fill-mask text2text-generation fill-mask text-classification Summarization Chinese CPT BART BERT seq2seq

Chinese CPT-Large

News

12/30/2022

An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:

We initialize the new version of models with the old version of checkpoints with vocabulary alignment. Token embeddings found in the old checkpoints are copied. And other newly added parameters are randomly initialized. We further train the new CPT & Chinese BART 50K steps with batch size 2048, max-seq-length 1024, peak learning rate 2e-5, and warmup ratio 0.1.

The result compared to the previous checkpoints is as followings:

AFQMC IFLYTEK CSL-sum LCSTS AVG
Previous
bart-base 73.0 60 62.1 37.8 58.23
cpt-base 75.1 60.5 63.0 38.2 59.20
bart-large 75.7 62.1 64.2 40.6 60.65
cpt-large 75.9 61.8 63.7 42.0 60.85
Updataed
bart-base 73.03 61.25 61.51 38.78 58.64
cpt-base 74.40 61.23 62.09 38.81 59.13
bart-large 75.81 61.52 64.62 40.90 60.71
cpt-large 75.97 61.63 63.83 42.08 60.88

The result shows that the updated models maintain comparative performance compared with previous checkpoints. There are still some cases that the updated model is slightly worse than the previous one, which results from the following reasons: 1) Training additional a few steps did not lead to significant performance improvement; 2) some downstream tasks are not affected by the newly added tokens and longer encoding sequences, but sensitive to the fine-tuning hyperparameters.

Model description

This is an implementation of CPT-Large. To use CPT, please import the file modeling_cpt.py (Download Here) that define the architecture of CPT into your project.

CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu

Github Link: https://github.com/fastnlp/CPT

Usage

>>> from modeling_cpt import CPTForConditionalGeneration
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("fnlp/cpt-large")
>>> model = CPTForConditionalGeneration.from_pretrained("fnlp/cpt-large")
>>> input_ids = 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[0]))
    ['[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

@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}
}