Datasets:
File size: 1,754 Bytes
79aee66 d3eeacb 79aee66 d3eeacb 79aee66 d3eeacb 79aee66 fc55c9d 79aee66 e3539d4 79aee66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
---
language:
- id
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- feature-extraction
- sentence-similarity
tags:
- sentence-transformers
pretty_name: STSB-indo-mt
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/stsb.train.*
- split: validation
path: data/stsb.validation.*
- split: test
path: data/stsb.test.*
---
# Dataset Card for STSB
This dataset is sourced from the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) repository.
The content has been translated using DeepL machine translation.
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': 'Seorang pria sedang memainkan seruling besar.',
'sentence2': 'Seorang pria sedang memainkan seruling.',
'score': 0.76,
}
```
* Collection strategy: Reading the sentences and score from STSB dataset and dividing the score by 5.
* Deduplified: No |