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hfl_chinese-bert-wwm

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language:

  • zh license: "apache-2.0"

Chinese BERT with Whole Word Masking

For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.

Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu

This repository is developed based on:https://github.com/google-research/bert

You may also interested in,

  • Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
  • Chinese MacBERT: https://github.com/ymcui/MacBERT
  • Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
  • Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
  • Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer

More resources by HFL: https://github.com/ymcui/HFL-Anthology

Citation

If you find the technical report or resource is useful, please cite the following technical report in your paper.

  • Primary: https://arxiv.org/abs/2004.13922
@inproceedings{cui-etal-2020-revisiting,
    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
    author = "Cui, Yiming  and
      Che, Wanxiang  and
      Liu, Ting  and
      Qin, Bing  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
    pages = "657--668",
}
  • Secondary: https://arxiv.org/abs/1906.08101
@article{chinese-bert-wwm,
  title={Pre-Training with Whole Word Masking for Chinese BERT},
  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
  journal={arXiv preprint arXiv:1906.08101},
  year={2019}
 }

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Files & hashes

PathSizesha1sha256
README.md1.9 KB (1,995 B)31d6e4243fd67814fc55124ee84ded93264ef04c8fe72a1f4fe789244c66a65b0498d253003ad7a149503e5c86d83975b16bea6f
added_tokens.json2 B (2 B)9e26dfeeb6e641a33dae4961196235bdb965b21b44136fa355b3678a1146ad16f7e8649e94fb4fc21fe77e8310c060f61caaff8a
config.json647 B (647 B)5198ed6643fc484e297d0ff7a1d4deaf9293b410ce60aaa7b45136a78aa1f5f237b515e713f489abe6026bee85fae6e674207494
pytorch_model.bin392.5 MB (411,578,458 B)c6d7071602a410b243dbc29b94ad1dcf1b29a7baa279cbb1f2d47b8d65b06e832fceabaff570d6089d6d9c72071d8d41fe12f65a
special_tokens_map.json112 B (112 B)e7b0375001f109a6b8873d756ad4f7bbb15fbaa5303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3
tokenizer.json262.7 KB (268,961 B)eba64ab600f7bb029b38ac391b35651e3b55f18553ff61207898738bbdc000f38abebef01041c8d23b6270c11855fc692d0a3ad6
tokenizer_config.json19 B (19 B)66051e65c65b3ec5e0b437496d1e545c5d8934b461785aeaba176fba6d6489f27dccfd2ddee6aee2af0e590451cab7d8b57e0874
vocab.txt107.0 KB (109,540 B)ca4f9781030019ab9b253c6dcb8c7878b6dc87a545bbac6b341c319adc98a532532882e91a9cefc0329aa57bac9ae761c27b291c

Cite this release

Canonical URL
https://aiseedbank.org/models/hfl_chinese-bert-wwm/
Slug
hfl_chinese-bert-wwm
Infohash
0e1e2905d40131d6c60884137a7ccdebc8a347c8
License
apache-2.0
Signing key fingerprint
85a3b32c3712427b

Every file carries a locally computed sha256 — verify a download against the signed sums: hfl_chinese-bert-wwm.SHA256SUMS (+ minisign signature).

Provenance

Upstream repositoryhfl/chinese-bert-wwm
Revision (pinned)ab0aa81da273504efc8540aa4d0bbaa3016a1bb5
Fetched at2026-09-04T00:34:21Z
License at fetchapache-2.0
Snapshot toolhuggingface · seedbank 0.1.0

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apache-2.0392.9 MB (411,959,734 bytes)transformerspytorchjaxbertfill-maskendpoints_compatible2 languages (tf, zh)paper: 1906.08101paper: 2004.13922