|
--- |
|
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 |
