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Alexis Conneau committed Aug 20, 2019
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Expand Up @@ -200,12 +200,12 @@ python glue-xnli.py

We provide large pretrained models for the 15 languages of [XNLI](https://github.com/facebookresearch/XNLI), and two other models in 17 and 100 languages.

| Languages | Pretraining | Model | BPE codes | Vocabulary |
| ---------------- | ----------- |:-------------------------------------------------------------------:|:-------------------------------------------------------------:| --------------------------------------------------------------:|
| 15 | MLM | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_xnli15_1024.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_15) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_15) |
| 15 | MLM + TLM | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_tlm_xnli15_1024.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_15) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_15) |
| 17 | MLM | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_17_1280.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_17) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_17) |
| 100 | MLM | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_100_1280.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_100) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_100) |
| Languages | Pretraining | Tokenization | Model | BPE codes | Vocabulary |
| ---------------- | ----------- | ----------- |:-------------------------------------------------------------------:|:-------------------------------------------------------------:| --------------------------------------------------------------:|
| 15 | MLM | tokenize.sh + lower-case + accent-removal + BPE | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_xnli15_1024.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_15) (80k) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_15) (95k) |
| 15 | MLM + TLM | tokenize.sh + lower-case + accent-removal + BPE | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_tlm_xnli15_1024.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_15) (80k) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_15) (95k) |
| 17 | MLM | tokenize.sh + BPE | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_17_1280.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_17) (175k) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_17) (200k) |
| 100 | MLM | tokenize.sh + BPE | [Model](https://dl.fbaipublicfiles.com/XLM/mlm_100_1280.pth) | [BPE codes](https://dl.fbaipublicfiles.com/XLM/codes_xnli_100) (175k) | [Vocabulary](https://dl.fbaipublicfiles.com/XLM/vocab_xnli_100) (200k) |

which obtains better performance than mBERT on the [XNLI cross-lingual classification task](https://arxiv.org/abs/1809.05053):

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