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