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Open-weight model · Fill mask

chinese-bert-wwm

by Joint Laboratory of HIT and iFLYTEK Research (HFL) hfl/chinese-bert-wwm

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

Parameters
Context512
Weights1.2 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads273.4k

Model Card

By Joint Laboratory of HIT and iFLYTEK Research (HFL), published under apache-2.0, revision ab0aa81da273.

For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. 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, If you find the technical report or resource is useful, please cite the following technical report in your paper.

Read Joint Laboratory of HIT and iFLYTEK Research (HFL)'s full model card

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}
 }

Configuration

Architecture
BertForMaskedLM
Context length (tokens)
512
Layers
12
Hidden size
768
Feed-forward size
3,072
Attention heads
12
Vocabulary size
21,128
Model type
bert

Identity and Version

Repository
hfl/chinese-bert-wwm
Publisher
Joint Laboratory of HIT and iFLYTEK Research (HFL)
Task
Fill mask
Modality
Text
Library
transformers
Parameters
Not stated by the source
Languages
zh
Revision
ab0aa81da273504efc8540aa4d0bbaa3016a1bb5
First published
2022-03-02
Last updated
2021-05-19

Files and Weights

11 files, 1.2 GB in total. The weights are 3 files totalling 1.2 GB in bin, h5, msgpack.

Weights3 files · 1.2 GB
Configuration3 files · 761 B
Tokenizer3 files · 378.5 KB
Documentation1 file · 2.0 KB
Repository1 file · 391 B
Every file
FileTypeSizeSHA-256
flax_model.msgpackWeights409.2 MB 7ffe8ddfdc3e
pytorch_model.binWeights411.6 MB a279cbb1f2d4
tf_model.h5Weights409.3 MB d687197753e8
added_tokens.jsonConfiguration2 B
config.jsonConfiguration647 B
special_tokens_map.jsonConfiguration112 B
README.mdDocumentation2.0 KB
.gitattributesRepository391 B
tokenizer.jsonTokenizer269.0 KB
tokenizer_config.jsonTokenizer19 B
vocab.txtTokenizer109.5 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
1.2 GB
Download from Joint Laboratory of HIT and iFLYTEK Research (HFL)

Released by Joint Laboratory of HIT and iFLYTEK Research (HFL) through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published1.2 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About chinese-bert-wwm

Can I use chinese-bert-wwm commercially?

Yes. chinese-bert-wwm is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is chinese-bert-wwm's context length?

512 tokens, from the maximum position embeddings in its published configuration.

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