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README.md

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-# Chinese BART-Base
-
-## Model description
-
-This is an implementation of CPT-Base. To use CPT, please import the file `modeling_cpt.py` (**Download** [Here](https://github.com/fastnlp/CPT/blob/master/finetune/modeling_cpt.py)) that define the architecture of CPT into your project.
-
-[**CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation**](https://arxiv.org/pdf/2109.05729.pdf)
-
-Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu
-
-**Github Link:** https://github.com/fastnlp/CPT
-
-## Usage
-
-```python
->>> from modeling_cpt import CPTForConditionalGeneration
->>> from transformers import BertTokenizer
->>> tokenizer = BertTokenizer.from_pretrained("fnlp/cpt-base")
->>> model = CPTForConditionalGeneration.from_pretrained("fnlp/cpt-base")
->>> inputs = tokenizer.encode("北京是[MASK]的首都", return_tensors='pt')
->>> pred_ids = model.generate(input_ids, num_beams=4, max_length=20)
->>> print(tokenizer.convert_ids_to_tokens(pred_ids[i]))
-    ['[SEP]', '[CLS]', '北', '京', '是', '中', '国', '的', '首', '都', '[SEP]']
-```
-
-**Note: Please use BertTokenizer for the model vocabulary. DO NOT use original BartTokenizer.**
-
-## Citation
-
-```bibtex
-@article{shao2021cpt,
-  title={CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation}, 
-  author={Yunfan Shao and Zhichao Geng and Yitao Liu and Junqi Dai and Fei Yang and Li Zhe and Hujun Bao and Xipeng Qiu},
-  journal={arXiv preprint arXiv:2109.05729},
-  year={2021}
-}
-```