also try this model using Sinkin.ai: https://sinkin.ai/m/vln8Nwr MeinaMix and the other of Meinas will ALWAYS be FREE. Recommendations of use: Enable Quantization in K samplers. Hires.fix is needed for prompts where the character is far away in order to make decent images, it drastically improve the quality of face and eyes! Recommended parameters: Sampler: Euler a: 40 to 60 steps. Sampler: DPM++ SDE Karras: 20 to 30 steps. Sampler: DPM++ 2M Karras: 20 to 40 steps. CFG Scale: 7. Resolutions: 512x768, 512x1024 for Portrait! Resolutions: 768x512, 1024x512, 1536x512 for Landscape! Hires.fix: R-ESRGAN 4x+Anime6b, with 10 steps at 0.3 up to 0.5 denoising. Clip Skip: 2. Negatives: ' (worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic) '
{"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["art", "anime", "stable diffusion"], "pipeline_tag": "text-to-image"}
text-to-image
roktimsardar123/MeinaMix_V11
[ "diffusers", "safetensors", "art", "anime", "stable diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-07T16:35:05+00:00
[]
[ "en" ]
TAGS #diffusers #safetensors #art #anime #stable diffusion #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
MeinaMix Objective is to be able to do good art with little prompting. For examples and prompts, please checkout: URL I have a discord server where you can post images that you generated, discuss prompt and/or ask for help. URL If you like one of my models and want to support their updates I've made a ko-fi page; URL where you can pay me a coffee <3 And a Patreon page; URL where you can support me and get acess to beta of my models! You may also try this model using URL: URL MeinaMix and the other of Meinas will ALWAYS be FREE. Recommendations of use: Enable Quantization in K samplers. URL is needed for prompts where the character is far away in order to make decent images, it drastically improve the quality of face and eyes! Recommended parameters: Sampler: Euler a: 40 to 60 steps. Sampler: DPM++ SDE Karras: 20 to 30 steps. Sampler: DPM++ 2M Karras: 20 to 40 steps. CFG Scale: 7. Resolutions: 512x768, 512x1024 for Portrait! Resolutions: 768x512, 1024x512, 1536x512 for Landscape! URL: R-ESRGAN 4x+Anime6b, with 10 steps at 0.3 up to 0.5 denoising. Clip Skip: 2. Negatives: ' (worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic) '
[]
[ "TAGS\n#diffusers #safetensors #art #anime #stable diffusion #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n" ]
[ 66 ]
[ "passage: TAGS\n#diffusers #safetensors #art #anime #stable diffusion #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n" ]
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transformers
# MetricX-23 *This is not an officially supported Google product.* **GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)** This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/). The models were trained in [T5X](https://github.com/google-research/t5x) and then converted for use in PyTorch. ## Available Models There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * [MetricX-23-XXL](https://huggingface.co/google/metricx-23-large-v2p0) * [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0) * [MetricX-23-Large](https://huggingface.co/google/metricx-23-xxl-v2p0) * [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-large-v2p0) * [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0) * [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-xxl-v2p0) We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. ## Changes to the WMT'23 Submission These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/) then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. **That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score).** Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. | Failure mode | Synthetic example description | | ----------- | ----------- | | Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. | | Overtranslation | Candidate translation duplicated (with space in between). | | Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. | | Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). | | Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). | | Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. | | Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). | Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. ## Usage The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx). The repository contains example prediction scripts, described below. The `metricx23/predict.py` script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"reference"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-qe-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl \ --qe ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"source"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. ## Meta-Evaluation The `metricx23/evaluate.py` script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the [MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library. Example usage: ```bash python -m metricx23.evaluate \ --dataset wmt22 \ --lp en-de \ --input_file input.jsonl \ --output_file output.json ``` `input.jsonl` is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * `"system_id"`: The name of the system that generated the translation. * `"segment_id"`: The 0-based index of the corresponding segment in the MT Metrics Eval data. * `"label"`: The ground-truth translation quality score (with higher is better). * `"prediction"`: The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 | | MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 | | MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 | | MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 | | MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 | | MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 | English-Russian: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 | | MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 | | MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 | | MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 | | MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 | | MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 | Chinese-English: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 | | MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 | | MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 | | MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 | | MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 | | MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 | The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation score that was used to rank submissions from the [WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf). Example usage: ```bash python -m metricx23.evaluate_wmt23 \ --en_de predictions_ende.jsonl \ --he_en predictions_heen.jsonl \ --zh_en predictions_zhen.jsonl \ --output_file output.json ``` Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: | Model | Average Correlation | | ----------- | ----------- | | MetricX-23-XXL | 0.812 | | MetricX-23-XL | 0.813 | | MetricX-23-Large | 0.794 | | MetricX-23-QE-XXL | 0.797 | | MetricX-23-QE-XL | 0.767 | | MetricX-23-QE-Large | 0.762 | ## Citation If you use MetricX-23 in your research, please cite the following publication: ```bibtex @inproceedings{juraska-etal-2023-metricx, title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}}, author = "Juraska, Juraj and Finkelstein, Mara and Deutsch, Daniel and Siddhant, Aditya and Mirzazadeh, Mehdi and Freitag, Markus", editor = "Koehn, Philipp and Haddow, Barry and Kocmi, Tom and Monz, Christof", booktitle = "Proceedings of the Eighth Conference on Machine Translation", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wmt-1.63", doi = "10.18653/v1/2023.wmt-1.63", pages = "756--767", } ```
{"license": "apache-2.0"}
null
google/metricx-23-qe-xl-v2p0
[ "transformers", "pytorch", "mt5", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:35:10+00:00
[]
[]
TAGS #transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
MetricX-23 ========== *This is not an officially supported Google product.* GitHub repository: URL This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. Available Models ---------------- There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * MetricX-23-XXL * MetricX-23-XL * MetricX-23-Large * MetricX-23-QE-XXL * MetricX-23-QE-XL * MetricX-23-QE-Large We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. Changes to the WMT'23 Submission -------------------------------- These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with mT5 then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score). Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. Usage ----- The code for using MetricX models can be found at URL The repository contains example prediction scripts, described below. The 'metricx23/URL' script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: 'URL' is expected to have 1 serialized JSON object per line with '"reference"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: 'URL' is expected to have 1 serialized JSON object per line with '"source"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Meta-Evaluation --------------- The 'metricx23/URL' script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the MT Metrics Eval library. Example usage: 'URL' is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * '"system\_id"': The name of the system that generated the translation. * '"segment\_id"': The 0-based index of the corresponding segment in the MT Metrics Eval data. * '"label"': The ground-truth translation quality score (with higher is better). * '"prediction"': The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: English-Russian: Chinese-English: The 'metricx23/evaluate\_wmt23.py' script re-calculates the average correlation score that was used to rank submissions from the WMT'23 Shared Task. Example usage: Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: If you use MetricX-23 in your research, please cite the following publication:
[ "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ "TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ 42, 666, 111, 457 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "passage: ### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior." ]
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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. --> # videomae-base-finetuned-kinetics-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2309 - Accuracy: 0.9806 ## 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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 148 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2587 | 0.13 | 19 | 1.2644 | 1.0 | | 0.6711 | 1.13 | 38 | 0.2098 | 1.0 | | 0.1355 | 2.13 | 57 | 0.0465 | 1.0 | | 0.0295 | 3.13 | 76 | 0.0431 | 0.9857 | | 0.0155 | 4.13 | 95 | 0.0226 | 1.0 | | 0.0175 | 5.13 | 114 | 0.0178 | 1.0 | | 0.0168 | 6.13 | 133 | 0.0180 | 1.0 | | 0.008 | 7.1 | 148 | 0.0184 | 1.0 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.11.0 - Tokenizers 0.15.1
{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base-finetuned-kinetics", "model-index": [{"name": "videomae-base-finetuned-kinetics-finetuned-ucf101-subset", "results": []}]}
video-classification
Saini-Manisha/videomae-base-finetuned-kinetics-finetuned-ucf101-subset
[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
2024-02-07T16:35:16+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base-finetuned-kinetics #license-cc-by-nc-4.0 #endpoints_compatible #region-us
videomae-base-finetuned-kinetics-finetuned-ucf101-subset ======================================================== This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.2309 * Accuracy: 0.9806 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 * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 148 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.2.0+cu121 * Datasets 2.11.0 * 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 148", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.11.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base-finetuned-kinetics #license-cc-by-nc-4.0 #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: 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* training\\_steps: 148", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.11.0\n* Tokenizers 0.15.1" ]
[ 77, 115, 4, 38 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base-finetuned-kinetics #license-cc-by-nc-4.0 #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: 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* training\\_steps: 148### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.11.0\n* Tokenizers 0.15.1" ]
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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. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (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 <!-- This should link to a Dataset 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. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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 [More Information Needed] #### 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:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"}
null
noza-kit/Adapter_llama2_translate_Q_enpt_ex2-3epoch
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]
2024-02-07T16:35:34+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us
# Model Card for Model ID ## Model Details ### Model Description - 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\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 0.8.2" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #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 0.8.2" ]
[ 41, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #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 0.8.2" ]
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null
null
transformers
# MetricX-23 *This is not an officially supported Google product.* **GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)** This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/). The models were trained in [T5X](https://github.com/google-research/t5x) and then converted for use in PyTorch. ## Available Models There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * [MetricX-23-XXL](https://huggingface.co/google/metricx-23-large-v2p0) * [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0) * [MetricX-23-Large](https://huggingface.co/google/metricx-23-xxl-v2p0) * [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-large-v2p0) * [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0) * [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-xxl-v2p0) We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. ## Changes to the WMT'23 Submission These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/) then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. **That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score).** Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. | Failure mode | Synthetic example description | | ----------- | ----------- | | Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. | | Overtranslation | Candidate translation duplicated (with space in between). | | Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. | | Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). | | Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). | | Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. | | Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). | Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. ## Usage The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx). The repository contains example prediction scripts, described below. The `metricx23/predict.py` script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"reference"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-qe-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl \ --qe ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"source"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. ## Meta-Evaluation The `metricx23/evaluate.py` script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the [MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library. Example usage: ```bash python -m metricx23.evaluate \ --dataset wmt22 \ --lp en-de \ --input_file input.jsonl \ --output_file output.json ``` `input.jsonl` is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * `"system_id"`: The name of the system that generated the translation. * `"segment_id"`: The 0-based index of the corresponding segment in the MT Metrics Eval data. * `"label"`: The ground-truth translation quality score (with higher is better). * `"prediction"`: The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 | | MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 | | MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 | | MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 | | MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 | | MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 | English-Russian: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 | | MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 | | MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 | | MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 | | MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 | | MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 | Chinese-English: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 | | MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 | | MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 | | MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 | | MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 | | MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 | The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation score that was used to rank submissions from the [WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf). Example usage: ```bash python -m metricx23.evaluate_wmt23 \ --en_de predictions_ende.jsonl \ --he_en predictions_heen.jsonl \ --zh_en predictions_zhen.jsonl \ --output_file output.json ``` Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: | Model | Average Correlation | | ----------- | ----------- | | MetricX-23-XXL | 0.812 | | MetricX-23-XL | 0.813 | | MetricX-23-Large | 0.794 | | MetricX-23-QE-XXL | 0.797 | | MetricX-23-QE-XL | 0.767 | | MetricX-23-QE-Large | 0.762 | ## Citation If you use MetricX-23 in your research, please cite the following publication: ```bibtex @inproceedings{juraska-etal-2023-metricx, title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}}, author = "Juraska, Juraj and Finkelstein, Mara and Deutsch, Daniel and Siddhant, Aditya and Mirzazadeh, Mehdi and Freitag, Markus", editor = "Koehn, Philipp and Haddow, Barry and Kocmi, Tom and Monz, Christof", booktitle = "Proceedings of the Eighth Conference on Machine Translation", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wmt-1.63", doi = "10.18653/v1/2023.wmt-1.63", pages = "756--767", } ```
{"license": "apache-2.0"}
null
google/metricx-23-qe-large-v2p0
[ "transformers", "pytorch", "mt5", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:35:44+00:00
[]
[]
TAGS #transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
MetricX-23 ========== *This is not an officially supported Google product.* GitHub repository: URL This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. Available Models ---------------- There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * MetricX-23-XXL * MetricX-23-XL * MetricX-23-Large * MetricX-23-QE-XXL * MetricX-23-QE-XL * MetricX-23-QE-Large We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. Changes to the WMT'23 Submission -------------------------------- These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with mT5 then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score). Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. Usage ----- The code for using MetricX models can be found at URL The repository contains example prediction scripts, described below. The 'metricx23/URL' script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: 'URL' is expected to have 1 serialized JSON object per line with '"reference"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: 'URL' is expected to have 1 serialized JSON object per line with '"source"' and '"hypothesis"' fields. The output jsonl will be parallel to 'URL' but additionally contain a '"prediction"' field with the predicted score. Meta-Evaluation --------------- The 'metricx23/URL' script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the MT Metrics Eval library. Example usage: 'URL' is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * '"system\_id"': The name of the system that generated the translation. * '"segment\_id"': The 0-based index of the corresponding segment in the MT Metrics Eval data. * '"label"': The ground-truth translation quality score (with higher is better). * '"prediction"': The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: English-Russian: Chinese-English: The 'metricx23/evaluate\_wmt23.py' script re-calculates the average correlation score that was used to rank submissions from the WMT'23 Shared Task. Example usage: Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: If you use MetricX-23 in your research, please cite the following publication:
[ "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ "TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.", "### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior.", "### Reference-Free\n\n\nExample usage for a reference-free model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"source\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nMeta-Evaluation\n---------------\n\n\nThe 'metricx23/URL' script contains code to calculate various correlations\nbetween the MetricX-23 scores and MQM ratings of translation quality using the\nMT Metrics Eval library.\n\n\nExample usage:\n\n\n'URL' is expected to have one JSON object serialized per line.\nEach JSON object is expected to contain 4 fields:\n\n\n* '\"system\\_id\"': The name of the system that generated the translation.\n* '\"segment\\_id\"': The 0-based index of the corresponding segment in the MT\nMetrics Eval data.\n* '\"label\"': The ground-truth translation quality score (with higher is better).\n* '\"prediction\"': The model predicted translation quality score (with lower is\nbetter; the script negates the scores so higher is better).\n\n\nThe script will calculate the 4 agreement/correlations that were used in the\nWMT'23 Shared Task. Below are the results for the MetricX-23 models on the\nWMT'22 Metrics Shared Task data:\n\n\nEnglish-German:\n\n\n\nEnglish-Russian:\n\n\n\nChinese-English:\n\n\n\nThe 'metricx23/evaluate\\_wmt23.py' script re-calculates the average correlation\nscore that was used to rank submissions from the\nWMT'23 Shared Task.\n\n\nExample usage:\n\n\nEach of the 3 input files is expected to be in the same format as described\nabove. Each file should correspond to running inference on each of the language\npairs from the WMT'23 dataset.\n\n\nThe results for each of the models is the following:\n\n\n\nIf you use MetricX-23 in your research, please cite the following publication:" ]
[ 42, 666, 111, 457 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "passage: ### Synthetic Data\n\n\nIn order for our MetricX models to learn to identify certain types of bad\ntranslations that are not sufficiently (or at all) represented in the regular\ntraining data, we created synthetic examples and mixed them in during training.\nThe synthetic training data was generated from the DA datasets ranging from\nWMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have\nthe candidate translation manipulated so as to turn it into a bad translation\nwith a specific issue commonly unrecognized by learned metrics.\n\n\nThe table below provides an overview of the various failure modes that we\nconsidered, including brief descriptions of how we prepared the synthetic data\nto address them.\n\n\n\nExamples from the first 4 categories were assigned a label corresponding to the\nworst score on the given rating scale (e.g., 25 when mixed with MQM training\ndata), whereas the reference-matching translation examples are assigned the best\nscore (e.g., 0 when used with MQM data). The missing/incorrect punctuation\nexamples were labeled with a score slightly worse than perfect.\n\n\nNote that some of the synthetic datasets are only meaningful in the\nreference-based scenario, and we thus excluded them when training a QE variant\nof MetricX. These are the Latin-vs-special punctuation and the\nreference-matching translation examples.\n\n\nMost of the synthetic training sets were created using stratified sampling\nacross target languages, taking 500 examples per target language. One exception\nis the missing punctuation set, which used a stratified sample across different\npunctuation symbols instead.\n\n\nWhen training MetricX, a small proportion of the synthetic examples was mixed\nwith the regular training examples. During the first-stage fine-tuning on DA\ndata, each synthetic training set constituted between 0.1% and 1% of all\ntraining examples, whereas in the second-stage fine-tuning on MQM data we used\nan even smaller proportion, around 0.05%.\n\n\nAs for evaluating the effect of the synthetic training data on the model's\nperformance, the DEMETR challenge set - which we originally used to evaluate the\nmodels submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We\ntherefore created a new DEMETR-style test set based on the WMT22 DA data, with\nexamples constructed analogically to the synthetic training examples, as\ndescribed above. This test set helped us determine the right proportions of\nsynthetic data for fine-tuning in order to make MetricX robust for the failure\nmodes in consideration, without sacrificing the system- and segment-level\ncorrelations with human ratings.\n\n\nUsage\n-----\n\n\nThe code for using MetricX models can be found at URL\nThe repository contains example prediction scripts, described below.\n\n\nThe 'metricx23/URL' script contains an example for how to run inference\non the models.### Reference-Based\n\n\nExample usage for a reference-based model:\n\n\n'URL' is expected to have 1 serialized JSON object per line with\n'\"reference\"' and '\"hypothesis\"' fields. The output jsonl will be parallel\nto 'URL' but additionally contain a '\"prediction\"' field with the predicted score.\n\n\nNote that the model was trained with a maximum input length of 1024 tokens, so\nsignificantly increasing that value may lead to unpredictable behavior." ]
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null
null
diffusers
# DreamBooth - mustafakara/duck This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of rsu monster toy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "dreambooth"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of rsu monster toy", "inference": true}
text-to-image
mustafakara/duck
[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-07T16:37:08+00:00
[]
[]
TAGS #diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
# DreamBooth - mustafakara/duck This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of rsu monster toy using DreamBooth. You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
[ "# DreamBooth - mustafakara/duck\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of rsu monster toy using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# DreamBooth - mustafakara/duck\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of rsu monster toy using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
[ 97, 78 ]
[ "passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# DreamBooth - mustafakara/duck\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of rsu monster toy using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
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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. --> # spatio_temporal_vit-finetuned-ucf101-subset This model is a fine-tuned version of [Tommidi/st_vit_untrained](https://huggingface.co/Tommidi/st_vit_untrained) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1244 - Accuracy: 0.9 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 37 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6013 | 1.0 | 37 | 0.1244 | 0.9 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "Tommidi/st_vit_untrained", "model-index": [{"name": "spatio_temporal_vit-finetuned-ucf101-subset", "results": []}]}
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Tommidi/spatio_temporal_vit-finetuned-ucf101-subset
[ "transformers", "tensorboard", "safetensors", "st_vit", "generated_from_trainer", "base_model:Tommidi/st_vit_untrained", "endpoints_compatible", "region:us" ]
2024-02-07T16:39:37+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #st_vit #generated_from_trainer #base_model-Tommidi/st_vit_untrained #endpoints_compatible #region-us
spatio\_temporal\_vit-finetuned-ucf101-subset ============================================= This model is a fine-tuned version of Tommidi/st\_vit\_untrained on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.1244 * Accuracy: 0.9 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 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 37 ### 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* 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* training\\_steps: 37", "### 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 #st_vit #generated_from_trainer #base_model-Tommidi/st_vit_untrained #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* 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* training\\_steps: 37", "### 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" ]
[ 52, 115, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #st_vit #generated_from_trainer #base_model-Tommidi/st_vit_untrained #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* 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* training\\_steps: 37### 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
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": "169.08 +/- 100.02", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
scifarer/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-07T16:42:17+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
diffusers
# SDXL LoRA DreamBooth - StorkelOpa/ancient-world <Gallery /> ## Model description ### These are StorkelOpa/ancient-world LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - **LoRA**: download **[`ancient-world.safetensors` here 💾](/StorkelOpa/ancient-world/blob/main/ancient-world.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:ancient-world:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - *Embeddings*: download **[`ancient-world_emb.safetensors` here 💾](/StorkelOpa/ancient-world/blob/main/ancient-world_emb.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `ancient-world_emb` to your prompt. For example, `ancient world painting` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('StorkelOpa/ancient-world', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='StorkelOpa/ancient-world', filename='ancient-world_emb.safetensors' repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('ancient world painting').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/StorkelOpa/ancient-world/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "ancient world painting of Earth's Early Landscape, Showcasing Towering Mountains, Deep Valleys, and Volcanic Activity, Circa 4.5 Billion Years Ago.", "output": {"url": "image-0.png"}}, {"text": "ancient world painting of Earth's Early Ocean Floor, Alive with Primitive Plant Life Amidst Volcanic Rock Formations, Circa 3.5 Billion Years Ago.", "output": {"url": "image-1.png"}}, {"text": "ancient world painting of Cambrian Marine Life, Featuring Trilobites and Jellyfish Amidst Ocean Flora.", "output": {"url": "image-2.png"}}, {"text": "ancient world painting of the Cambrian Seabed, Featuring the Trilobites Paradoxides gracilis, Comocoryphe sulzeri, and Ptychoparia striata, with the Stalked Echinoderm Acadocrinus jani and the Algae Dalya, Set Against a Backdrop of Jellyfish in the Open Water.", "output": {"url": "image-3.png"}}, {"text": "ancient world painting of Upper Silurian Marine Life, with Predatory Nautiloids and Sea Lilies in a Coral Seabed Landscape.", "output": {"url": "image-4.png"}}, {"text": "ancient world painting of the Late Silurian Period, Depicting the First Land Plant Invasion with Primitive Psilophytes Colonizing Coastal Floodplains and Marshes.", "output": {"url": "image-5.png"}}, {"text": "ancient world painting of Middle Devonian Flora, Featuring True Horsetails, Clubmosses, and Ferns Amidst a Primitive Landscape with Waterfalls and Rocky Terrain.", "output": {"url": "image-6.png"}}, {"text": "ancient world painting", "output": {"url": "image-7.png"}}, {"text": "ancient world painting of Early Devonian Aquatic Life, Depicting Osteolepis Attacking Heterostracan Armored Fish with Primitive Plants in the Foreground.", "output": {"url": "image-8.png"}}, {"text": "ancient world painting of Devonian Aquatic Ecosystem, Illustrating Armored Placoderms Like Pterichthyodes and Bothrialepis Navigating the Ocean Floor.", "output": {"url": "image-9.png"}}, {"text": "ancient world painting of Devonian Sea Life, Showcasing the Arthrodira Placoderms in a Dynamic Underwater Scene.", "output": {"url": "image-10.png"}}, {"text": "ancient world painting of Silurian to Devonian Freshwater Fish, Depicting the Primitive Acanthodii Group with Climatius, Euthacanthus, and Parexus.", "output": {"url": "image-11.png"}}, {"text": "ancient world painting of Late Devonian Landscape, Featuring Ichthyostega and the Differentiated Archaeopteris Flora with Cyclostigma Trees and Sphenophyllum Plants.", "output": {"url": "image-12.png"}}, {"text": "ancient world painting", "output": {"url": "image-13.png"}}, {"text": "ancient world painting", "output": {"url": "image-14.png"}}, {"text": "ancient world painting", "output": {"url": "image-15.png"}}, {"text": "ancient world painting", "output": {"url": "image-16.png"}}, {"text": "ancient world painting", "output": {"url": 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text-to-image
StorkelOpa/ancient-world
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]
2024-02-07T16:43:00+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
# SDXL LoRA DreamBooth - StorkelOpa/ancient-world <Gallery /> ## Model description ### These are StorkelOpa/ancient-world LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download 'ancient-world.safetensors' here . - Place it on your 'models/Lora' folder. - On AUTOMATIC1111, load the LoRA by adding '<lora:ancient-world:1>' to your prompt. On ComfyUI just load it as a regular LoRA. - *Embeddings*: download 'ancient-world_emb.safetensors' here . - Place it on it on your 'embeddings' folder - Use it by adding 'ancient-world_emb' to your prompt. For example, 'ancient world painting' (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the diffusers library For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept 'TOK' → use '<s0><s1>' in your prompt ## Details All Files & versions. The weights were trained using diffusers Advanced Dreambooth Training Script. LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
[ "# SDXL LoRA DreamBooth - StorkelOpa/ancient-world\n\n<Gallery />", "## Model description", "### These are StorkelOpa/ancient-world LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.", "## Download model", "### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'ancient-world.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:ancient-world:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'ancient-world_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'ancient-world_emb' to your prompt. For example, 'ancient world painting'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)", "## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers", "## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt", "## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - StorkelOpa/ancient-world\n\n<Gallery />", "## Model description", "### These are StorkelOpa/ancient-world LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.", "## Download model", "### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'ancient-world.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:ancient-world:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'ancient-world_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'ancient-world_emb' to your prompt. For example, 'ancient world painting'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)", "## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers", "## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt", "## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix." ]
[ 86, 23, 3, 37, 3, 190, 38, 54, 74 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - StorkelOpa/ancient-world\n\n<Gallery />## Model description### These are StorkelOpa/ancient-world LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.## Download model### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'ancient-world.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:ancient-world:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'ancient-world_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'ancient-world_emb' to your prompt. For example, 'ancient world painting'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt" ]
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null
null
transformers
# 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
Adeptschneider/mistral_lora_instruct_model
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T16:43:03+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. - 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
[ "# 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 #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 #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. --> # robust_llm_pythia-tt-1b-mz-v0 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown 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: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b-deduped", "model-index": [{"name": "robust_llm_pythia-tt-1b-mz-v0", "results": []}]}
text-classification
AlignmentResearch/robust_llm_pythia-tt-1b-mz-v0
[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:44:14+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-1b-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# robust_llm_pythia-tt-1b-mz-v0 This model is a fine-tuned version of EleutherAI/pythia-1b-deduped on an unknown 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: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# robust_llm_pythia-tt-1b-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-1b-deduped on an unknown 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: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-1b-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# robust_llm_pythia-tt-1b-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-1b-deduped on an unknown 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: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 87, 49, 6, 12, 8, 3, 90, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-1b-deduped #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# robust_llm_pythia-tt-1b-mz-v0\n\nThis model is a fine-tuned version of EleutherAI/pythia-1b-deduped on an unknown 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: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.37.1\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
# 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
token-classification
MahtaFetrat/ner_transformer
[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T16:44:45+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #bert #token-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: - 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
[ "# 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 #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #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 #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #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. --> # wav2vec_RTSplit0208_3 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: 0.0234 - Wer: 0.2221 - Cer: 0.1631 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.8578 | 1.0 | 120 | 3.5714 | 0.9484 | 0.9843 | | 1.5722 | 2.0 | 240 | 1.3923 | 0.9926 | 0.7587 | | 0.9411 | 3.0 | 360 | 0.7411 | 0.7542 | 0.4648 | | 0.6235 | 4.0 | 480 | 0.4713 | 0.6691 | 0.3789 | | 0.4954 | 5.0 | 600 | 0.3408 | 0.5381 | 0.3106 | | 0.3909 | 6.0 | 720 | 0.2140 | 0.3727 | 0.2213 | | 0.2891 | 7.0 | 840 | 0.1158 | 0.2806 | 0.1666 | | 0.2193 | 8.0 | 960 | 0.0602 | 0.2556 | 0.1736 | | 0.1925 | 9.0 | 1080 | 0.0287 | 0.2206 | 0.1586 | | 0.1094 | 10.0 | 1200 | 0.0234 | 0.2221 | 0.1631 | ### 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_RTSplit0208_3", "results": []}]}
automatic-speech-recognition
tndklab/wav2vec_RTSplit0208_3
[ "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" ]
2024-02-07T16:45:55+00:00
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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\_RTSplit0208\_3 ======================= 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: 0.0234 * Wer: 0.2221 * Cer: 0.1631 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
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-b5-finetuned-segments-instryde-foot-test This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the inStryde/inStrydeSegmentationFoot dataset. It achieves the following results on the evaluation set: - Loss: 0.0149 - Mean Iou: 0.4800 - Mean Accuracy: 0.9599 - Overall Accuracy: 0.9599 - Per Category Iou: [0.0, 0.9599216842864238] - Per Category Accuracy: [nan, 0.9599216842864238] ## 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: 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 | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:| | 0.1024 | 0.27 | 20 | 0.2085 | 0.4534 | 0.9067 | 0.9067 | [0.0, 0.9067344993758137] | [nan, 0.9067344993758137] | | 0.0431 | 0.53 | 40 | 0.0487 | 0.4604 | 0.9207 | 0.9207 | [0.0, 0.9207331455341442] | [nan, 0.9207331455341442] | | 0.0354 | 0.8 | 60 | 0.0319 | 0.4577 | 0.9155 | 0.9155 | [0.0, 0.9154662028576415] | [nan, 0.9154662028576415] | | 0.0389 | 1.07 | 80 | 0.0276 | 0.4629 | 0.9257 | 0.9257 | [0.0, 0.9257162800419576] | [nan, 0.9257162800419576] | | 0.0208 | 1.33 | 100 | 0.0244 | 0.4702 | 0.9404 | 0.9404 | [0.0, 0.9403945317069335] | [nan, 0.9403945317069335] | | 0.0241 | 1.6 | 120 | 0.0212 | 0.4703 | 0.9406 | 0.9406 | [0.0, 0.9406131407017349] | [nan, 0.9406131407017349] | | 0.0167 | 1.87 | 140 | 0.0208 | 0.4761 | 0.9521 | 0.9521 | [0.0, 0.9521215619420916] | [nan, 0.9521215619420916] | | 0.0156 | 2.13 | 160 | 0.0205 | 0.4612 | 0.9224 | 0.9224 | [0.0, 0.9224359945462809] | [nan, 0.9224359945462809] | | 0.0156 | 2.4 | 180 | 0.0208 | 0.4734 | 0.9468 | 0.9468 | [0.0, 0.9467575875538612] | [nan, 0.9467575875538612] | | 0.0167 | 2.67 | 200 | 0.0182 | 0.4833 | 0.9667 | 0.9667 | [0.0, 0.9666659635383208] | [nan, 0.9666659635383208] | | 0.0145 | 2.93 | 220 | 0.0243 | 0.4351 | 0.8702 | 0.8702 | [0.0, 0.8702122233110058] | [nan, 0.8702122233110058] | | 0.0114 | 3.2 | 240 | 0.0176 | 0.4686 | 0.9373 | 0.9373 | [0.0, 0.93726765603217] | [nan, 0.93726765603217] | | 0.0155 | 3.47 | 260 | 0.0161 | 0.4770 | 0.9541 | 0.9541 | [0.0, 0.9540767701096305] | [nan, 0.9540767701096305] | | 0.0158 | 3.73 | 280 | 0.0169 | 0.4684 | 0.9368 | 0.9368 | [0.0, 0.9368239181251786] | [nan, 0.9368239181251786] | | 0.0114 | 4.0 | 300 | 0.0162 | 0.4777 | 0.9554 | 0.9554 | [0.0, 0.9554348305492647] | [nan, 0.9554348305492647] | | 0.0112 | 4.27 | 320 | 0.0159 | 0.4839 | 0.9678 | 0.9678 | [0.0, 0.9677532556440432] | [nan, 0.9677532556440432] | | 0.0131 | 4.53 | 340 | 0.0154 | 0.4811 | 0.9622 | 0.9622 | [0.0, 0.9622032718479555] | [nan, 0.9622032718479555] | | 0.0101 | 4.8 | 360 | 0.0156 | 0.4683 | 0.9367 | 0.9367 | [0.0, 0.9366846987126999] | [nan, 0.9366846987126999] | | 0.0102 | 5.07 | 380 | 0.0152 | 0.4758 | 0.9517 | 0.9517 | [0.0, 0.9516509773164403] | [nan, 0.9516509773164403] | | 0.0101 | 5.33 | 400 | 0.0169 | 0.4884 | 0.9768 | 0.9768 | [0.0, 0.9768393358121804] | [nan, 0.9768393358121804] | | 0.0082 | 5.6 | 420 | 0.0150 | 0.4761 | 0.9522 | 0.9522 | [0.0, 0.9522462074215836] | [nan, 0.9522462074215836] | | 0.01 | 5.87 | 440 | 0.0152 | 0.4788 | 0.9576 | 0.9576 | [0.0, 0.9575745140264517] | [nan, 0.9575745140264517] | | 0.0098 | 6.13 | 460 | 0.0148 | 0.4783 | 0.9565 | 0.9565 | [0.0, 0.9565489693736469] | [nan, 0.9565489693736469] | | 0.0088 | 6.4 | 480 | 0.0153 | 0.4795 | 0.9591 | 0.9591 | [0.0, 0.959051850601846] | [nan, 0.959051850601846] | | 0.0091 | 6.67 | 500 | 0.0152 | 0.4828 | 0.9656 | 0.9656 | [0.0, 0.965590177169167] | [nan, 0.965590177169167] | | 0.0102 | 6.93 | 520 | 0.0149 | 0.4800 | 0.9599 | 0.9599 | [0.0, 0.9599216842864238] | [nan, 0.9599216842864238] | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b5", "model-index": [{"name": "segformer-b5-finetuned-segments-instryde-foot-test", "results": []}]}
image-segmentation
PostsDesert/segformer-b5-finetuned-segments-instryde-foot-test
[ "transformers", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b5", "license:other", "endpoints_compatible", "region:us" ]
2024-02-07T16:47:37+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b5 #license-other #endpoints_compatible #region-us
segformer-b5-finetuned-segments-instryde-foot-test ================================================== This model is a fine-tuned version of nvidia/mit-b5 on the inStryde/inStrydeSegmentationFoot dataset. It achieves the following results on the evaluation set: * Loss: 0.0149 * Mean Iou: 0.4800 * Mean Accuracy: 0.9599 * Overall Accuracy: 0.9599 * Per Category Iou: [0.0, 0.9599216842864238] * Per Category Accuracy: [nan, 0.9599216842864238] 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: 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 2.0.1 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\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\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b5 #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\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\n* Transformers 4.37.2\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 62, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b5 #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\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\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. --> # mistral_yt_transcribe_classification This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0363 ## 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-07 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - total_eval_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0398 | 1.0 | 427 | 0.0380 | | 0.0268 | 2.0 | 854 | 0.0323 | | 0.0188 | 3.0 | 1281 | 0.0334 | | 0.0135 | 4.0 | 1708 | 0.0363 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.1+cu118 - Datasets 2.17.0 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_yt_transcribe_classification", "results": []}]}
text-generation
hiiamsid/mistral_yt_transcribe_classification
[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T16:51:58+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
mistral\_yt\_transcribe\_classification ======================================= This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0363 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-07 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 6 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 24 * total\_eval\_batch\_size: 6 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 4 ### Training results ### Framework versions * Transformers 4.36.0 * Pytorch 2.0.1+cu118 * Datasets 2.17.0 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* total\\_eval\\_batch\\_size: 6\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #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: 5e-07\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* total\\_eval\\_batch\\_size: 6\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ 85, 180, 4, 35 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #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: 5e-07\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* total\\_eval\\_batch\\_size: 6\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.36.0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
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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. --> # feb7th This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0464 - Accuracy: 0.9899 ## 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: 8 - seed: 1234 - gradient_accumulation_steps: 10 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.97 | 12 | 0.0598 | 0.9798 | | No log | 1.94 | 24 | 0.0480 | 0.9879 | | No log | 2.98 | 37 | 0.0531 | 0.9838 | | No log | 3.95 | 49 | 0.0456 | 0.9899 | | No log | 4.84 | 60 | 0.0464 | 0.9899 | ### 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": "facebook/deit-base-distilled-patch16-224", "model-index": [{"name": "feb7th", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9898785425101214, "name": "Accuracy"}]}]}]}
image-classification
sruthis/feb7th
[ "transformers", "safetensors", "deit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-base-distilled-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T16:55:10+00:00
[]
[]
TAGS #transformers #safetensors #deit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-distilled-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
feb7th ====== This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.0464 * Accuracy: 0.9899 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: 8 * seed: 1234 * gradient\_accumulation\_steps: 10 * total\_train\_batch\_size: 160 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### 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: 8\n* seed: 1234\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### 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 #safetensors #deit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-distilled-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: 16\n* eval\\_batch\\_size: 8\n* seed: 1234\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### 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" ]
[ 85, 127, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #deit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-distilled-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: 16\n* eval\\_batch\\_size: 8\n* seed: 1234\n* gradient\\_accumulation\\_steps: 10\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### 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. --> # SMIDS_3x_beit_large_SGD_lr001_fold2 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: 0.2730 - Accuracy: 0.9101 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6183 | 1.0 | 450 | 0.5281 | 0.8103 | | 0.4308 | 2.0 | 900 | 0.3746 | 0.8669 | | 0.3321 | 3.0 | 1350 | 0.3194 | 0.8869 | | 0.2741 | 4.0 | 1800 | 0.2941 | 0.8835 | | 0.2911 | 5.0 | 2250 | 0.2779 | 0.8952 | | 0.2345 | 6.0 | 2700 | 0.2676 | 0.8985 | | 0.2656 | 7.0 | 3150 | 0.2640 | 0.8985 | | 0.2454 | 8.0 | 3600 | 0.2617 | 0.8985 | | 0.2325 | 9.0 | 4050 | 0.2651 | 0.8935 | | 0.2736 | 10.0 | 4500 | 0.2583 | 0.8968 | | 0.2631 | 11.0 | 4950 | 0.2630 | 0.8918 | | 0.2185 | 12.0 | 5400 | 0.2609 | 0.8985 | | 0.1998 | 13.0 | 5850 | 0.2581 | 0.8968 | | 0.2041 | 14.0 | 6300 | 0.2537 | 0.9002 | | 0.2148 | 15.0 | 6750 | 0.2607 | 0.9052 | | 0.2184 | 16.0 | 7200 | 0.2551 | 0.9018 | | 0.1852 | 17.0 | 7650 | 0.2565 | 0.9018 | | 0.144 | 18.0 | 8100 | 0.2589 | 0.9068 | | 0.2342 | 19.0 | 8550 | 0.2648 | 0.9052 | | 0.1959 | 20.0 | 9000 | 0.2552 | 0.9068 | | 0.1454 | 21.0 | 9450 | 0.2555 | 0.9085 | | 0.2119 | 22.0 | 9900 | 0.2629 | 0.9101 | | 0.2103 | 23.0 | 10350 | 0.2595 | 0.9085 | | 0.1202 | 24.0 | 10800 | 0.2671 | 0.9085 | | 0.1769 | 25.0 | 11250 | 0.2606 | 0.9101 | | 0.1659 | 26.0 | 11700 | 0.2665 | 0.9101 | | 0.1642 | 27.0 | 12150 | 0.2638 | 0.9101 | | 0.159 | 28.0 | 12600 | 0.2681 | 0.9101 | | 0.2289 | 29.0 | 13050 | 0.2645 | 0.9118 | | 0.1249 | 30.0 | 13500 | 0.2685 | 0.9085 | | 0.1195 | 31.0 | 13950 | 0.2692 | 0.9085 | | 0.1041 | 32.0 | 14400 | 0.2692 | 0.9068 | | 0.2053 | 33.0 | 14850 | 0.2639 | 0.9101 | | 0.1366 | 34.0 | 15300 | 0.2708 | 0.9085 | | 0.1378 | 35.0 | 15750 | 0.2715 | 0.9118 | | 0.1913 | 36.0 | 16200 | 0.2686 | 0.9118 | | 0.1193 | 37.0 | 16650 | 0.2681 | 0.9118 | | 0.0953 | 38.0 | 17100 | 0.2718 | 0.9118 | | 0.2328 | 39.0 | 17550 | 0.2715 | 0.9101 | | 0.081 | 40.0 | 18000 | 0.2739 | 0.9101 | | 0.1182 | 41.0 | 18450 | 0.2750 | 0.9118 | | 0.1418 | 42.0 | 18900 | 0.2721 | 0.9101 | | 0.142 | 43.0 | 19350 | 0.2739 | 0.9101 | | 0.1749 | 44.0 | 19800 | 0.2729 | 0.9101 | | 0.2226 | 45.0 | 20250 | 0.2735 | 0.9118 | | 0.0963 | 46.0 | 20700 | 0.2738 | 0.9135 | | 0.1257 | 47.0 | 21150 | 0.2731 | 0.9118 | | 0.0797 | 48.0 | 21600 | 0.2732 | 0.9101 | | 0.1681 | 49.0 | 22050 | 0.2730 | 0.9101 | | 0.2027 | 50.0 | 22500 | 0.2730 | 0.9101 | ### 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_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9101497504159733, "name": "Accuracy"}]}]}]}
image-classification
onizukal/SMIDS_3x_beit_large_SGD_lr001_fold2
[ "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" ]
2024-02-07T16:55:44+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\_fold2 ========================================= 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: 0.2730 * Accuracy: 0.9101 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
[ "### 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" ]
[ "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" ]
[ 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: 0.001\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
<!-- 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. --> # crossencoder-airline-refine - This model is trained on open source airline related dataset. - The base model is "cross-encoder/stsb-roberta-large" ## Model description - Cross encoder is useful when we want to calculate the similarity between search query and data items. - If a Cross-Encoder model is trained on a representative training set, it achieves higher accuracy than Bi-Encoders. - A Cross-Encoder does not produce a sentence embedding. Also, we are not able to pass individual sentences to a Cross-Encoder. ## Intended uses & limitations - The model is finetuned on limited data. - It might not produce right result in airline related text. - Model will be finetuned increamentally based on the availablity of the data. ## Training and evaluation data - Below is the example of training data format for cross encoder. - Training data has sentence1, sentence2 and the similarity score between the two sentence. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/644744cd3ce50963d1c60d76/1_FqzYCXq0yTSF0BD-gJG.png) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - 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 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 2 | 2.0393 | | No log | 2.0 | 4 | 1.3405 | | No log | 3.0 | 6 | 0.9373 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1
{"tags": ["generated_from_trainer"], "model-index": [{"name": "crossencoder-airline-refine", "results": []}]}
text-classification
srmishra/crossencoder-airline-refine
[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T16:57:17+00:00
[]
[]
TAGS #transformers #safetensors #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
crossencoder-airline-refine =========================== * This model is trained on open source airline related dataset. * The base model is "cross-encoder/stsb-roberta-large" Model description ----------------- * Cross encoder is useful when we want to calculate the similarity between search query and data items. * If a Cross-Encoder model is trained on a representative training set, it achieves higher accuracy than Bi-Encoders. * A Cross-Encoder does not produce a sentence embedding. Also, we are not able to pass individual sentences to a Cross-Encoder. Intended uses & limitations --------------------------- * The model is finetuned on limited data. * It might not produce right result in airline related text. * Model will be finetuned increamentally based on the availablity of the data. Training and evaluation data ---------------------------- * Below is the example of training data format for cross encoder. * Training data has sentence1, sentence2 and the similarity score between the two sentence. !image/png ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 8 * 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 * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.1.0 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 45, 116, 4, 30 ]
[ "passage: TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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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_RMSProp_lr00001_fold2 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: 1.0857 - Accuracy: 0.9151 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2703 | 1.0 | 450 | 0.2711 | 0.9035 | | 0.0491 | 2.0 | 900 | 0.4201 | 0.9018 | | 0.0826 | 3.0 | 1350 | 0.4300 | 0.9118 | | 0.0476 | 4.0 | 1800 | 0.6646 | 0.9085 | | 0.0003 | 5.0 | 2250 | 0.6381 | 0.9135 | | 0.1483 | 6.0 | 2700 | 0.7657 | 0.9035 | | 0.0469 | 7.0 | 3150 | 0.7235 | 0.9068 | | 0.0897 | 8.0 | 3600 | 0.9031 | 0.8985 | | 0.1009 | 9.0 | 4050 | 0.7978 | 0.8968 | | 0.058 | 10.0 | 4500 | 0.9975 | 0.8735 | | 0.0 | 11.0 | 4950 | 0.9432 | 0.9035 | | 0.035 | 12.0 | 5400 | 0.8407 | 0.8885 | | 0.0 | 13.0 | 5850 | 0.9152 | 0.9035 | | 0.0 | 14.0 | 6300 | 0.9215 | 0.9018 | | 0.03 | 15.0 | 6750 | 0.9905 | 0.8852 | | 0.0 | 16.0 | 7200 | 0.9325 | 0.8952 | | 0.0524 | 17.0 | 7650 | 0.7955 | 0.9002 | | 0.0 | 18.0 | 8100 | 0.8288 | 0.9168 | | 0.0001 | 19.0 | 8550 | 0.9944 | 0.9101 | | 0.0 | 20.0 | 9000 | 0.9683 | 0.9151 | | 0.0301 | 21.0 | 9450 | 0.9011 | 0.9018 | | 0.0 | 22.0 | 9900 | 0.9396 | 0.9068 | | 0.0001 | 23.0 | 10350 | 1.0498 | 0.9168 | | 0.0 | 24.0 | 10800 | 1.0341 | 0.9118 | | 0.0001 | 25.0 | 11250 | 0.9397 | 0.9002 | | 0.0 | 26.0 | 11700 | 0.8880 | 0.9135 | | 0.0 | 27.0 | 12150 | 0.8988 | 0.9185 | | 0.0 | 28.0 | 12600 | 0.9994 | 0.9201 | | 0.006 | 29.0 | 13050 | 0.9581 | 0.9218 | | 0.0 | 30.0 | 13500 | 0.9983 | 0.9018 | | 0.0 | 31.0 | 13950 | 0.9678 | 0.9151 | | 0.0 | 32.0 | 14400 | 0.9493 | 0.9085 | | 0.0 | 33.0 | 14850 | 1.0032 | 0.9135 | | 0.0 | 34.0 | 15300 | 0.9780 | 0.9185 | | 0.0 | 35.0 | 15750 | 0.9913 | 0.9135 | | 0.0 | 36.0 | 16200 | 1.0181 | 0.9201 | | 0.0 | 37.0 | 16650 | 0.9777 | 0.9201 | | 0.0 | 38.0 | 17100 | 1.0351 | 0.9151 | | 0.0 | 39.0 | 17550 | 1.0920 | 0.9135 | | 0.0052 | 40.0 | 18000 | 1.1361 | 0.9118 | | 0.0 | 41.0 | 18450 | 1.1228 | 0.9052 | | 0.0 | 42.0 | 18900 | 1.1117 | 0.9068 | | 0.0 | 43.0 | 19350 | 1.0631 | 0.9135 | | 0.0 | 44.0 | 19800 | 1.0763 | 0.9118 | | 0.0 | 45.0 | 20250 | 1.0861 | 0.9118 | | 0.0 | 46.0 | 20700 | 1.1097 | 0.9135 | | 0.0 | 47.0 | 21150 | 1.0778 | 0.9151 | | 0.005 | 48.0 | 21600 | 1.0803 | 0.9151 | | 0.0 | 49.0 | 22050 | 1.0804 | 0.9151 | | 0.0 | 50.0 | 22500 | 1.0857 | 0.9151 | ### 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_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9151414309484193, "name": "Accuracy"}]}]}]}
image-classification
onizukal/SMIDS_3x_beit_large_RMSProp_lr00001_fold2
[ "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" ]
2024-02-07T17:01:51+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\_fold2 =============================================== 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: 1.0857 * Accuracy: 0.9151 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
null
A test quantization of OpenHermes-2.5-Mistral-7B by teknium using importance matrices computed on Ukrainian text, hopefully decreasing the coherence hit after quantization in Ukrainian at the cost of some performance in other languages. Importance matrix was computed in roughly 20 minutes with a Ryzen 5 3550H and GTX 1650 with 8 layers offloaded. Will be updated with perplexity testing later, probably? 😭 Haven't done proper tests quite yet, feels better than old quants when chatting in Ukrainian, hopefully I get around to actually benching it somehow
{"license": "apache-2.0"}
null
interrobang/OpenHermes-2.5-Mistral-7B-GGUF-ukrainian-imatrix
[ "gguf", "license:apache-2.0", "region:us" ]
2024-02-07T17:04:31+00:00
[]
[]
TAGS #gguf #license-apache-2.0 #region-us
A test quantization of OpenHermes-2.5-Mistral-7B by teknium using importance matrices computed on Ukrainian text, hopefully decreasing the coherence hit after quantization in Ukrainian at the cost of some performance in other languages. Importance matrix was computed in roughly 20 minutes with a Ryzen 5 3550H and GTX 1650 with 8 layers offloaded. Will be updated with perplexity testing later, probably? Haven't done proper tests quite yet, feels better than old quants when chatting in Ukrainian, hopefully I get around to actually benching it somehow
[]
[ "TAGS\n#gguf #license-apache-2.0 #region-us \n" ]
[ 17 ]
[ "passage: TAGS\n#gguf #license-apache-2.0 #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. --> # output This model is a fine-tuned version of [](https://huggingface.co/) on an unknown 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: 0.0001 - train_batch_size: 64 - 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_ratio: 0.1 - num_epochs: 120 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "model-index": [{"name": "output", "results": []}]}
null
io-roboto/decision-transformer
[ "transformers", "tensorboard", "safetensors", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]
2024-02-07T17:13:22+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #decision_transformer #generated_from_trainer #endpoints_compatible #region-us
# output This model is a fine-tuned version of [](URL on an unknown 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: 0.0001 - train_batch_size: 64 - 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_ratio: 0.1 - num_epochs: 120 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# output\n\nThis model is a fine-tuned version of [](URL on an unknown 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: 0.0001\n- train_batch_size: 64\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_ratio: 0.1\n- num_epochs: 120", "### 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 #decision_transformer #generated_from_trainer #endpoints_compatible #region-us \n", "# output\n\nThis model is a fine-tuned version of [](URL on an unknown 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: 0.0001\n- train_batch_size: 64\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_ratio: 0.1\n- num_epochs: 120", "### 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" ]
[ 40, 24, 6, 12, 8, 3, 104, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #decision_transformer #generated_from_trainer #endpoints_compatible #region-us \n# output\n\nThis model is a fine-tuned version of [](URL on an unknown 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: 0.0001\n- train_batch_size: 64\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_ratio: 0.1\n- num_epochs: 120### 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
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
jegilj/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:13:42+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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<!-- 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. --> # zephyr-support-chatbot-david-v4 This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) 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 - 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 2.17.0 - Tokenizers 0.15.1
{"license": "mit", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/zephyr-7B-alpha-GPTQ", "model-index": [{"name": "zephyr-support-chatbot-david-v4", "results": []}]}
null
David19930/zephyr-support-chatbot-david-v4
[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]
2024-02-07T17:15:25+00:00
[]
[]
TAGS #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-alpha-GPTQ #license-mit #region-us
# zephyr-support-chatbot-david-v4 This model is a fine-tuned version of TheBloke/zephyr-7B-alpha-GPTQ 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 - 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 2.17.0 - Tokenizers 0.15.1
[ "# zephyr-support-chatbot-david-v4\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-alpha-GPTQ 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- 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 2.17.0\n- Tokenizers 0.15.1" ]
[ "TAGS\n#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-alpha-GPTQ #license-mit #region-us \n", "# zephyr-support-chatbot-david-v4\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-alpha-GPTQ 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- 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 2.17.0\n- Tokenizers 0.15.1" ]
[ 53, 47, 6, 12, 8, 3, 102, 4, 33 ]
[ "passage: TAGS\n#tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-alpha-GPTQ #license-mit #region-us \n# zephyr-support-chatbot-david-v4\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-alpha-GPTQ 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- 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 2.17.0\n- Tokenizers 0.15.1" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
MiVaCod/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:15:35+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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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": "275.40 +/- 22.27", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Poliuszko/ppo-LunarLander-v21-1
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-07T17:16:49+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
# 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
text-generation
manche/gpt2-safeguard-sg1
[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T17:18:03+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #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
[ "# 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 #gpt2 #text-generation #arxiv-1910.09700 #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 #gpt2 #text-generation #arxiv-1910.09700 #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
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
crrodrvi/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:20:34+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
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nemo
# Canary 1B <style> img { display: inline; } </style> [![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--Transformer-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-1B-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-multilingual-lightgrey#model-badge)](#datasets) NVIDIA [NeMo Canary](https://nvidia.github.io/NeMo/blogs/2024/2024-02-canary/) is a family of multi-lingual multi-tasking models that achieves state-of-the art performance on multiple benchmarks. With 1 billion parameters, Canary-1B supports automatic speech-to-text recognition (ASR) in 4 languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). ## Model Architecture Canary is an encoder-decoder model with FastConformer [1] encoder and Transformer Decoder [2]. With audio features extracted from the encoder, task tokens such as `<source language>`, `<target language>`, `<task>` and `<toggle PnC>` are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer from individual SentencePiece [3] tokenizers of each language, which makes it easy to scale up to more languages. The Canay-1B model has 24 encoder layers and 24 layers of decoder layers in total. ## NVIDIA NeMo To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed Cython and latest PyTorch version. ``` pip install git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Loading the Model ```python from nemo.collections.asr.models import EncDecMultiTaskModel # load model canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b') # update dcode params decode_cfg = canary_model.cfg.decoding decode_cfg.beam.beam_size = 1 canary_model.change_decoding_strategy(decode_cfg) ``` ### Input Format The input to the model can be a directory containing audio files, in which case the model will perform ASR on English and produces text with punctuation and capitalization: ```python predicted_text = canary_model.transcribe( audio_dir="<path to directory containing audios>", batch_size=16, # batch size to run the inference with ) ``` or use: ```bash python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/canary-1b" audio_dir="<path to audio directory>" ``` Another recommended option is to use a json manifest as input, where each line in the file is a dictionary containing the following fields: ```yaml # Example of a line in input_manifest.json { "audio_filepath": "/path/to/audio.wav", # path to the audio file "duration": 1000, # duration of the audio "taskname": "asr", # use "ast" for speech-to-text translation "source_lang": "en", # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr'] "target_lang": "en", # language of the text output, choices=['en','de','es','fr'] "pnc": "yes", # whether to have PnC output, choices=['yes', 'no'] "answer": "na", } ``` and then use: ```python predicted_text = canary_model.transcribe( "<path to input manifest file>", batch_size=16, # batch size to run the inference with ) ``` or use: ```bash python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/canary-1b" dataset_manifest="<path to manifest file>" ``` ### Automatic Speech-to-text Recognition (ASR) An example manifest for transcribing English audios can be: ```yaml # Example of a line in input_manifest.json { "audio_filepath": "/path/to/audio.wav", # path to the audio file "duration": 1000, # duration of the audio "taskname": "asr", "source_lang": "en", # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr'] "target_lang": "en", # language of the text output, choices=['en','de','es','fr'] "pnc": "yes", # whether to have PnC output, choices=['yes', 'no'] "answer": "na", } ``` ### Automatic Speech-to-text Translation (AST) An example manifest for transcribing English audios into German text can be: ```yaml # Example of a line in input_manifest.json { "audio_filepath": "/path/to/audio.wav", # path to the audio file "duration": 1000, # duration of the audio "taskname": "ast", "source_lang": "en", # language of the audio input, choices=['en','de','es','fr'] "target_lang": "de", # language of the text output, choices=['en','de','es','fr'] "pnc": "yes", # whether to have PnC output, choices=['yes', 'no'] "answer": "na" } ``` ### Input This model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input. ### Output The model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization. ## Training Canary-1B is trained using the NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs. The model can be trained using this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_multitask/speech_to_text_aed.py) and [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/speech_multitask/fast-conformer_aed.yaml). The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). ### Datasets The Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by [Suno](https://suno.ai/), and 34k hrs of in-house data. The constituents of public data are as follows. #### English (25.5k hours) - Librispeech 960 hours - Fisher Corpus - Switchboard-1 Dataset - WSJ-0 and WSJ-1 - National Speech Corpus (Part 1, Part 6) - VCTK - VoxPopuli (EN) - Europarl-ASR (EN) - Multilingual Librispeech (MLS EN) - 2,000 hour subset - Mozilla Common Voice (v7.0) - People's Speech - 12,000 hour subset - Mozilla Common Voice (v11.0) - 1,474 hour subset #### German (2.5k hours) - Mozilla Common Voice (v12.0) - 800 hour subset - Multilingual Librispeech (MLS DE) - 1,500 hour subset - VoxPopuli (DE) - 200 hr subset #### Spanish (1.4k hours) - Mozilla Common Voice (v12.0) - 395 hour subset - Multilingual Librispeech (MLS ES) - 780 hour subset - VoxPopuli (ES) - 108 hour subset - Fisher - 141 hour subset #### French (1.8k hours) - Mozilla Common Voice (v12.0) - 708 hour subset - Multilingual Librispeech (MLS FR) - 926 hour subset - VoxPopuli (FR) - 165 hour subset ## Performance In both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0. ### ASR Performance (w/o PnC) The ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with [whisper-normalizer](https://pypi.org/project/whisper-normalizer/). WER on [MCV-16.1](https://commonvoice.mozilla.org/en/datasets) test set: | **Version** | **Model** | **En** | **De** | **Es** | **Fr** | |:---------:|:-----------:|:------:|:------:|:------:|:------:| | 1.23.0 | canary-1b | 7.97 | 4.61 | 3.99 | 6.53 | WER on [MLS](https://huggingface.co/datasets/facebook/multilingual_librispeech) test set: | **Version** | **Model** | **En** | **De** | **Es** | **Fr** | |:---------:|:-----------:|:------:|:------:|:------:|:------:| | 1.23.0 | canary-1b | 3.06 | 4.19 | 3.15 | 4.12 | More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard) ### AST Performance We evaluate AST performance with [BLEU score](https://lightning.ai/docs/torchmetrics/stable/text/sacre_bleu_score.html), and use native annotations with punctuation and capitalization in the datasets. BLEU score on [FLEURS](https://huggingface.co/datasets/google/fleurs) test set: | **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** | **De->En** | **Es->En** | **Fr->En** | |:-----------:|:---------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:| | 1.23.0 | canary-1b | 22.66 | 41.11 | 40.76 | 32.64 | 32.15 | 23.57 | BLEU score on [COVOST-v2](https://github.com/facebookresearch/covost) test set: | **Version** | **Model** | **De->En** | **Es->En** | **Fr->En** | |:-----------:|:---------:|:----------:|:----------:|:----------:| | 1.23.0 | canary-1b | 37.67 | 40.7 | 40.42 | BLEU score on [mExpresso](https://huggingface.co/facebook/seamless-expressive#mexpresso-multilingual-expresso) test set: | **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** | |:-----------:|:---------:|:----------:|:----------:|:----------:| | 1.23.0 | canary-1b | 23.84 | 35.74 | 28.29 | ## NVIDIA Riva: Deployment [NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support Although this model isn’t supported yet by Riva, the [list of supported models](https://huggingface.co/models?other=Riva) is here. Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References [1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084) [2] [Attention is all you need](https://arxiv.org/abs/1706.03762) [3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) [4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) ## Licence License to use this model is covered by the [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en#:~:text=NonCommercial%20%E2%80%94%20You%20may%20not%20use,doing%20anything%20the%20license%20permits.). By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-4.0 license.
{"language": ["en", "de", "es", "fr"], "license": "cc-by-nc-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "automatic-speech-translation", "speech", "audio", "Transformer", "FastConformer", "Conformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["librispeech_asr", "fisher_corpus", "Switchboard-1", "WSJ-0", "WSJ-1", "National-Singapore-Corpus-Part-1", "National-Singapore-Corpus-Part-6", "vctk", "voxpopuli", "europarl", "multilingual_librispeech", "mozilla-foundation/common_voice_8_0", "MLCommons/peoples_speech"], "metrics": ["wer", "bleu"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/sample2.flac"}], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "canary-1b", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (other)", "type": "librispeech_asr", "config": "other", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 2.89, "name": "Test WER"}]}, {"task": {"type": "Automatic Speech Recognition", "name": "automatic-speech-recognition"}, "dataset": {"name": "SPGI Speech", "type": "kensho/spgispeech", "config": "test", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 4.79, "name": "Test WER"}]}, {"task": {"type": "Automatic Speech Recognition", "name": "automatic-speech-recognition"}, "dataset": {"name": "Mozilla Common Voice 16.1", "type": "mozilla-foundation/common_voice_16_1", "config": "en", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 7.97, "name": "Test WER (En)"}]}, {"task": {"type": "Automatic Speech Recognition", "name": "automatic-speech-recognition"}, "dataset": {"name": "Mozilla Common Voice 16.1", "type": "mozilla-foundation/common_voice_16_1", "config": "de", "split": "test", "args": {"language": "de"}}, "metrics": [{"type": "wer", "value": 4.61, "name": "Test WER (De)"}]}, {"task": {"type": "Automatic Speech Recognition", "name": "automatic-speech-recognition"}, "dataset": {"name": "Mozilla Common Voice 16.1", "type": "mozilla-foundation/common_voice_16_1", "config": "es", "split": "test", "args": {"language": "es"}}, "metrics": [{"type": "wer", "value": 3.99, "name": "Test WER (ES)"}]}, {"task": {"type": "Automatic Speech Recognition", "name": "automatic-speech-recognition"}, "dataset": {"name": "Mozilla Common Voice 16.1", "type": "mozilla-foundation/common_voice_16_1", "config": "fr", "split": "test", "args": {"language": "fr"}}, "metrics": [{"type": "wer", "value": 6.53, "name": "Test WER (Fr)"}]}, {"task": {"type": "Automatic Speech Translation", "name": "automatic-speech-translation"}, "dataset": {"name": "FLEURS", "type": "google/fleurs", "config": "en_us", "split": "test", "args": {"language": "en-de"}}, "metrics": [{"type": "bleu", "value": 22.66, "name": "Test BLEU (En->De)"}, {"type": "bleu", "value": 41.11, "name": "Test BLEU (En->Es)"}, {"type": "bleu", "value": 40.76, "name": "Test BLEU (En->Fr)"}]}, {"task": {"type": "Automatic Speech Translation", "name": "automatic-speech-translation"}, "dataset": {"name": "FLEURS", "type": "google/fleurs", "config": "de_de", "split": "test", "args": {"language": "de-en"}}, "metrics": [{"type": "bleu", "value": 32.64, "name": "Test BLEU (De->En)"}]}, {"task": {"type": "Automatic Speech Translation", "name": "automatic-speech-translation"}, "dataset": {"name": "FLEURS", "type": "google/fleurs", "config": "es_419", "split": "test", "args": {"language": "es-en"}}, "metrics": [{"type": "bleu", "value": 32.15, "name": "Test BLEU (Es->En)"}]}, {"task": {"type": "Automatic Speech Translation", "name": "automatic-speech-translation"}, "dataset": {"name": "FLEURS", "type": "google/fleurs", "config": "fr_fr", "split": "test", "args": {"language": "fr-en"}}, "metrics": [{"type": "bleu", "value": 23.57, "name": "Test BLEU (Fr->En)"}]}, {"task": {"type": "Automatic Speech Translation", "name": "automatic-speech-translation"}, "dataset": {"name": "COVOST", "type": "covost2", "config": "de_de", "split": "test", "args": {"language": "de-en"}}, "metrics": [{"type": "bleu", "value": 37.67, "name": "Test BLEU (De->En)"}]}, {"task": {"type": "Automatic Speech Translation", "name": "automatic-speech-translation"}, "dataset": {"name": "COVOST", "type": "covost2", "config": "es_419", "split": "test", "args": {"language": "es-en"}}, "metrics": [{"type": "bleu", "value": 40.7, "name": "Test BLEU (Es->En)"}]}, {"task": {"type": "Automatic Speech Translation", "name": "automatic-speech-translation"}, "dataset": {"name": "COVOST", "type": "covost2", "config": "fr_fr", "split": "test", "args": {"language": "fr-en"}}, "metrics": [{"type": "bleu", "value": 40.42, "name": "Test BLEU (Fr->En)"}]}]}]}
automatic-speech-recognition
nvidia/canary-1b
[ "nemo", "automatic-speech-recognition", "automatic-speech-translation", "speech", "audio", "Transformer", "FastConformer", "Conformer", "pytorch", "NeMo", "hf-asr-leaderboard", "en", "de", "es", "fr", "dataset:librispeech_asr", "dataset:fisher_corpus", "dataset:Switchboard-1", "dataset:WSJ-0", "dataset:WSJ-1", "dataset:National-Singapore-Corpus-Part-1", "dataset:National-Singapore-Corpus-Part-6", "dataset:vctk", "dataset:voxpopuli", "dataset:europarl", "dataset:multilingual_librispeech", "dataset:mozilla-foundation/common_voice_8_0", "dataset:MLCommons/peoples_speech", "arxiv:2305.05084", "arxiv:1706.03762", "license:cc-by-nc-4.0", "model-index", "has_space", "region:us" ]
2024-02-07T17:20:55+00:00
[ "2305.05084", "1706.03762" ]
[ "en", "de", "es", "fr" ]
TAGS #nemo #automatic-speech-recognition #automatic-speech-translation #speech #audio #Transformer #FastConformer #Conformer #pytorch #NeMo #hf-asr-leaderboard #en #de #es #fr #dataset-librispeech_asr #dataset-fisher_corpus #dataset-Switchboard-1 #dataset-WSJ-0 #dataset-WSJ-1 #dataset-National-Singapore-Corpus-Part-1 #dataset-National-Singapore-Corpus-Part-6 #dataset-vctk #dataset-voxpopuli #dataset-europarl #dataset-multilingual_librispeech #dataset-mozilla-foundation/common_voice_8_0 #dataset-MLCommons/peoples_speech #arxiv-2305.05084 #arxiv-1706.03762 #license-cc-by-nc-4.0 #model-index #has_space #region-us
Canary 1B ========= img { display: inline; } ![Model architecture](#model-architecture) | ![Model size](#model-architecture) | ![Language](#datasets) NVIDIA NeMo Canary is a family of multi-lingual multi-tasking models that achieves state-of-the art performance on multiple benchmarks. With 1 billion parameters, Canary-1B supports automatic speech-to-text recognition (ASR) in 4 languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). Model Architecture ------------------ Canary is an encoder-decoder model with FastConformer [1] encoder and Transformer Decoder [2]. With audio features extracted from the encoder, task tokens such as '', '', '' and '' are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer from individual SentencePiece [3] tokenizers of each language, which makes it easy to scale up to more languages. The Canay-1B model has 24 encoder layers and 24 layers of decoder layers in total. NVIDIA NeMo ----------- To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed Cython and latest PyTorch version. How to Use this Model --------------------- The model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Loading the Model ### Input Format The input to the model can be a directory containing audio files, in which case the model will perform ASR on English and produces text with punctuation and capitalization: or use: Another recommended option is to use a json manifest as input, where each line in the file is a dictionary containing the following fields: and then use: or use: ### Automatic Speech-to-text Recognition (ASR) An example manifest for transcribing English audios can be: ### Automatic Speech-to-text Translation (AST) An example manifest for transcribing English audios into German text can be: ### Input This model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input. ### Output The model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization. Training -------- Canary-1B is trained using the NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs. The model can be trained using this example script and base config. The tokenizers for these models were built using the text transcripts of the train set with this script. ### Datasets The Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by Suno, and 34k hrs of in-house data. The constituents of public data are as follows. #### English (25.5k hours) * Librispeech 960 hours * Fisher Corpus * Switchboard-1 Dataset * WSJ-0 and WSJ-1 * National Speech Corpus (Part 1, Part 6) * VCTK * VoxPopuli (EN) * Europarl-ASR (EN) * Multilingual Librispeech (MLS EN) - 2,000 hour subset * Mozilla Common Voice (v7.0) * People's Speech - 12,000 hour subset * Mozilla Common Voice (v11.0) - 1,474 hour subset #### German (2.5k hours) * Mozilla Common Voice (v12.0) - 800 hour subset * Multilingual Librispeech (MLS DE) - 1,500 hour subset * VoxPopuli (DE) - 200 hr subset #### Spanish (1.4k hours) * Mozilla Common Voice (v12.0) - 395 hour subset * Multilingual Librispeech (MLS ES) - 780 hour subset * VoxPopuli (ES) - 108 hour subset * Fisher - 141 hour subset #### French (1.8k hours) * Mozilla Common Voice (v12.0) - 708 hour subset * Multilingual Librispeech (MLS FR) - 926 hour subset * VoxPopuli (FR) - 165 hour subset Performance ----------- In both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0. ### ASR Performance (w/o PnC) The ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with whisper-normalizer. WER on MCV-16.1 test set: WER on MLS test set: More details on evaluation can be found at HuggingFace ASR Leaderboard ### AST Performance We evaluate AST performance with BLEU score, and use native annotations with punctuation and capitalization in the datasets. BLEU score on FLEURS test set: BLEU score on COVOST-v2 test set: BLEU score on mExpresso test set: NVIDIA Riva: Deployment ----------------------- NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support Although this model isn’t supported yet by Riva, the list of supported models is here. Check out Riva live demo. References ---------- [1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition [2] Attention is all you need [3] Google Sentencepiece Tokenizer [4] NVIDIA NeMo Toolkit Licence ------- License to use this model is covered by the CC-BY-NC-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-4.0 license.
[ "### Loading the Model", "### Input Format\n\n\nThe input to the model can be a directory containing audio files, in which case the model will perform ASR on English and produces text with punctuation and capitalization:\n\n\nor use:\n\n\nAnother recommended option is to use a json manifest as input, where each line in the file is a dictionary containing the following fields:\n\n\nand then use:\n\n\nor use:", "### Automatic Speech-to-text Recognition (ASR)\n\n\nAn example manifest for transcribing English audios can be:", "### Automatic Speech-to-text Translation (AST)\n\n\nAn example manifest for transcribing English audios into German text can be:", "### Input\n\n\nThis model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input.", "### Output\n\n\nThe model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization.\n\n\nTraining\n--------\n\n\nCanary-1B is trained using the NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs.\nThe model can be trained using this example script and base config.\n\n\nThe tokenizers for these models were built using the text transcripts of the train set with this script.", "### Datasets\n\n\nThe Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by Suno, and 34k hrs of in-house data.\n\n\nThe constituents of public data are as follows.", "#### English (25.5k hours)\n\n\n* Librispeech 960 hours\n* Fisher Corpus\n* Switchboard-1 Dataset\n* WSJ-0 and WSJ-1\n* National Speech Corpus (Part 1, Part 6)\n* VCTK\n* VoxPopuli (EN)\n* Europarl-ASR (EN)\n* Multilingual Librispeech (MLS EN) - 2,000 hour subset\n* Mozilla Common Voice (v7.0)\n* People's Speech - 12,000 hour subset\n* Mozilla Common Voice (v11.0) - 1,474 hour subset", "#### German (2.5k hours)\n\n\n* Mozilla Common Voice (v12.0) - 800 hour subset\n* Multilingual Librispeech (MLS DE) - 1,500 hour subset\n* VoxPopuli (DE) - 200 hr subset", "#### Spanish (1.4k hours)\n\n\n* Mozilla Common Voice (v12.0) - 395 hour subset\n* Multilingual Librispeech (MLS ES) - 780 hour subset\n* VoxPopuli (ES) - 108 hour subset\n* Fisher - 141 hour subset", "#### French (1.8k hours)\n\n\n* Mozilla Common Voice (v12.0) - 708 hour subset\n* Multilingual Librispeech (MLS FR) - 926 hour subset\n* VoxPopuli (FR) - 165 hour subset\n\n\nPerformance\n-----------\n\n\nIn both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0.", "### ASR Performance (w/o PnC)\n\n\nThe ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with whisper-normalizer.\n\n\nWER on MCV-16.1 test set:\n\n\n\nWER on MLS test set:\n\n\n\nMore details on evaluation can be found at HuggingFace ASR Leaderboard", "### AST Performance\n\n\nWe evaluate AST performance with BLEU score, and use native annotations with punctuation and capitalization in the datasets.\n\n\nBLEU score on FLEURS test set:\n\n\n\nBLEU score on COVOST-v2 test set:\n\n\n\nBLEU score on mExpresso test set:\n\n\n\nNVIDIA Riva: Deployment\n-----------------------\n\n\nNVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.\nAdditionally, Riva provides:\n\n\n* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours\n* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization\n* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support\n\n\nAlthough this model isn’t supported yet by Riva, the list of supported models is here. \n\nCheck out Riva live demo.\n\n\nReferences\n----------\n\n\n[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition\n\n\n[2] Attention is all you need\n\n\n[3] Google Sentencepiece Tokenizer\n\n\n[4] NVIDIA NeMo Toolkit\n\n\nLicence\n-------\n\n\nLicense to use this model is covered by the CC-BY-NC-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-4.0 license." ]
[ "TAGS\n#nemo #automatic-speech-recognition #automatic-speech-translation #speech #audio #Transformer #FastConformer #Conformer #pytorch #NeMo #hf-asr-leaderboard #en #de #es #fr #dataset-librispeech_asr #dataset-fisher_corpus #dataset-Switchboard-1 #dataset-WSJ-0 #dataset-WSJ-1 #dataset-National-Singapore-Corpus-Part-1 #dataset-National-Singapore-Corpus-Part-6 #dataset-vctk #dataset-voxpopuli #dataset-europarl #dataset-multilingual_librispeech #dataset-mozilla-foundation/common_voice_8_0 #dataset-MLCommons/peoples_speech #arxiv-2305.05084 #arxiv-1706.03762 #license-cc-by-nc-4.0 #model-index #has_space #region-us \n", "### Loading the Model", "### Input Format\n\n\nThe input to the model can be a directory containing audio files, in which case the model will perform ASR on English and produces text with punctuation and capitalization:\n\n\nor use:\n\n\nAnother recommended option is to use a json manifest as input, where each line in the file is a dictionary containing the following fields:\n\n\nand then use:\n\n\nor use:", "### Automatic Speech-to-text Recognition (ASR)\n\n\nAn example manifest for transcribing English audios can be:", "### Automatic Speech-to-text Translation (AST)\n\n\nAn example manifest for transcribing English audios into German text can be:", "### Input\n\n\nThis model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input.", "### Output\n\n\nThe model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization.\n\n\nTraining\n--------\n\n\nCanary-1B is trained using the NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs.\nThe model can be trained using this example script and base config.\n\n\nThe tokenizers for these models were built using the text transcripts of the train set with this script.", "### Datasets\n\n\nThe Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by Suno, and 34k hrs of in-house data.\n\n\nThe constituents of public data are as follows.", "#### English (25.5k hours)\n\n\n* Librispeech 960 hours\n* Fisher Corpus\n* Switchboard-1 Dataset\n* WSJ-0 and WSJ-1\n* National Speech Corpus (Part 1, Part 6)\n* VCTK\n* VoxPopuli (EN)\n* Europarl-ASR (EN)\n* Multilingual Librispeech (MLS EN) - 2,000 hour subset\n* Mozilla Common Voice (v7.0)\n* People's Speech - 12,000 hour subset\n* Mozilla Common Voice (v11.0) - 1,474 hour subset", "#### German (2.5k hours)\n\n\n* Mozilla Common Voice (v12.0) - 800 hour subset\n* Multilingual Librispeech (MLS DE) - 1,500 hour subset\n* VoxPopuli (DE) - 200 hr subset", "#### Spanish (1.4k hours)\n\n\n* Mozilla Common Voice (v12.0) - 395 hour subset\n* Multilingual Librispeech (MLS ES) - 780 hour subset\n* VoxPopuli (ES) - 108 hour subset\n* Fisher - 141 hour subset", "#### French (1.8k hours)\n\n\n* Mozilla Common Voice (v12.0) - 708 hour subset\n* Multilingual Librispeech (MLS FR) - 926 hour subset\n* VoxPopuli (FR) - 165 hour subset\n\n\nPerformance\n-----------\n\n\nIn both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0.", "### ASR Performance (w/o PnC)\n\n\nThe ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with whisper-normalizer.\n\n\nWER on MCV-16.1 test set:\n\n\n\nWER on MLS test set:\n\n\n\nMore details on evaluation can be found at HuggingFace ASR Leaderboard", "### AST Performance\n\n\nWe evaluate AST performance with BLEU score, and use native annotations with punctuation and capitalization in the datasets.\n\n\nBLEU score on FLEURS test set:\n\n\n\nBLEU score on COVOST-v2 test set:\n\n\n\nBLEU score on mExpresso test set:\n\n\n\nNVIDIA Riva: Deployment\n-----------------------\n\n\nNVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.\nAdditionally, Riva provides:\n\n\n* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours\n* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization\n* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support\n\n\nAlthough this model isn’t supported yet by Riva, the list of supported models is here. \n\nCheck out Riva live demo.\n\n\nReferences\n----------\n\n\n[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition\n\n\n[2] Attention is all you need\n\n\n[3] Google Sentencepiece Tokenizer\n\n\n[4] NVIDIA NeMo Toolkit\n\n\nLicence\n-------\n\n\nLicense to use this model is covered by the CC-BY-NC-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-4.0 license." ]
[ 240, 5, 84, 29, 29, 36, 128, 70, 116, 54, 61, 85, 81, 352 ]
[ "passage: TAGS\n#nemo #automatic-speech-recognition #automatic-speech-translation #speech #audio #Transformer #FastConformer #Conformer #pytorch #NeMo #hf-asr-leaderboard #en #de #es #fr #dataset-librispeech_asr #dataset-fisher_corpus #dataset-Switchboard-1 #dataset-WSJ-0 #dataset-WSJ-1 #dataset-National-Singapore-Corpus-Part-1 #dataset-National-Singapore-Corpus-Part-6 #dataset-vctk #dataset-voxpopuli #dataset-europarl #dataset-multilingual_librispeech #dataset-mozilla-foundation/common_voice_8_0 #dataset-MLCommons/peoples_speech #arxiv-2305.05084 #arxiv-1706.03762 #license-cc-by-nc-4.0 #model-index #has_space #region-us \n### Loading the Model### Input Format\n\n\nThe input to the model can be a directory containing audio files, in which case the model will perform ASR on English and produces text with punctuation and capitalization:\n\n\nor use:\n\n\nAnother recommended option is to use a json manifest as input, where each line in the file is a dictionary containing the following fields:\n\n\nand then use:\n\n\nor use:### Automatic Speech-to-text Recognition (ASR)\n\n\nAn example manifest for transcribing English audios can be:### Automatic Speech-to-text Translation (AST)\n\n\nAn example manifest for transcribing English audios into German text can be:### Input\n\n\nThis model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input.", "passage: ### Output\n\n\nThe model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization.\n\n\nTraining\n--------\n\n\nCanary-1B is trained using the NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs.\nThe model can be trained using this example script and base config.\n\n\nThe tokenizers for these models were built using the text transcripts of the train set with this script.### Datasets\n\n\nThe Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by Suno, and 34k hrs of in-house data.\n\n\nThe constituents of public data are as follows.#### English (25.5k hours)\n\n\n* Librispeech 960 hours\n* Fisher Corpus\n* Switchboard-1 Dataset\n* WSJ-0 and WSJ-1\n* National Speech Corpus (Part 1, Part 6)\n* VCTK\n* VoxPopuli (EN)\n* Europarl-ASR (EN)\n* Multilingual Librispeech (MLS EN) - 2,000 hour subset\n* Mozilla Common Voice (v7.0)\n* People's Speech - 12,000 hour subset\n* Mozilla Common Voice (v11.0) - 1,474 hour subset#### German (2.5k hours)\n\n\n* Mozilla Common Voice (v12.0) - 800 hour subset\n* Multilingual Librispeech (MLS DE) - 1,500 hour subset\n* VoxPopuli (DE) - 200 hr subset#### Spanish (1.4k hours)\n\n\n* Mozilla Common Voice (v12.0) - 395 hour subset\n* Multilingual Librispeech (MLS ES) - 780 hour subset\n* VoxPopuli (ES) - 108 hour subset\n* Fisher - 141 hour subset#### French (1.8k hours)\n\n\n* Mozilla Common Voice (v12.0) - 708 hour subset\n* Multilingual Librispeech (MLS FR) - 926 hour subset\n* VoxPopuli (FR) - 165 hour subset\n\n\nPerformance\n-----------\n\n\nIn both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0.### ASR Performance (w/o PnC)\n\n\nThe ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with whisper-normalizer.\n\n\nWER on MCV-16.1 test set:\n\n\n\nWER on MLS test set:\n\n\n\nMore details on evaluation can be found at HuggingFace ASR Leaderboard" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
angela1996/intel-image-classification
[ "fastai", "region:us" ]
2024-02-07T17:21:03+00:00
[]
[]
TAGS #fastai #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 9, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
null
transformers
# 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
null
SpideyDLK/wav2vec2-large-xls-r-300m-sinhala-test2-half-data
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T17:21:51+00:00
[ "1910.09700" ]
[]
TAGS #transformers #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: - 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
[ "# 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 #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 #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. --> # vit-base-patch16-224-in21k-finetuned-mgasior-07-02-2024 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: 0.8842 - F1: 0.7717 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.731 | 0.98 | 35 | 1.6748 | 0.3386 | | 1.5196 | 1.99 | 71 | 1.4890 | 0.4173 | | 1.3727 | 2.99 | 107 | 1.2938 | 0.5276 | | 1.2194 | 4.0 | 143 | 1.1519 | 0.6457 | | 1.1538 | 4.98 | 178 | 1.0544 | 0.6693 | | 1.0379 | 5.99 | 214 | 0.9852 | 0.7165 | | 1.0232 | 6.99 | 250 | 0.9439 | 0.7323 | | 0.9586 | 8.0 | 286 | 0.9136 | 0.7480 | | 0.9374 | 8.98 | 321 | 0.8946 | 0.7638 | | 0.96 | 9.79 | 350 | 0.8842 | 0.7717 | ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["f1"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-base-patch16-224-in21k-finetuned-mgasior-07-02-2024", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "f1", "value": 0.7716535433070866, "name": "F1"}]}]}]}
image-classification
MichalGas/vit-base-patch16-224-in21k-finetuned-mgasior-07-02-2024
[ "transformers", "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" ]
2024-02-07T17:22:14+00:00
[]
[]
TAGS #transformers #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-base-patch16-224-in21k-finetuned-mgasior-07-02-2024 ======================================================= 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: 0.8842 * F1: 0.7717 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: 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: 10 ### Training results ### Framework versions * Transformers 4.36.1 * Pytorch 2.1.2+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: 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: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #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: 2e-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: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 82, 144, 4, 33 ]
[ "passage: TAGS\n#transformers #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: 2e-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: 10### Training results### Framework versions\n\n\n* Transformers 4.36.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
pamunarr/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:24:06+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
null
transformers
# 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
delli/mistral-7b-address-validator
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T17:25:39+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. - 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
[ "# 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 #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 #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
diffusers
# 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. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> 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 [More Information Needed] #### 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:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "diffusers"}
null
a-r-r-o-w/motionctrl-svd
[ "diffusers", "safetensors", "arxiv:1910.09700", "diffusers:StableVideoDiffusionPipeline", "region:us" ]
2024-02-07T17:26:35+00:00
[ "1910.09700" ]
[]
TAGS #diffusers #safetensors #arxiv-1910.09700 #diffusers-StableVideoDiffusionPipeline #region-us
# Model Card for Model ID ## Model Details ### Model Description This is the model card of a diffusers 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
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers 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#diffusers #safetensors #arxiv-1910.09700 #diffusers-StableVideoDiffusionPipeline #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers 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#diffusers #safetensors #arxiv-1910.09700 #diffusers-StableVideoDiffusionPipeline #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a diffusers 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
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
luis56125/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:27:30+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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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. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (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 <!-- This should link to a Dataset 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. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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 [More Information Needed] #### 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:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
{"library_name": "peft", "base_model": "Trelis/Llama-2-7b-chat-hf-sharded-bf16"}
null
SolaireOfTheSun/Llama-2-7b-chat-hf-sharded-bf16-feinabgestimmt-adapters-2
[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "region:us" ]
2024-02-07T17:27:54+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #region-us
# Model Card for Model ID ## Model Details ### Model Description - 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\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 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #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 0.8.2" ]
[ 43, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #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 0.8.2" ]
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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. --> # bart-noised-with-gcd-dist-0.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown 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: 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_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-noised-with-gcd-dist-0.1", "results": []}]}
text2text-generation
gayanin/bart-noised-with-gcd-dist-0.1
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T17:28:08+00:00
[]
[]
TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# bart-noised-with-gcd-dist-0.1 This model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# bart-noised-with-gcd-dist-0.1\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bart-noised-with-gcd-dist-0.1\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 64, 38, 6, 12, 8, 3, 118, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bart-noised-with-gcd-dist-0.1\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+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-noised-with-gcd-dist-0.2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown 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: 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_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-noised-with-gcd-dist-0.2", "results": []}]}
text2text-generation
gayanin/bart-noised-with-gcd-dist-0.2
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T17:28:55+00:00
[]
[]
TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# bart-noised-with-gcd-dist-0.2 This model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# bart-noised-with-gcd-dist-0.2\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bart-noised-with-gcd-dist-0.2\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 64, 38, 6, 12, 8, 3, 118, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bart-noised-with-gcd-dist-0.2\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+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-noised-with-gcd-dist-0.3 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown 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: 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_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-noised-with-gcd-dist-0.3", "results": []}]}
text2text-generation
gayanin/bart-noised-with-gcd-dist-0.3
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T17:29:08+00:00
[]
[]
TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# bart-noised-with-gcd-dist-0.3 This model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# bart-noised-with-gcd-dist-0.3\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bart-noised-with-gcd-dist-0.3\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 64, 38, 6, 12, 8, 3, 118, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bart-noised-with-gcd-dist-0.3\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown 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: 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_steps: 10\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
luisvarona/intel-image-classification
[ "fastai", "region:us" ]
2024-02-07T17:29:27+00:00
[]
[]
TAGS #fastai #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 9, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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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-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown 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: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - 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
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "results", "results": []}]}
null
prasadkanche/prasad_Mistral_FT_Model
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
2024-02-07T17:29:57+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
# results This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - 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
[ "# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 4\n- total_train_batch_size: 32\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: 2", "### Training results", "### Framework versions\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" ]
[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 4\n- total_train_batch_size: 32\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: 2", "### Training results", "### Framework versions\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" ]
[ 55, 31, 6, 12, 8, 3, 127, 4, 44 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n# results\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 4\n- total_train_batch_size: 32\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: 2### Training results### Framework versions\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
transformers
# 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
Tommidi/st_vit_trained-1epoch-ucf101-subset
[ "transformers", "safetensors", "st_vit", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-07T17:30:24+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #st_vit #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: - 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
[ "# 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 #st_vit #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 #st_vit #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
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
valintea/primer-modelo
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:30:54+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
null
transformers
# original model repo : 📖 this is a cutomized version of the following model [aaronespasa/deepfake-detection-resnetinceptionv1](https://huggingface.co/aaronespasa/deepfake-detection-resnetinceptionv1) # how to use ```python from transformers import pipeline pipe = pipeline(model="not-lain/deepfake",trust_remote_code=True) pipe.predict("img_path.jpg") ``` ```python >> {"confidences":confidences,"face_with_mask": face_with_mask} ``` # dependencies to install related dependencies simply use the command ``` !wget https://huggingface.co/not-lain/deepfake/resolve/main/requirements.txt && pip install -r requirements.txt ```
{"license": "apache-2.0", "library_name": "transformers", "base_model": "aaronespasa/deepfake-detection-resnetinceptionv1"}
image-classification
wyyadd/fork-detect-fake
[ "transformers", "pytorch", "safetensors", "ResNet", "image-classification", "custom_code", "base_model:aaronespasa/deepfake-detection-resnetinceptionv1", "license:apache-2.0", "autotrain_compatible", "region:us" ]
2024-02-07T17:31:03+00:00
[]
[]
TAGS #transformers #pytorch #safetensors #ResNet #image-classification #custom_code #base_model-aaronespasa/deepfake-detection-resnetinceptionv1 #license-apache-2.0 #autotrain_compatible #region-us
# original model repo : this is a cutomized version of the following model aaronespasa/deepfake-detection-resnetinceptionv1 # how to use # dependencies to install related dependencies simply use the command
[ "# original model repo : \n this is a cutomized version of the following model aaronespasa/deepfake-detection-resnetinceptionv1", "# how to use", "# dependencies\nto install related dependencies simply use the command" ]
[ "TAGS\n#transformers #pytorch #safetensors #ResNet #image-classification #custom_code #base_model-aaronespasa/deepfake-detection-resnetinceptionv1 #license-apache-2.0 #autotrain_compatible #region-us \n", "# original model repo : \n this is a cutomized version of the following model aaronespasa/deepfake-detection-resnetinceptionv1", "# how to use", "# dependencies\nto install related dependencies simply use the command" ]
[ 70, 34, 4, 12 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #ResNet #image-classification #custom_code #base_model-aaronespasa/deepfake-detection-resnetinceptionv1 #license-apache-2.0 #autotrain_compatible #region-us \n# original model repo : \n this is a cutomized version of the following model aaronespasa/deepfake-detection-resnetinceptionv1# how to use# dependencies\nto install related dependencies simply use the command" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
jonruida/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:31:23+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
null
transformers
Machine-generated text-detection by fine-tuning of language models === This project is related to a bachelor's thesis with the title "*Turning Poachers into Gamekeepers: Detecting Machine-Generated Text in Academia using Large Language Models*" (see [here](https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/3078096)) written by *Nicolai Thorer Sivesind* and *Andreas Bentzen Winje* at the *Department of Computer Science* at the *Norwegian University of Science and Technology*. It contains text classification models trained to distinguish human-written text from text generated by language models like ChatGPT and GPT-3. The best models were able to achieve an accuracy of 100% on real and *GPT-3*-generated wikipedia articles (4500 samples), and an accuracy of 98.4% on real and *ChatGPT*-generated research abstracts (3000 samples). The dataset card for the dataset that was created in relation to this project can be found [here](https://huggingface.co/datasets/NicolaiSivesind/human-vs-machine). **NOTE**: the hosted inference on this site only works for the RoBERTa-models, and not for the Bloomz-models. The Bloomz-models otherwise can produce wrong predictions when not explicitly providing the attention mask from the tokenizer to the model for inference. To be sure, the [pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines)-library seems to produce the most consistent results. ## Fine-tuned detectors This project includes 12 fine-tuned models based on the RoBERTa-base model, and three sizes of the bloomz-models. | Base-model | RoBERTa-base | Bloomz-560m | Bloomz-1b7 | Bloomz-3b | |------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------| | Wiki | [roberta-wiki](https://huggingface.co/andreas122001/roberta-wiki-detector) | [Bloomz-560m-wiki](https://huggingface.co/andreas122001/bloomz-560m-wiki-detector) | [Bloomz-1b7-wiki](https://huggingface.co/andreas122001/bloomz-1b7-wiki-detector) | [Bloomz-3b-wiki](https://huggingface.co/andreas122001/bloomz-3b-wiki-detector) | | Academic | [roberta-academic](https://huggingface.co/andreas122001/roberta-academic-detector) | [Bloomz-560m-academic](https://huggingface.co/andreas122001/bloomz-560m-academic-detector) | [Bloomz-1b7-academic](https://huggingface.co/andreas122001/bloomz-1b7-academic-detector) | [Bloomz-3b-academic](https://huggingface.co/andreas122001/bloomz-3b-academic-detector) | | Mixed | [roberta-mixed](https://huggingface.co/andreas122001/roberta-mixed-detector) | [Bloomz-560m-mixed](https://huggingface.co/andreas122001/bloomz-560m-mixed-detector) | [Bloomz-1b7-mixed](https://huggingface.co/andreas122001/bloomz-1b7-mixed-detector) | [Bloomz-3b-mixed](https://huggingface.co/andreas122001/bloomz-3b-mixed-detector) | ### Datasets The models were trained on selections from the [GPT-wiki-intros]() and [ChatGPT-Research-Abstracts](), and are separated into three types, **wiki**-detectors, **academic**-detectors and **mixed**-detectors, respectively. - **Wiki-detectors**: - Trained on 30'000 datapoints (10%) of GPT-wiki-intros. - Best model (in-domain) is Bloomz-3b-wiki, with an accuracy of 100%. - **Academic-detectors**: - Trained on 20'000 datapoints (100%) of ChatGPT-Research-Abstracts. - Best model (in-domain) is Bloomz-3b-academic, with an accuracy of 98.4% - **Mixed-detectors**: - Trained on 15'000 datapoints (5%) of GPT-wiki-intros and 10'000 datapoints (50%) of ChatGPT-Research-Abstracts. - Best model (in-domain) is RoBERTa-mixed, with an F1-score of 99.3%. ### Hyperparameters All models were trained using the same hyperparameters: ```python { "num_train_epochs": 1, "adam_beta1": 0.9, "adam_beta2": 0.999, "batch_size": 8, "adam_epsilon": 1e-08 "optim": "adamw_torch" # the optimizer (AdamW) "learning_rate": 5e-05, # (LR) "lr_scheduler_type": "linear", # scheduler type for LR "seed": 42, # seed for PyTorch RNG-generator. } ``` ### Metrics Metrics can be found at https://wandb.ai/idatt2900-072/IDATT2900-072. In-domain performance of wiki-detectors: | Base model | Accuracy | Precision | Recall | F1-score | |-------------|----------|-----------|--------|----------| | Bloomz-560m | 0.973 | *1.000 | 0.945 | 0.972 | | Bloomz-1b7 | 0.972 | *1.000 | 0.945 | 0.972 | | Bloomz-3b | *1.000 | *1.000 | *1.000 | *1.000 | | RoBERTa | 0.998 | 0.999 | 0.997 | 0.998 | In-domain peformance of academic-detectors: | Base model | Accuracy | Precision | Recall | F1-score | |-------------|----------|-----------|--------|----------| | Bloomz-560m | 0.964 | 0.963 | 0.965 | 0.964 | | Bloomz-1b7 | 0.946 | 0.941 | 0.951 | 0.946 | | Bloomz-3b | *0.984 | *0.983 | 0.985 | *0.984 | | RoBERTa | 0.982 | 0.968 | *0.997 | 0.982 | F1-scores of the mixed-detectors on all three datasets: | Base model | Mixed | Wiki | CRA | |-------------|--------|--------|--------| | Bloomz-560m | 0.948 | 0.972 | *0.848 | | Bloomz-1b7 | 0.929 | 0.964 | 0.816 | | Bloomz-3b | 0.988 | 0.996 | 0.772 | | RoBERTa | *0.993 | *0.997 | 0.829 | ## Credits - [GPT-wiki-intro](https://huggingface.co/datasets/aadityaubhat/GPT-wiki-intro), by Aaditya Bhat - [arxiv-abstracts-2021](https://huggingface.co/datasets/gfissore/arxiv-abstracts-2021), by Giancarlo - [Bloomz](bigscience/bloomz), by BigScience - [RoBERTa](https://huggingface.co/roberta-base), by Liu et. al. ## Citation Please use the following citation: ``` @misc {sivesind_2023, author = { {Nicolai Thorer Sivesind} and {Andreas Bentzen Winje} }, title = { Machine-generated text-detection by fine-tuning of language models }, url = { https://huggingface.co/andreas122001/roberta-academic-detector } year = 2023, publisher = { Hugging Face } } ```
{"language": ["en"], "license": "openrail", "tags": ["mgt-detection", "ai-detection"], "datasets": ["NicolaiSivesind/human-vs-machine", "gfissore/arxiv-abstracts-2021"], "widget": [{"text": "I am totally a human, trust me bro.", "example_title": "default"}, {"text": "In Finnish folklore, all places and things, and also human beings, have a haltija (a genius, guardian spirit) of their own. One such haltija is called eti\u00e4inen\u2014an image, doppelg\u00e4nger, or just an impression that goes ahead of a person, doing things the person in question later does. For example, people waiting at home might hear the door close or even see a shadow or a silhouette, only to realize that no one has yet arrived. Eti\u00e4inen can also refer to some kind of a feeling that something is going to happen. Sometimes it could, for example, warn of a bad year coming. In modern Finnish, the term has detached from its shamanistic origins and refers to premonition. Unlike clairvoyance, divination, and similar practices, eti\u00e4iset (plural) are spontaneous and can't be induced. Quite the opposite, they may be unwanted and cause anxiety, like ghosts. Eti\u00e4iset need not be too dramatic and may concern everyday events, although ones related to e.g. deaths are common. As these phenomena are still reported today, they can be considered a living tradition, as a way to explain the psychological experience of premonition.", "example_title": "real wikipedia"}, {"text": "In Finnish folklore, all places and things, animate or inanimate, have a spirit or \"eti\u00e4inen\" that lives there. Eti\u00e4inen can manifest in many forms, but is usually described as a kind, elderly woman with white hair. She is the guardian of natural places and often helps people in need. Eti\u00e4inen has been a part of Finnish culture for centuries and is still widely believed in today. Folklorists study eti\u00e4inen to understand Finnish traditions and how they have changed over time.", "example_title": "generated wikipedia"}, {"text": "This paper presents a novel framework for sparsity-certifying graph decompositions, which are important tools in various areas of computer science, including algorithm design, complexity theory, and optimization. Our approach is based on the concept of \"cut sparsifiers,\" which are sparse graphs that preserve the cut structure of the original graph up to a certain error bound. We show that cut sparsifiers can be efficiently constructed using a combination of spectral techniques and random sampling, and we use them to develop new algorithms for decomposing graphs into sparse subgraphs.", "example_title": "from ChatGPT"}, {"text": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.", "example_title": "GPT-3 paper"}], "pipeline_tag": "text-classification"}
text-classification
hossamdaoud/bloomz-1b7-academic-detector
[ "transformers", "pytorch", "safetensors", "bloom", "text-classification", "mgt-detection", "ai-detection", "en", "dataset:NicolaiSivesind/human-vs-machine", "dataset:gfissore/arxiv-abstracts-2021", "license:openrail", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-07T17:36:09+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #bloom #text-classification #mgt-detection #ai-detection #en #dataset-NicolaiSivesind/human-vs-machine #dataset-gfissore/arxiv-abstracts-2021 #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Machine-generated text-detection by fine-tuning of language models ================================================================== This project is related to a bachelor's thesis with the title "*Turning Poachers into Gamekeepers: Detecting Machine-Generated Text in Academia using Large Language Models*" (see here) written by *Nicolai Thorer Sivesind* and *Andreas Bentzen Winje* at the *Department of Computer Science* at the *Norwegian University of Science and Technology*. It contains text classification models trained to distinguish human-written text from text generated by language models like ChatGPT and GPT-3. The best models were able to achieve an accuracy of 100% on real and *GPT-3*-generated wikipedia articles (4500 samples), and an accuracy of 98.4% on real and *ChatGPT*-generated research abstracts (3000 samples). The dataset card for the dataset that was created in relation to this project can be found here. NOTE: the hosted inference on this site only works for the RoBERTa-models, and not for the Bloomz-models. The Bloomz-models otherwise can produce wrong predictions when not explicitly providing the attention mask from the tokenizer to the model for inference. To be sure, the pipeline-library seems to produce the most consistent results. Fine-tuned detectors -------------------- This project includes 12 fine-tuned models based on the RoBERTa-base model, and three sizes of the bloomz-models. ### Datasets The models were trained on selections from the GPT-wiki-intros and ChatGPT-Research-Abstracts, and are separated into three types, wiki-detectors, academic-detectors and mixed-detectors, respectively. * Wiki-detectors: + Trained on 30'000 datapoints (10%) of GPT-wiki-intros. + Best model (in-domain) is Bloomz-3b-wiki, with an accuracy of 100%. * Academic-detectors: + Trained on 20'000 datapoints (100%) of ChatGPT-Research-Abstracts. + Best model (in-domain) is Bloomz-3b-academic, with an accuracy of 98.4% * Mixed-detectors: + Trained on 15'000 datapoints (5%) of GPT-wiki-intros and 10'000 datapoints (50%) of ChatGPT-Research-Abstracts. + Best model (in-domain) is RoBERTa-mixed, with an F1-score of 99.3%. ### Hyperparameters All models were trained using the same hyperparameters: ### Metrics Metrics can be found at URL In-domain performance of wiki-detectors: In-domain peformance of academic-detectors: F1-scores of the mixed-detectors on all three datasets: Credits ------- * GPT-wiki-intro, by Aaditya Bhat * arxiv-abstracts-2021, by Giancarlo * Bloomz, by BigScience * RoBERTa, by Liu et. al. Please use the following citation:
[ "### Datasets\n\n\nThe models were trained on selections from the GPT-wiki-intros and ChatGPT-Research-Abstracts, and are separated into three types, wiki-detectors, academic-detectors and mixed-detectors, respectively.\n\n\n* Wiki-detectors:\n\t+ Trained on 30'000 datapoints (10%) of GPT-wiki-intros.\n\t+ Best model (in-domain) is Bloomz-3b-wiki, with an accuracy of 100%.\n* Academic-detectors:\n\t+ Trained on 20'000 datapoints (100%) of ChatGPT-Research-Abstracts.\n\t+ Best model (in-domain) is Bloomz-3b-academic, with an accuracy of 98.4%\n* Mixed-detectors:\n\t+ Trained on 15'000 datapoints (5%) of GPT-wiki-intros and 10'000 datapoints (50%) of ChatGPT-Research-Abstracts.\n\t+ Best model (in-domain) is RoBERTa-mixed, with an F1-score of 99.3%.", "### Hyperparameters\n\n\nAll models were trained using the same hyperparameters:", "### Metrics\n\n\nMetrics can be found at URL\n\n\nIn-domain performance of wiki-detectors:\n\n\n\nIn-domain peformance of academic-detectors:\n\n\n\nF1-scores of the mixed-detectors on all three datasets:\n\n\n\nCredits\n-------\n\n\n* GPT-wiki-intro, by Aaditya Bhat\n* arxiv-abstracts-2021, by Giancarlo\n* Bloomz, by BigScience\n* RoBERTa, by Liu et. al.\n\n\nPlease use the following citation:" ]
[ "TAGS\n#transformers #pytorch #safetensors #bloom #text-classification #mgt-detection #ai-detection #en #dataset-NicolaiSivesind/human-vs-machine #dataset-gfissore/arxiv-abstracts-2021 #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Datasets\n\n\nThe models were trained on selections from the GPT-wiki-intros and ChatGPT-Research-Abstracts, and are separated into three types, wiki-detectors, academic-detectors and mixed-detectors, respectively.\n\n\n* Wiki-detectors:\n\t+ Trained on 30'000 datapoints (10%) of GPT-wiki-intros.\n\t+ Best model (in-domain) is Bloomz-3b-wiki, with an accuracy of 100%.\n* Academic-detectors:\n\t+ Trained on 20'000 datapoints (100%) of ChatGPT-Research-Abstracts.\n\t+ Best model (in-domain) is Bloomz-3b-academic, with an accuracy of 98.4%\n* Mixed-detectors:\n\t+ Trained on 15'000 datapoints (5%) of GPT-wiki-intros and 10'000 datapoints (50%) of ChatGPT-Research-Abstracts.\n\t+ Best model (in-domain) is RoBERTa-mixed, with an F1-score of 99.3%.", "### Hyperparameters\n\n\nAll models were trained using the same hyperparameters:", "### Metrics\n\n\nMetrics can be found at URL\n\n\nIn-domain performance of wiki-detectors:\n\n\n\nIn-domain peformance of academic-detectors:\n\n\n\nF1-scores of the mixed-detectors on all three datasets:\n\n\n\nCredits\n-------\n\n\n* GPT-wiki-intro, by Aaditya Bhat\n* arxiv-abstracts-2021, by Giancarlo\n* Bloomz, by BigScience\n* RoBERTa, by Liu et. al.\n\n\nPlease use the following citation:" ]
[ 104, 259, 19, 116 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bloom #text-classification #mgt-detection #ai-detection #en #dataset-NicolaiSivesind/human-vs-machine #dataset-gfissore/arxiv-abstracts-2021 #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Datasets\n\n\nThe models were trained on selections from the GPT-wiki-intros and ChatGPT-Research-Abstracts, and are separated into three types, wiki-detectors, academic-detectors and mixed-detectors, respectively.\n\n\n* Wiki-detectors:\n\t+ Trained on 30'000 datapoints (10%) of GPT-wiki-intros.\n\t+ Best model (in-domain) is Bloomz-3b-wiki, with an accuracy of 100%.\n* Academic-detectors:\n\t+ Trained on 20'000 datapoints (100%) of ChatGPT-Research-Abstracts.\n\t+ Best model (in-domain) is Bloomz-3b-academic, with an accuracy of 98.4%\n* Mixed-detectors:\n\t+ Trained on 15'000 datapoints (5%) of GPT-wiki-intros and 10'000 datapoints (50%) of ChatGPT-Research-Abstracts.\n\t+ Best model (in-domain) is RoBERTa-mixed, with an F1-score of 99.3%.### Hyperparameters\n\n\nAll models were trained using the same hyperparameters:### Metrics\n\n\nMetrics can be found at URL\n\n\nIn-domain performance of wiki-detectors:\n\n\n\nIn-domain peformance of academic-detectors:\n\n\n\nF1-scores of the mixed-detectors on all three datasets:\n\n\n\nCredits\n-------\n\n\n* GPT-wiki-intro, by Aaditya Bhat\n* arxiv-abstracts-2021, by Giancarlo\n* Bloomz, by BigScience\n* RoBERTa, by Liu et. al.\n\n\nPlease use the following citation:" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
Hitomiblood/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:37:52+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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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-7B-Instruct-v0.2-atc This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.1517 ## 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: 3 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.13 | 0.04 | 100 | 0.1517 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-atc", "results": []}]}
null
atlaspilotpuppy/Mistral-7B-Instruct-v0.2-atc
[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]
2024-02-07T17:38:29+00:00
[]
[]
TAGS #peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us
Mistral-7B-Instruct-v0.2-atc ============================ This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the generator dataset. It achieves the following results on the evaluation set: * Loss: 0.1517 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: 3 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.03 * training\_steps: 100 ### Training results ### Framework versions * PEFT 0.8.2 * 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: 3\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: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 100", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #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: 3\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: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 100", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 60, 117, 4, 39 ]
[ "passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #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: 3\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: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 100### Training results### Framework versions\n\n\n* PEFT 0.8.2\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
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. --> # train_2024-02-07-03-18-19 This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the openorca 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.5 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "base_model": "Qwen/Qwen1.5-7B", "model-index": [{"name": "train_2024-02-07-03-18-19", "results": []}]}
null
Crystalcareai/CrystalQwen-1.5-7B-Alpha-Lora
[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "license:other", "region:us" ]
2024-02-07T17:39:24+00:00
[]
[]
TAGS #peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-Qwen/Qwen1.5-7B #license-other #region-us
# train_2024-02-07-03-18-19 This model is a fine-tuned version of Qwen/Qwen1.5-7B on the openorca 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.5 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# train_2024-02-07-03-18-19\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-7B on the openorca 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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 1.5", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-Qwen/Qwen1.5-7B #license-other #region-us \n", "# train_2024-02-07-03-18-19\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-7B on the openorca 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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 1.5", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 48, 36, 6, 12, 8, 3, 114, 4, 39 ]
[ "passage: TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-Qwen/Qwen1.5-7B #license-other #region-us \n# train_2024-02-07-03-18-19\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-7B on the openorca 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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 1.5### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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# **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": "cartPole8", "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
turgutburak01/cartPole8
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2024-02-07T17:39:35+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
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. --> # mixtral-nek-finetune_0.3_all_data_4_lines This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8051 ## 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: 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: 1 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8456 | 0.09 | 1000 | 0.8573 | | 0.838 | 0.18 | 2000 | 0.8426 | | 0.8373 | 0.27 | 3000 | 0.8341 | | 0.8168 | 0.36 | 4000 | 0.8274 | | 0.8163 | 0.44 | 5000 | 0.8222 | | 0.8079 | 0.53 | 6000 | 0.8181 | | 0.8089 | 0.62 | 7000 | 0.8140 | | 0.8119 | 0.71 | 8000 | 0.8108 | | 0.8007 | 0.8 | 9000 | 0.8082 | | 0.809 | 0.89 | 10000 | 0.8062 | | 0.8084 | 0.98 | 11000 | 0.8051 | ### Framework versions - PEFT 0.8.2.dev0 - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mixtral-8x7B-Instruct-v0.1", "model-index": [{"name": "mixtral-nek-finetune_0.3_all_data_4_lines", "results": []}]}
null
POLYQ/mixtral-nek-finetune_0.3_all_data_4_lines
[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]
2024-02-07T17:40:12+00:00
[]
[]
TAGS #peft #safetensors #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us
mixtral-nek-finetune\_0.3\_all\_data\_4\_lines ============================================== This model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.8051 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: 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: 1 * num\_epochs: 1.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.8.2.dev0 * Transformers 4.38.0.dev0 * Pytorch 2.1.2+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: 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: 1\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2.dev0\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-Instruct-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: 5e-05\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: 1\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2.dev0\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 51, 159, 4, 47 ]
[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-Instruct-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: 5e-05\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: 1\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2.dev0\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
crrodrvi/Practica1
[ "fastai", "region:us" ]
2024-02-07T17:40:29+00:00
[]
[]
TAGS #fastai #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 9, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
angela1996/Practica1
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:40:55+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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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 Paquique -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 Paquique -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 Paquique ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('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": "548.00 +/- 276.52", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Paquique/dqn-SpaceInvadersNoFrameskip-v4
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-07T17:40:57+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
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_RTSplit0208_4 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: 0.0286 - Wer: 0.2110 - Cer: 0.2340 ## 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: 5.5e-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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.5617 | 1.0 | 120 | 3.4716 | 1.0 | 0.9474 | | 1.5803 | 2.0 | 240 | 1.3647 | 1.0 | 0.7373 | | 0.9177 | 3.0 | 360 | 0.7341 | 0.8214 | 0.6103 | | 0.5589 | 4.0 | 480 | 0.4265 | 0.7531 | 0.5284 | | 0.3676 | 5.0 | 600 | 0.2065 | 0.4244 | 0.3298 | | 0.2415 | 6.0 | 720 | 0.0940 | 0.2638 | 0.2046 | | 0.2053 | 7.0 | 840 | 0.0500 | 0.2361 | 0.1960 | | 0.1712 | 8.0 | 960 | 0.0286 | 0.2110 | 0.2340 | ### 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_RTSplit0208_4", "results": []}]}
automatic-speech-recognition
tndklab/wav2vec_RTSplit0208_4
[ "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" ]
2024-02-07T17:41:34+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\_RTSplit0208\_4 ======================= 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: 0.0286 * Wer: 0.2110 * Cer: 0.2340 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: 5.5e-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: 8 ### 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: 5.5e-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: 8", "### 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: 5.5e-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: 8", "### 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: 5.5e-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: 8### 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
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
islasher/intel-image-classification
[ "fastai", "has_space", "region:us" ]
2024-02-07T17:51:13+00:00
[]
[]
TAGS #fastai #has_space #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 13, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1135 - Accuracy: 0.9703 ## 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: 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1503 | 1.0 | 57 | 0.2204 | 0.9381 | | 0.1349 | 2.0 | 114 | 0.1394 | 0.9567 | | 0.0552 | 3.0 | 171 | 0.1430 | 0.9678 | | 0.0722 | 4.0 | 228 | 0.1568 | 0.9629 | | 0.0523 | 5.0 | 285 | 0.1135 | 0.9703 | ### 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": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9702970297029703, "name": "Accuracy"}]}]}]}
image-classification
jvbjkbjkbfjis/swin-tiny-patch4-window7-224-finetuned-eurosat
[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-07T17:51:48+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
swin-tiny-patch4-window7-224-finetuned-eurosat ============================================== This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.1135 * Accuracy: 0.9703 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: 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 4.35.2 * 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: 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 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #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: 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 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 88, 144, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #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: 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 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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