FLORES-200 (FLoRes-200) Evaluation Benchmark for Multilingual Machine Translation.

FLORES-200 (sometimes written FLoRes-200) is a multilingual evaluation benchmark for machine translation that extends Facebook AI’s earlier FLORES benchmarks to cover ~200 languages. It provides fully aligned sentence-level translations across many languages (many-to-many evaluation) and standard dev/devtest splits that are widely used as the primary evaluation benchmark for multilingual MT research (papers commonly report metrics such as COMET-22 and SacreBLEU on the FLORES-200 devtest split). The dataset is maintained from Meta/Facebook AI resources (GitHub) and is available via community-curated Hugging Face dataset repos (e.g., Muennighoff/flores200). FLORES-200 is the evaluation set used in the NLLB (No Language Left Behind) work and many subsequent multilingual MT evaluations.

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