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---
language: Chinese
widget:
- question: "小王在哪上学？"
- context: "小王在北京上学，他今年二十岁。"
---
# Chinese RoBERTa Model
## Model description
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).
## How to use
You can use the model directly with a pipeline for extractive question answering:
```python
>>> from transformers import pipeline
>>> path = 'uer/roberta-base-chinese-extractive-qa'
>>> nlp = pipeline('question-answering', model=path, tokenizer=path)
>>> QA_input = {'question': "小王在哪上学？",'context': "小王在北京上学，他今年二十岁。"}
>>> nlp(QA_input)
{'score': 0.7618623375892639, 'start': 3, 'end': 5, 'answer': '北京'}
```
## Training data
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).
## Training procedure
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).
```
python3 run_cmrc.py --dataset_path lyric_dataset.pt \
--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 --batch_size 32 --epochs_num 3 \
--embedding word_pos_seg --encoder transformer --mask fully_visible
```
Finally, we convert the pre-trained model into Huggingface's format:
```
python3 scripts/convert_roberta_extractive_qa_from_uer_to_huggingface.py --input_model_path extractive_qa_model.bin \
--output_model_path pytorch_model.bin \
--layers_num 12
```
### BibTeX entry and citation info
```
@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}
}
``` |