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Datasets:
neuclir
/
csl

Tasks:
Text Retrieval
Modalities:
Text
Sub-tasks:
document-retrieval
Languages:
Chinese
English
Size:
100K - 1M
Libraries:
Datasets
License:
Dataset card Viewer Files Community
1
csl
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---
annotations_creators:
- no-annotation
language:
- zh
language_creators:
- found
license:
- apache-2.0
pretty_name: CSL
size_categories:
- 1M<n<10M
source_datasets:
- extended|csl
tags: []
task_categories:
- text-retrieval
task_ids:
- document-retrieval
---

# Dataset Card for CSL

## Dataset Description

CSL is the Chinese Scientific Literature Dataset.

- **Paper:** https://aclanthology.org/2022.coling-1.344
- **Repository:** https://github.com/ydli-ai/CSL

### Dataset Summary

The dataset contains titles, abstracts, keywords of papers written in Chinese from several academic fields.

### Languages

 - Chinese

## Dataset Structure

### Data Instances

| Split           | Documents |
|-----------------|----------:|
| `csl`           |      396k |

### Data Fields
 - `id`: unique identifier for this document
 - `cc_file`: source file from connon crawl
 - `time`: extracted date/time from article
 - `title`: title extracted from article
 - `text`: extracted article body
 - `url`: source URL

## Dataset Usage

Using 🤗 Datasets:

```python
from datasets import load_dataset

dataset = load_dataset('neuclir/csl')['csl']
```

## License & Citation

This dataset is based off the [Chinese Scientific Literature Dataset](https://github.com/ydli-ai/CSL) under Apache 2.0.
The primay change is the addition of English translactions of the category and discipline descriptions by a native speaker.

If you use this data, please cite:

```
@inproceedings{li-etal-2022-csl,
    title = "{CSL}: A Large-scale {C}hinese Scientific Literature Dataset",
    author = "Li, Yudong  and
      Zhang, Yuqing  and
      Zhao, Zhe  and
      Shen, Linlin  and
      Liu, Weijie  and
      Mao, Weiquan  and
      Zhang, Hui",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.344",
    pages = "3917--3923",
}
```