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https://github.com/ymcui/cmrc2018
A Span-Extraction Dataset for Chinese Machine Reading Comprehension (CMRC 2018)
https://github.com/ymcui/cmrc2018
bert natural-language-processing question-answering reading-comprehension
Last synced: 2 days ago
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A Span-Extraction Dataset for Chinese Machine Reading Comprehension (CMRC 2018)
- Host: GitHub
- URL: https://github.com/ymcui/cmrc2018
- Owner: ymcui
- License: cc-by-sa-4.0
- Created: 2018-01-04T01:20:52.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-15T01:04:26.000Z (over 2 years ago)
- Last Synced: 2025-02-12T11:10:17.810Z (9 days ago)
- Topics: bert, natural-language-processing, question-answering, reading-comprehension
- Language: Python
- Homepage: https://ymcui.github.io/cmrc2018/
- Size: 6.18 MB
- Stars: 421
- Watchers: 12
- Forks: 87
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[**中文说明**](./README_CN.md) | [**English**](./README.md)
This repository contains the data for [The Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018)](https://hfl-rc.github.io/cmrc2018/). We will present our paper on [EMNLP 2019](http://emnlp-ijcnlp2019.org).
**Title: A Span-Extraction Dataset for Chinese Machine Reading Comprehension**
Authors: Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu
Link: [https://www.aclweb.org/anthology/D19-1600/](https://www.aclweb.org/anthology/D19-1600/)
Venue: EMNLP-IJCNLP 2019
### Open Challenge Leaderboard (New!)
Keep track of the latest state-of-the-art systems on CMRC 2018 dataset.
[https://ymcui.github.io/cmrc2018/](https://ymcui.github.io/cmrc2018/)
### CMRC 2018 Public Datasets
Please download CMRC 2018 public datasets via the following CodaLab Worksheet.
[https://worksheets.codalab.org/worksheets/0x92a80d2fab4b4f79a2b4064f7ddca9ce](https://worksheets.codalab.org/worksheets/0x92a80d2fab4b4f79a2b4064f7ddca9ce)
### Submission Guidelines
If you would like to **test your model on the hidden test and challenge set**, please follow the instructions on how to submit your model via CodaLab worksheet.
[https://worksheets.codalab.org/worksheets/0x96f61ee5e9914aee8b54bd11e66ec647/](https://worksheets.codalab.org/worksheets/0x96f61ee5e9914aee8b54bd11e66ec647/)
**Note that the test set on [CLUE](https://github.com/CLUEbenchmark/CLUE) is NOT the complete test set. If you wish to evaluate your model OFFICIALLY on CMRC 2018, you should follow the guidelines here. **
### Quick Load Through 🤗datasets
You can also access this dataset as part of the [HuggingFace `datasets` library](https://github.com/huggingface/datasets) library as follow:
```python
!pip install datasets
from datasets import load_dataset
dataset = load_dataset('cmrc2018')
```
More details on the options and usage for this library can be found on the `nlp` repository at https://github.com/huggingface/nlp
### Reference
If you wish to use our data in your research, please cite:
```
@inproceedings{cui-emnlp2019-cmrc2018,
title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension",
author = "Cui, Yiming and
Liu, Ting and
Che, Wanxiang and
Xiao, Li and
Chen, Zhipeng and
Ma, Wentao and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1600",
doi = "10.18653/v1/D19-1600",
pages = "5886--5891",
}
```
### International Standard Language Resource Number (ISLRN)
ISLRN: 013-662-947-043-2
http://www.islrn.org/resources/resources_info/7952/
### Official HFL WeChat Account
Follow Joint Laboratory of HIT and iFLYTEK Research (HFL) on WeChat.
### Contact us
Please submit an issue.