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---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: Wikipedia Sections
tags:
- sentence-transformers
dataset_info:
- config_name: pair
features:
- name: anchor
dtype: string
- name: positive
dtype: string
splits:
- name: train
num_bytes: 490913561
num_examples: 1779417
- name: validation
num_bytes: 60891304
num_examples: 220400
- name: test
num_bytes: 61385426
num_examples: 222957
download_size: 295222520
dataset_size: 613190291
- config_name: triplet
features:
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 733058519
num_examples: 1779417
- name: validation
num_bytes: 90881953
num_examples: 220400
- name: test
num_bytes: 91705993
num_examples: 222957
download_size: 500545462
dataset_size: 915646465
configs:
- config_name: pair
data_files:
- split: train
path: pair/train-*
- split: validation
path: pair/validation-*
- split: test
path: pair/test-*
- config_name: triplet
data_files:
- split: train
path: triplet/train-*
- split: validation
path: triplet/validation-*
- split: test
path: triplet/test-*
---
# Dataset Card for Wikipedia Sections
This dataset contains pairs and triplets that can be used to train and finetune Sentence Transformer embedding models. The dataset originates from [Dor et al.](https://aclanthology.org/P18-2009.pdf), and was downloaded from [this download link](https://sbert.net/datasets/wikipedia-sections-triplets.zip).
Notably, the "anchor" column contains sentences from Wikipedia, wheras the "positive" column contains other sentences from the same section. The "negative" column contains sentences from other sections.
## Dataset Subsets
### `pair` subset
* Columns: "anchor", "positive"
* Column types: `str`, `str`
* Examples:
```python
```
* Collection strategy: Reading the Wikipedia Sections dataset from https://sbert.net.
* Deduplified: Yes
### `triplet` subset
* Columns: "anchor", "positive", "negative"
* Column types: `str`, `str`, `str`
* Examples:
```python
```
* Collection strategy: Reading the Wikipedia Sections dataset from https://sbert.net.
* Deduplified: Yes |