Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the paper. The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``). - re-ranking note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

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wmt19-en-de

facebook

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wmt19-en-ru

facebook

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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facebook

XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\* (\*Equal Contribution). The original implementation was released in this repository. For intended usage of the model, please refer to the model card released by the XGLM-564M development team. The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi.

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xglm-7.5B

facebook

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

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xlm-roberta-xl

facebook

XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau and first released in this repository. Disclaimer: The team releasing XLM-RoBERTa-XL did not write a model card for this model so this model card has been written by the Hugging Face team. XLM-RoBERTa-XL is a extra large multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.

Fill-mask

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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