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https://github.com/ivylee/model-cards-and-datasheets
A collection of machine learning model cards and datasheets.
https://github.com/ivylee/model-cards-and-datasheets
Last synced: 13 days ago
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A collection of machine learning model cards and datasheets.
- Host: GitHub
- URL: https://github.com/ivylee/model-cards-and-datasheets
- Owner: ivylee
- License: mit
- Created: 2020-01-20T23:51:50.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-06-21T14:15:23.000Z (5 months ago)
- Last Synced: 2024-07-31T21:53:10.222Z (3 months ago)
- Homepage:
- Size: 97.7 KB
- Stars: 67
- Watchers: 8
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-machine-learning-interpretability - model-cards-and-datasheets - cards-and-datasheets?style=social) (Community and Official Guidance Resources / Community Frameworks and Guidance)
README
# model-cards-and-datasheets
A collection of machine learning model cards and datasheets.## Model Cards
* [Model Cards for Model Reporting][1]
* [DAG Card is the new Model Card][54]
* [Face Detection, Google Cloud Vision API][2]
* [Object Detection, Google Cloud Vision API][3]
* [Toxicity, Google Conversation AI][4]
* [GPT-2, Open AI][5]
* [GPT-3, Open AI][39]
* [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models][10]
* ["The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning][11]
* [MediaPipe BlazeFace][12]
* [MediaPipe Face Mesh][13]
* [MediaPipe Hands][14]
* [MediaPipe Hair Segmentation][15]
* [MediaPipe KNIFT (Keypoint Neural Invariant Feature Transform)][16]
* [MediaPipe Meet Segmentation][31]
* [MediaPipe Iris][32]
* [MediaPipe BlazePose Upper-body][33]
* [MediaPipe Objectron][34]
* [PeopleNet, Nvidia][23]
* [TrafficCamNet, Nvidia][24]
* [DashCamNet, Nvidia][25]
* [FaceDetect-IR, Nvidia][26]
* [VehicleTypeNet, Nvidia][27]
* [VehicleMakeNet, Nvidia][28]
* [CLIP, OpenAI][35]
* [DALL-E, OpenAI][36]
* [DynaSent][37]
* [CTRL - A Conditional Transformer Language Model for Controllable Generation, Salesforce][40]
* [Einstein OCR Model Card, Salesforce][41]
* [MLKit Selfie Segmentation, Google ML Kit][42]
* [Detector for CTRL, Salesforce][43]
* [AI Economist: Foundation, Salesforce][44]
* [Einstein Messaging Insights, Salesforce][45]
* [Einstein Engagement Frequency, Salesforce][46]
* [Einstein Engagement Scoring for Mobile, Salesforce][47]
* [Einstein Call Coaching Language Model, Salesforce][48]
* [Detect Sentiment, Salesforce][49]
* [Einstein Opportunity Scoring, Salesforce][50]
* [Generative Pretrained Transformer, Cohere][51]
* [Masked Language Model, Cohere][52]
* [Language Agnostic Link-Based Article Topic Model Card][55]
* [InstructGPT: Training Language Models to Follow Instructions with Human Feedback, OpenAI][56]
* [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models, OpenAI][57]
* [Diffusion Models Beat GANS on Image Synthesis, Open AI][58]
* [Learning to Summarize from Human Feedback][59]
* [Scaling Language Models: Methods, Analysis & Insights from Training Gopher, DeepMind][60]
* [DALL-E Mini, Hugging Face][62]
* [Serverless Prediction of Peptide Properties with Recurrent Neural Networks][63]
* [STT En Conformer-CTC Large, Nvidia][64]
* [Towards Climate Awareness in NLP Research][65]
* [OPT: Open Pre-trained Transformer Language Models, Meta AI][66]
* [DALL-E 2, OpenAI][67]
* [PaLM: Scaling Language Modeling with Pathways, Google][68]
* [Whisper, OpenAI][70]
* [GALACTICA, Meta][71]
* [Stable Diffusion, CompVis][72]
* [InstructGPT, OpenAI][73]
* [Gopher, DeepMind][75]
* [Extract Skills, Nesta][76]
* [Skills to Taxonomy Mapping, Nesta][77]
* [GPT-4, OpenAI][85]
* [GPT-4V, OpenAI][78]
* [Claude, Anthropic][79]
* [Llama, Meta][84]
* [Llama 2, Meta][81]
* [AudioGen, Meta][82]
* [MusicGen, Meta][83]
* [Grok-1, xAI][86]
* [Gemma, Google][87]
* [CodeGemma, Google][95]
* [PaliGemma, Google][94]
* [RecurrentGemma, Google][96]
* [Claude 3, Anthropic][88]
* [GOODY-2][89]
* [Gemini Ultra, Google][92]
* [Gemini 1.5, Google][93]
* [Llama 3, Meta][91]
* [Claude 3.5 Sonnet, Anthropic][90]
* [Perspective API, Google][97]
* [MedLM, Google][98]
* [PaLM2, Google][99]## Datasheets
* [Datasheets for Datasets][8]
* [Open Images Extended - Crowdsourced][9]
* [Crowdsourced high-quality Chilean Spanish (es-cl) multi-speaker speech dataset][18]
* [Crowdsourced high-quality Colombian Spanish (es-co) multi-speaker speech dataset][19]
* [Crowdsourced high-quality Argentinian Spanish (es-ar) speech multi-speaker dataset][20]
* [Crowdsourced high-quality Peruvian Spanish (es-pe) multi-speaker speech dataset][21]
* [Crowdsourced high-quality Venezuelan Spanish (es-ve) multi-speaker speech dataset][22]
* [DynaSent][38]## Tools
* [Model Card Toolkit][17]
* [VerifyML][53]
* [The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design][69]
* [Model card template][74]
* [Amazon SageMaker Model Cards][80]## Tutorials
* [How to create and deploy a model card in the cloud with Scikit-Learn][29]
* [Using Model Card Toolkit for TF Model Transparency][30]
* [Create a Custom Model Card with Google’s Model Card Toolkit][61][1]: https://arxiv.org/abs/1810.03993
[2]: https://modelcards.withgoogle.com/face-detection
[3]: https://modelcards.withgoogle.com/object-detection
[4]: https://github.com/conversationai/perspectiveapi/blob/main/model-cards/English/toxicity.md
[5]: https://github.com/openai/gpt-2/blob/master/model_card.md
[8]: https://arxiv.org/abs/1803.09010
[9]: https://research.google/static/documents/datasets/open-images-extended-crowdsourced.pdf
[10]: https://drive.google.com/file/d/1fV7FsmunjDuRrsn4KYf2Efwp0FNBtcR4/view
[11]: https://arxiv.org/ftp/arxiv/papers/1911/1911.08089.pdf
[12]: https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view
[13]: https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view
[14]: https://drive.google.com/file/d/14pjkgLl3t3jiTiCFuvWGB-uAX_aVZOS5/view
[15]: https://drive.google.com/file/d/1lPwJ8BD_-3UUor4LayQ0xpa_RIC_hoRh/view
[16]: https://drive.google.com/file/d/1RCdA83a8JDV3ZPS-mtpsV8mTj3r0F9s-/view
[17]: https://github.com/tensorflow/model-card-toolkit
[18]: https://research.google/tools/datasets/chilean-spanish-tts/
[19]: https://research.google/tools/datasets/colombian-spanish-tts/
[20]: https://research.google/tools/datasets/argentinian-spanish-tts/
[21]: https://research.google/tools/datasets/peruvian-spanish-tts/
[22]: https://research.google/tools/datasets/venezuelan-spanish-tts/
[23]: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet
[24]: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/trafficcamnet
[25]: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/dashcamnet
[26]: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/facedetectir
[27]: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/vehicletypenet
[28]: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/vehiclemakenet
[29]: https://cloud.google.com/blog/products/ai-machine-learning/create-a-model-card-with-scikit-learn
[30]: https://blog.tensorflow.org/2020/11/using-model-card-toolkit-for-tf-model-transparency.html
[31]: https://drive.google.com/file/d/1WvHxUONoATFJ9JpSgAF7dU_F5V58HItS/view
[32]: https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view
[33]: https://drive.google.com/file/d/1tW-O4wE0SsluKNE7TLfRUgCxVFopwa1k/view
[34]: https://drive.google.com/file/d/1CMhN7Npdq0Dt2j0_z69mai2-m7oUTRKF/view
[35]: https://github.com/openai/CLIP/blob/main/model-card.md
[36]: https://github.com/openai/DALL-E/blob/master/model_card.md
[37]: https://github.com/cgpotts/dynasent/blob/main/dynasent_modelcard.md
[38]: https://github.com/cgpotts/dynasent/blob/main/dynasent_datasheet.md
[39]: https://github.com/openai/gpt-3/blob/master/model-card.md
[40]: https://github.com/salesforce/ctrl/blob/master/ModelCard.pdf
[41]: https://metamind.readme.io/docs/einstein-ocr-model-card
[42]: https://developers.google.com/ml-kit/images/vision/selfie-segmentation/selfie-model-card.pdf
[43]: https://github.com/salesforce/ctrl-detector/blob/master/ModelCard.pdf
[44]: https://github.com/salesforce/ai-economist/blob/master/Simulation_Card_Foundation_Economic_Simulation_Framework.pdf
[45]: https://help.salesforce.com/articleView?id=sf.mc_anb_einstein_messaging_insights_model_card.htm&type=5
[46]: https://help.salesforce.com/articleView?id=sf.mc_anb_einstein_engagement_frequency_model_card.htm&type=5
[47]: https://help.salesforce.com/articleView?id=sf.mc_anb_einstein_engagement_scoring_for_mobile_model_card.htm&type=5
[48]: https://einstein.ai/pdf/sales-call-coaching.pdf
[49]: https://help.salesforce.com/articleView?id=sf.bi_integrate_transformation_detectSentimentModelCard.htm&type=5
[50]: https://resources.docs.salesforce.com/latest/latest/en-us/sfdc/pdf/salesforce_ai_model_cards.pdf
[51]: https://docs.cohere.ai/generation-card
[52]: https://docs.cohere.ai/representation-card
[53]: https://www.verifyml.com
[54]: https://arxiv.org/abs/2110.13601
[55]: https://meta.wikimedia.org/wiki/User:HTriedman_(WMF)/Language_Agnostic_Link-Based_Article_Topic_Model_Card
[56]: https://github.com/openai/following-instructions-human-feedback/blob/main/model-card.md
[57]: https://github.com/openai/glide-text2im/blob/main/model-card.md
[58]: https://github.com/openai/guided-diffusion/blob/main/model-card.md
[59]: https://github.com/openai/summarize-from-feedback/blob/master/model_card.md
[60]: https://arxiv.org/abs/2112.11446
[61]: https://towardsdatascience.com/create-a-custom-model-card-with-googles-model-card-toolkit-a1e89a7887b5
[62]: https://huggingface.co/dalle-mini/dalle-mini
[63]: https://peptide.bio
[64]: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large
[65]: https://arxiv.org/abs/2205.05071
[66]: https://arxiv.org/abs/2205.01068
[67]: https://github.com/openai/dalle-2-preview/blob/main/system-card.md
[68]: https://arxiv.org/abs/2204.02311
[69]: https://www.andrew.cmu.edu/user/hongs/files/facct22-modelcardauthoring.pdf
[70]: https://github.com/openai/whisper/blob/main/model-card.md
[71]: https://github.com/paperswithcode/galai/blob/main/docs/model_card.md
[72]: https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md
[73]: https://github.com/openai/following-instructions-human-feedback/blob/main/model-card.md
[74]: https://meta.wikimedia.org/wiki/Machine_learning_models/Model_card_template
[75]: https://arxiv.org/pdf/2112.11446.pdf
[76]: https://nestauk.github.io/ojd_daps_skills/build/html/model_card.html#extract_skills_card
[77]: https://nestauk.github.io/ojd_daps_skills/build/html/model_card.html#mapping_card
[78]: https://cdn.openai.com/papers/GPTV_System_Card.pdf
[79]: https://www-files.anthropic.com/production/images/Model-Card-Claude-2.pdf
[80]: https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html
[81]: https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/
[82]: https://github.com/facebookresearch/audiocraft/blob/main/model_cards/AUDIOGEN_MODEL_CARD.md
[83]: https://github.com/facebookresearch/audiocraft/blob/main/model_cards/MUSICGEN_MODEL_CARD.md
[84]: https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md
[85]: https://cdn.openai.com/papers/gpt-4-system-card.pdf
[86]: https://x.ai/model-card/
[87]: https://ai.google.dev/gemma/docs/model_card
[88]: https://www.anthropic.com/claude-3-model-card
[89]: https://www.goody2.ai/goody2-modelcard.pdf
[90]: https://www-cdn.anthropic.com/fed9cc193a14b84131812372d8d5857f8f304c52/Model_Card_Claude_3_Addendum.pdf
[91]: https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md
[92]: https://arxiv.org/abs/2312.11805
[93]: https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf
[94]: https://ai.google.dev/gemma/docs/paligemma/model-card
[95]: https://ai.google.dev/gemma/docs/codegemma/model_card
[96]: https://ai.google.dev/gemma/docs/recurrentgemma/model_card
[97]: https://developers.perspectiveapi.com/s/about-the-api-model-cards?language=en_US
[98]: https://cloud.google.com/static/vertex-ai/generative-ai/docs/medlm/MedLM-model-card.pdf
[99]: https://ai.google/static/documents/palm2techreport.pdf#page=92