| Model Type | | auto-regressive, transformer |
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| Use Cases |
| Areas: | |
| Applications: | | question answering, natural language understanding, reading comprehension |
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| Primary Use Cases: | | Developing techniques to improve language models |
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| Limitations: | | Should not be used for downstream applications without risk evaluation |
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| Considerations: | | The model can generate toxic content, incorrect information, or unhelpful answers due to lack of human feedback training. |
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| Additional Notes | | Requires special support code and converted via GPTQ method, experimental release. |
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| Supported Languages | | en (English), bg (Bulgarian), ca (Catalan), cs (Czech), da (Danish), de (German), es (Spanish), fr (French), hr (Croatian), hu (Hungarian), it (Italian), nl (Dutch), pl (Polish), pt (Portuguese), ro (Romanian), ru (Russian), sl (Slovene), sr (Serbian), sv (Swedish), uk (Ukrainian) |
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| Training Details |
| Data Sources: | | CCNet, C4, GitHub, Wikipedia, Books, ArXiv, Stack Exchange |
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| Model Architecture: | |
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| Safety Evaluation |
| Risk Categories: | | misinformation, bias, toxicity |
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| Ethical Considerations: | | The model reflects biases from its training data, which is collected from various web sources. |
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| Responsible Ai Considerations |
| Fairness: | | Evaluated on RAI datasets for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance, and socio-economic status biases. |
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| Transparency: | | Bias evaluation results are reported. |
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| Mitigation Strategies: | | Filtered web data based on its proximity to Wikipedia using Kneser-Ney and fastText linear classifier. |
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| Input Output | |