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--- |
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language: Chinese |
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widget: |
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- text: "小王在哪上学？" |
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context: "小王在北京上学，他今年二十岁。" |
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--- |
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# Chinese RoBERTa Base Model for QA |
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## Model description |
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The model is used for extractive question answering. You can download the model from the link [roberta-base-chinese-extractive-qa](https://huggingface.co/uer/roberta-base-chinese-extractive-qa). |
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## How to use |
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You can use the model directly with a pipeline for extractive question answering: |
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```python |
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>>> from transformers import pipeline |
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>>> path = 'uer/roberta-base-chinese-extractive-qa' |
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>>> nlp = pipeline('question-answering', model=path, tokenizer=path) |
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>>> QA_input = {'question': "小王在哪上学？",'context': "小王在北京上学，他今年二十岁。"} |
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>>> nlp(QA_input) |
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{'score': 0.7618623375892639, 'start': 3, 'end': 5, 'answer': '北京'} |
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``` |
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## Training data |
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Training data contains three datasets ,including [cmrc2018](https://github.com/ymcui/cmrc2018), [webqa](https://spaces.ac.cn/archives/4338) and [莱斯杯](https://www.kesci.com/home/competition/5d142d8cbb14e6002c04e14a/content/0). |
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## Training procedure |
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The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). 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](https://huggingface.co/uer/chinese_roberta_L-12_H-768). |
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``` |
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python3 run_cmrc.py --dataset_path lyric_dataset.pt \ |
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--pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--train_path extractive_qa.json \ |
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--dev_path datasets/cmrc2018/dev.json \ |
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--output_model_path models/extractive_qa_model.bin \ |
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--learning_rate 3e-5 --batch_size 32 --epochs_num 3 \ |
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--embedding word_pos_seg --encoder transformer --mask fully_visible |
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``` |
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Finally, we convert the fine-tuned model into Huggingface's format: |
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``` |
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python3 scripts/convert_roberta_extractive_qa_from_uer_to_huggingface.py --input_model_path extractive_qa_model.bin \ |
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--output_model_path pytorch_model.bin \ |
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--layers_num 12 |
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``` |
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### BibTeX entry and citation info |
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``` |
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@article{zhao2019uer, |
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title={UER: An Open-Source Toolkit for Pre-training Models}, |
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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}, |
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journal={EMNLP-IJCNLP 2019}, |
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pages={241}, |
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year={2019} |
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} |
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``` |
