|
--- |
|
annotations_creators: |
|
- shibing624 |
|
language_creators: |
|
- shibing624 |
|
language: |
|
- zh |
|
license: |
|
- cc-by-4.0 |
|
multilinguality: |
|
- zh |
|
size_categories: |
|
- 100K<n<20M |
|
source_datasets: |
|
- https://www.biendata.xyz/competition/sohu_2021/data/ |
|
task_categories: |
|
- text-classification |
|
- sentence-similarity |
|
task_ids: |
|
- natural-language-inference |
|
- semantic-similarity-scoring |
|
- text-scoring |
|
paperswithcode_id: sts |
|
pretty_name: Sentence Text Similarity SOHU2021 |
|
--- |
|
# Dataset Card for sts-sohu2021 |
|
|
|
## Dataset Description |
|
- **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec) |
|
- **Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) (located on the homepage) |
|
- **Size of downloaded dataset files:** 218 MB |
|
- **Total amount of disk used:** 218 MB |
|
### Dataset Summary |
|
|
|
2021搜狐校园文本匹配算法大赛数据集 |
|
|
|
- 数据源:https://www.biendata.xyz/competition/sohu_2021/data/ |
|
|
|
分为 A 和 B 两个文件,A 和 B 文件匹配标准不一样。其中 A 和 B 文件又分为“短短文本匹配”、“短长文本匹配”和“长长文本匹配”。 |
|
A 文件匹配标准较为宽泛,两段文字是同一个话题便视为匹配,B 文件匹配标准较为严格,两段文字须是同一个事件才视为匹配。 |
|
|
|
|
|
数据类型: |
|
|
|
|
|
| type | 数据类型 | |
|
| --- | ------------| |
|
| dda | 短短匹配 A 类 | |
|
| ddb | 短短匹配 B 类 | |
|
| dca | 短长匹配 A 类 | |
|
| dcb | 短长匹配 B 类 | |
|
| cca | 长长匹配 A 类 | |
|
| ccb | 长长匹配 B 类 | |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
Supported Tasks: 支持中文文本匹配任务,文本相似度计算等相关任务。 |
|
|
|
中文匹配任务的结果目前在顶会paper上出现较少,我罗列一个我自己训练的结果: |
|
|
|
**Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) |
|
|
|
### Languages |
|
|
|
数据集均是简体中文文本。 |
|
|
|
## Dataset Structure |
|
### Data Instances |
|
An example of 'train' looks as follows. |
|
```python |
|
# A 类 短短 样本示例 |
|
{ |
|
"sentence1": "小艺的故事让爱回家2021年2月16日大年初五19:30带上你最亲爱的人与团团君相约《小艺的故事》直播间!", |
|
"sentence2": "香港代购了不起啊,宋点卷竟然在直播间“炫富”起来", |
|
"label": 0 |
|
} |
|
|
|
# B 类 短短 样本示例 |
|
{ |
|
"sentence1": "让很多网友好奇的是,张柏芝在一小时后也在社交平台发文:“给大家拜年啦。”还有网友猜测:谢霆锋的经纪人发文,张柏芝也发文,并且配图,似乎都在证实,谢霆锋依旧和王菲在一起,而张柏芝也有了新的恋人,并且生了孩子,两人也找到了各自的归宿,有了自己的幸福生活,让传言不攻自破。", |
|
"sentence2": "陈晓东谈旧爱张柏芝,一个口误暴露她的秘密,难怪谢霆锋会离开她", |
|
"label": 0 |
|
} |
|
``` |
|
|
|
label: 0表示不匹配,1表示匹配。 |
|
|
|
### Data Fields |
|
The data fields are the same among all splits. |
|
|
|
- `sentence1`: a `string` feature. |
|
- `sentence2`: a `string` feature. |
|
- `label`: a classification label, with possible values including `similarity` (1), `dissimilarity` (0). |
|
|
|
### Data Splits |
|
|
|
|
|
```shell |
|
> wc -l *.jsonl |
|
11690 cca.jsonl |
|
11690 ccb.jsonl |
|
11592 dca.jsonl |
|
11593 dcb.jsonl |
|
11512 dda.jsonl |
|
11501 ddb.jsonl |
|
69578 total |
|
``` |
|
|
|
### Curation Rationale |
|
作为中文NLI(natural langauge inference)数据集,这里把这个数据集上传到huggingface的datasets,方便大家使用。 |
|
|
|
#### Who are the source language producers? |
|
数据集的版权归原作者所有,使用各数据集时请尊重原数据集的版权。 |
|
|
|
#### Who are the annotators? |
|
原作者。 |
|
|
|
### Social Impact of Dataset |
|
This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. |
|
|
|
Systems that are successful at such a task may be more successful in modeling semantic representations. |
|
|
|
### Licensing Information |
|
|
|
用于学术研究。 |
|
|
|
|
|
### Contributions |
|
|
|
[shibing624](https://github.com/shibing624) upload this dataset. |