---
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