stsb

# Dataset Card for STSB

The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data.
Each pair is human-annotated with a similarity score from 1 to 5. However, for this variant, the similarity scores are normalized to between 0 and 1.

## Dataset Details

* Columns: "sentence1", "sentence2", "score"
* Column types: `str`, `str`, `float`
* Examples:
```python
{
'sentence1': 'A man is playing a large flute.',
'sentence2': 'A man is playing a flute.',
'score': 0.76,
}
```
* Collection strategy: Reading the sentences and score from STSB dataset and dividing the score by 5.
* Deduplified: No

# STSB 数据集卡片
语义文本相似度基准（Semantic Textual Similarity Benchmark，Cer等人，2017）是一组源自新闻标题、视频与图像标题及自然语言推理数据的句子对集合。每一组句子对均由人工标注1至5分的相似度得分。但在此数据集变体中，相似度得分已被归一化至0至1区间。

## 数据集详情
* 字段："sentence1"、"sentence2"、"score"
* 字段类型：字符串、字符串、浮点数
* 示例：
python
{
'sentence1': 'A man is playing a large flute.',
'sentence2': 'A man is playing a flute.',
'score': 0.76,
}

* 数据采集策略：从STSB数据集读取句子与得分，并将得分除以5完成归一化。
* 去重情况：否

搜集汇总

背景与挑战

背景概述

STSB数据集是一个语义文本相似度基准，包含从新闻标题、视频字幕等来源收集的句子对，每对由人工标注相似度分数，但此版本将原始1-5分的评分归一化到0-1之间。数据包括两个句子列和一个浮点数分数列，通过读取原始数据并除以5实现归一化处理。