NLLB 200 This is the model card of NLLB 200's distilled 600M variant. Here are the metrics for that particular checkpoint. Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB 200 is described in the paper. Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human Centered Machine Translation, Arxiv, 2022 License: CC BY NC Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues Intended Use Primary intended uses: NLLB 200 is a machine translation model primarily intended for research in machine translation, especially for low resource languages. It allows for single sentence translation among 200 languages. Information on how to use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data. Primary intended users: Primary users are researchers and machine translation research community. Out of scope use cases: NLLB 200 is…
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