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**M2M100** is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
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- [Supported models](#supported-models)
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- [Requirements](#requirements)
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- [Translating a file](#translate-a-file)
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- [Using single CPU/GPU](#using-a-single-cpu-gpu)
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- [Multi-GPU](#multi-gpu)
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- [Automatic Batch Size Finder](#automatic-batch-size-finder)
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- [Choose precision](#choose-precision)
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- [Evaluate translations](#evaluate-translations)
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>The model that can directly translate between the 9,900 directions of 100 languages.
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Easy-Translate is built on top of 🤗HuggingFace's [Transformers](https://huggingface.co/docs/transformers/index) and 🤗HuggingFace's[Accelerate](https://huggingface.co/docs/accelerate/index) library.
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**M2M100** is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
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>M2M100 that can directly translate between 9,900 directions of 100 languages.
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Easy-Translate is built on top of 🤗HuggingFace's [Transformers](https://huggingface.co/docs/transformers/index) and 🤗HuggingFace's[Accelerate](https://huggingface.co/docs/accelerate/index) library.
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