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model = AutoModel.from_pretrained("microsoft/xlm-align-base")| Model | POS | NER | XQuAD | MLQA | TyDiQA | XNLI | PAWS-X | Avg |
|---|---|---|---|---|---|---|---|---|
| XLM-R_base | 75.6 | 61.8 | 71.9 / 56.4 | 65.1 / 47.2 | 55.4 / 38.3 | 75.0 | 84.9 | 66.4 |
| XLM-Align | 76.0 | 63.7 | 74.7 / 59.0 | 68.1 / 49.8 | 62.1 / 44.8 | 76.2 | 86.8 | 68.9 |
b9d214025837250ede2f69c9385f812c config.json
6005db708eb4bab5b85fa3976b9db85b pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046 sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747 tokenizer.json@inproceedings{xlmalign,
title = "Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment",
author={Zewen Chi and Li Dong and Bo Zheng and Shaohan Huang and Xian-Ling Mao and Heyan Huang and Furu Wei},
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.265",
doi = "10.18653/v1/2021.acl-long.265",
pages = "3418--3430",}