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---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: STSB
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 755098
num_examples: 5749
- name: validation
num_bytes: 216064
num_examples: 1500
- name: test
num_bytes: 169987
num_examples: 1379
download_size: 720899
dataset_size: 1141149
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# 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 |