metadata

language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
task_categories:
  - feature-extraction
  - sentence-similarity
tags:
  - sentence-transformers
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:

{
  '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