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--- |
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tags: |
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- fill-mask |
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- text2text-generation |
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- fill-mask |
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- text-classification |
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- Summarization |
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- Chinese |
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- CPT |
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- BART |
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- BERT |
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- seq2seq |
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language: zh |
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--- |
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# Chinese CPT-Large |
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## Model description |
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This is an implementation of CPT-Large. 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. |
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[**CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation**](https://arxiv.org/pdf/2109.05729.pdf) |
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Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu |
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**Github Link:** https://github.com/fastnlp/CPT |
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## Usage |
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```python |
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>>> from modeling_cpt import CPTForConditionalGeneration |
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>>> from transformers import BertTokenizer |
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>>> tokenizer = BertTokenizer.from_pretrained("fnlp/cpt-large") |
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>>> model = CPTForConditionalGeneration.from_pretrained("fnlp/cpt-large") |
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>>> inputs = tokenizer.encode("北京是[MASK]的首都", return_tensors='pt') |
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>>> pred_ids = model.generate(input_ids, num_beams=4, max_length=20) |
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>>> print(tokenizer.convert_ids_to_tokens(pred_ids[i])) |
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['[SEP]', '[CLS]', '北', '京', '是', '中', '国', '的', '首', '都', '[SEP]'] |
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``` |
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**Note: Please use BertTokenizer for the model vocabulary. DO NOT use original BartTokenizer.** |
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## Citation |
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```bibtex |
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@article{shao2021cpt, |
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title={CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation}, |
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author={Yunfan Shao and Zhichao Geng and Yitao Liu and Junqi Dai and Fei Yang and Li Zhe and Hujun Bao and Xipeng Qiu}, |
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journal={arXiv preprint arXiv:2109.05729}, |
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year={2021} |
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} |
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``` |
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