BLOOM LM
BigScience Large Open-science Open-access Multilingual Language Model
Model Card
👁 BigScience LogoVersion 1.0 / 26.May.2022
Table of Contents
- Model Details
- Uses
- Training Data
- Risks and Limitations
- Evaluation
- Recommendations
- Glossary and Calculations
- More Information
- Model Card Authors
Model Details
Basics
This section provides information for anyone who wants to know about the model.
Technical Specifications
This section provides information for people who work on model development.
Environmental Impact
Uses
This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. It provides information for anyone considering using the model or who is affected by the model.
Training Data
This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.
Risks and Limitations
This section identifies foreseeable harms and misunderstandings.
Evaluation
This section describes the evaluation protocols and provides the results.
Recommendations
This section provides information on warnings and potential mitigations.
Glossary and Calculations
This section defines common terms and how metrics are calculated.
More Information
Model Card Authors
Ordered roughly chronologically and by amount of time spent.
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
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Evaluation results
- acc on arc_challengeself-reported0.280
- acc on arc_easyself-reported0.595
- acc on axbself-reported0.443
- acc on axgself-reported0.500
- acc on boolqself-reported0.617
- acc on cbself-reported0.304
- acc on colaself-reported0.611
- acc on copaself-reported0.630
