hfl_chinese-bert-wwm
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Model card
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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}
}
Magnet link
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magnet:?xt=urn:btih:0e1e2905d40131d6c60884137a7ccdebc8a347c8&dn=hfl_chinese-bert-wwmOpen magnet in torrent client · infohash 0e1e2905d40131d6c60884137a7ccdebc8a347c8
Files & hashes
| Path | Size | sha1 | sha256 |
|---|---|---|---|
| README.md | 1.9 KB (1,995 B) | 31d6e4243fd67814fc55124ee84ded93264ef04c | 8fe72a1f4fe789244c66a65b0498d253003ad7a149503e5c86d83975b16bea6f |
| added_tokens.json | 2 B (2 B) | 9e26dfeeb6e641a33dae4961196235bdb965b21b | 44136fa355b3678a1146ad16f7e8649e94fb4fc21fe77e8310c060f61caaff8a |
| config.json | 647 B (647 B) | 5198ed6643fc484e297d0ff7a1d4deaf9293b410 | ce60aaa7b45136a78aa1f5f237b515e713f489abe6026bee85fae6e674207494 |
| pytorch_model.bin | 392.5 MB (411,578,458 B) | c6d7071602a410b243dbc29b94ad1dcf1b29a7ba | a279cbb1f2d47b8d65b06e832fceabaff570d6089d6d9c72071d8d41fe12f65a |
| special_tokens_map.json | 112 B (112 B) | e7b0375001f109a6b8873d756ad4f7bbb15fbaa5 | 303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3 |
| tokenizer.json | 262.7 KB (268,961 B) | eba64ab600f7bb029b38ac391b35651e3b55f185 | 53ff61207898738bbdc000f38abebef01041c8d23b6270c11855fc692d0a3ad6 |
| tokenizer_config.json | 19 B (19 B) | 66051e65c65b3ec5e0b437496d1e545c5d8934b4 | 61785aeaba176fba6d6489f27dccfd2ddee6aee2af0e590451cab7d8b57e0874 |
| vocab.txt | 107.0 KB (109,540 B) | ca4f9781030019ab9b253c6dcb8c7878b6dc87a5 | 45bbac6b341c319adc98a532532882e91a9cefc0329aa57bac9ae761c27b291c |
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 repository | hfl/chinese-bert-wwm |
|---|---|
| Revision (pinned) | ab0aa81da273504efc8540aa4d0bbaa3016a1bb5 |
| Fetched at | 2026-09-04T00:34:21Z |
| License at fetch | apache-2.0 |
| Snapshot tool | huggingface · seedbank 0.1.0 |
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✓ verified · rehash-vs-hf-metadata at 2026-09-04T00:34:28Z
apache-2.0392.9 MB (411,959,734 bytes)transformerspytorchjaxbertfill-maskendpoints_compatible2 languages (tf, zh)paper: 1906.08101paper: 2004.13922