language: zh
widget:
- text: 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。
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 HuggingFace 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 finetune/run_cmrc.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
--vocab_path models/google_zh_vocab.txt \
--train_path datasets/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 Huggingface's format:
python3 scripts/convert_bert_extractive_qa_from_uer_to_huggingface.py --input_model_path models/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}