Create README.md

README.md

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+---
+tags:
+- fill-mask
+- text2text-generation
+- fill-mask
+- Chinese
+- cpt
+- seq2seq
+
+language: zh
+---
+
+# Chinese CPT-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}
+}
+```