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  - **Developed by:** Cohere For AI
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  - **Model type:** a Transformer style autoregressive massively multilingual language model.
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- - **Paper**: [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](arxiv.com)
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  - **Point of Contact**: Cohere For AI: [cohere.for.ai](cohere.for.ai)
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  - **Languages**: Refer to the list of languages in the `language` section of this model card.
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  - **License**: Apache-2.0
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  - [DataProvenance collection](https://huggingface.co/datasets/DataProvenanceInitiative/Commercially-Verified-Licenses)
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  - ShareGPT-Command
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- All datasets are subset to the 101 languages supported by [mT5]. See the [paper](arxiv.com) for details about filtering and pruning.
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  ## Evaluation
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- We refer to Section 5 from our paper for multilingual eval across 99 languages – including discriminative, generative tasks, human evaluation and simulated win rates that cover both held-out tasks and in-distribution performance.
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  ## Bias, Risks, and Limitations
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- For a detailed overview of our effort at safety mitigation and benchmarking toxicity and bias across multiple languages, we refer Sections 6 and 7 of our paper: [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](arxiv.com).
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  We hope that the release of the Aya model will make community-based redteaming efforts possible, by exposing an open-source massively-multilingual model for community research.
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  - **Developed by:** Cohere For AI
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  - **Model type:** a Transformer style autoregressive massively multilingual language model.
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+ - **Paper**: [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827)
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  - **Point of Contact**: Cohere For AI: [cohere.for.ai](cohere.for.ai)
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  - **Languages**: Refer to the list of languages in the `language` section of this model card.
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  - **License**: Apache-2.0
 
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  - [DataProvenance collection](https://huggingface.co/datasets/DataProvenanceInitiative/Commercially-Verified-Licenses)
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  - ShareGPT-Command
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+ All datasets are subset to the 101 languages supported by [mT5]. See the [paper](https://arxiv.org/abs/2402.07827) for details about filtering and pruning.
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  ## Evaluation
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+ We refer to Section 5 from our paper for multilingual eval across 99 languages – including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance.
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  ## Bias, Risks, and Limitations
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+ For a detailed overview of our effort at safety mitigation and benchmarking toxicity and bias across multiple languages, we refer to Sections 6 and 7 of our paper: [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827).
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  We hope that the release of the Aya model will make community-based redteaming efforts possible, by exposing an open-source massively-multilingual model for community research.
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