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--- |
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language: |
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- zh |
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license: apache-2.0 |
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widget: |
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- text: "生活的真谛是[MASK]。" |
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--- |
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# Mengzi-BERT base model (Chinese) |
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Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task. |
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[Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model](www.example.com) |
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## Usage |
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```python |
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from transformers import BertTokenizer, BertModel |
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tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base") |
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model = BertModel.from_pretrained("Langboat/mengzi-bert-base") |
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``` |
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## Scores on nine chinese tasks (without any data augmentation) |
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|Model|AFQMC|TNEWS|IFLYTEK|CMNLI|WSC|CSL|CMRC|C3|CHID| |
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|RoBERTa-wwm-ext|74.04|56.94|60.31|80.51|67.80|81.00|75.20|66.50|83.62| |
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|Mengzi-BERT-base|74.58|57.97|60.68|82.12|87.50|85.40|78.54|71.70|84.16| |
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RoBERTa-wwm-ext scores are from CLUE baseline |
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## Citation |
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If you find the technical report or resource is useful, please cite the following technical report in your paper. |
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``` |
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example |
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``` |
