Chinese RoBERTa-Base Model for QA

Model description

The model is used for extractive question answering. It is fine-tuned by UER-py, which is introduced in this paper. Besides, the model could also be fine-tuned by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.

You can download the model either from the UER-py Modelzoo page, or via Model Database from the link roberta-base-chinese-extractive-qa.

How to use

You can use the model directly with a pipeline for extractive question answering:

>>> from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline
>>> model = AutoModelForQuestionAnswering.from_pretrained('uer/roberta-base-chinese-extractive-qa')
>>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-chinese-extractive-qa')
>>> QA = pipeline('question-answering', model=model, tokenizer=tokenizer)
>>> QA_input = {'question': "著名诗歌《假如生活欺骗了你》的作者是",'context': "普希金从那里学习人民的语言，吸取了许多有益的养料，这一切对普希金后来的创作产生了很大的影响。这两年里，普希金创作了不少优秀的作品，如《囚徒》、《致大海》、《致凯恩》和《假如生活欺骗了你》等几十首抒情诗，叙事诗《努林伯爵》，历史剧《鲍里斯·戈都诺夫》，以及《叶甫盖尼·奥涅金》前六章。"}
>>> QA(QA_input)
    {'score': 0.9766426682472229, 'start': 0, 'end': 3, 'answer': '普希金'}

Training data

Training data comes from three sources: cmrc2018, webqa, and laisi. We only use the train set of three datasets.

Training procedure

The model is fine-tuned by UER-py on Tencent Cloud. We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model chinese_roberta_L-12_H-768. At the end of each epoch, the model is saved when the best performance on development set is achieved.

python3 run_cmrc.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
                    --vocab_path models/google_zh_vocab.txt \
                    --train_path extractive_qa.json \
                    --dev_path datasets/cmrc2018/dev.json \
                    --output_model_path models/extractive_qa_model.bin \
                    --learning_rate 3e-5 --epochs_num 3 --batch_size 32 --seq_length 512

Finally, we convert the fine-tuned model into Model Database's format:

python3 scripts/convert_bert_extractive_qa_from_uer_to_Model Database.py --input_model_path extractive_qa_model.bin \
                                                                      --output_model_path pytorch_model.bin \
                                                                      --layers_num 12

BibTeX entry and citation info

@article{liu2019roberta,
  title={Roberta: A robustly optimized bert pretraining approach},
  author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
  journal={arXiv preprint arXiv:1907.11692},
  year={2019}
}

@article{zhao2019uer,
  title={UER: An Open-Source Toolkit for Pre-training Models},
  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
  journal={EMNLP-IJCNLP 2019},
  pages={241},
  year={2019}
}

@article{zhao2023tencentpretrain,
  title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
  author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
  journal={ACL 2023},
  pages={217},
  year={2023}