README.md

---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: Quora Duplicate Questions
tags:
- sentence-transformers
dataset_info:
- config_name: pair
  features:
  - name: anchor
    dtype: string
  - name: positive
    dtype: string
  splits:
  - name: train
    num_bytes: 19063882.986566573
    num_examples: 149263
  download_size: 10710908
  dataset_size: 19063882.986566573
- config_name: pair-class
  features:
  - name: sentence1
    dtype: string
  - name: sentence2
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': different
          '1': duplicate
  splits:
  - name: train
    num_bytes: 54870273
    num_examples: 404290
  download_size: 34965546
  dataset_size: 54870273
- config_name: triplet
  features:
  - name: anchor
    dtype: string
  - name: positive
    dtype: string
  - name: negative
    dtype: string
  splits:
  - name: train
    num_bytes: 17575186
    num_examples: 101762
  download_size: 10954551
  dataset_size: 17575186
- config_name: triplet-all
  features:
  - name: anchor
    dtype: string
  - name: positive
    dtype: string
  - name: negative
    dtype: string
  splits:
  - name: train
    num_bytes: 483971801
    num_examples: 2792280
  download_size: 104682424
  dataset_size: 483971801
configs:
- config_name: pair
  data_files:
  - split: train
    path: pair/train-*
- config_name: pair-class
  data_files:
  - split: train
    path: pair-class/train-*
- config_name: triplet
  data_files:
  - split: train
    path: triplet/train-*
- config_name: triplet-all
  data_files:
  - split: train
    path: triplet-all/train-*
---

# Dataset Card for Quora Duplicate Questions

This dataset contains the [Quora](https://huggingface.co./datasets/quora) Question Pairs dataset in four formats that are easily used with Sentence Transformers to train embedding models. The data was originally created by Quora for [this Kaggle Competition](https://www.kaggle.com/c/quora-question-pairs).

## Dataset Subsets

### `pair-class` subset

* Columns: "sentence1", "sentence2", "label"
* Column types: `str`, `str`, `class` with `{"0": "different", "1": "duplicate"}`
* Examples:
    ```python
    {
      'sentence1': 'What is the step by step guide to invest in share market in india?',
      'sentence2': 'What is the step by step guide to invest in share market?',
      'label': 0,
    }
   ```
* Collection strategy: A direct copy of [Quora](https://huggingface.co./datasets/quora), but with more conveniently parsable columns.
* Deduplified: No

### `pair` subset

* Columns: "anchor", "positive"
* Column types: `str`, `str`
* Examples:
    ```python
    {
      'anchor': 'Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?',
      'positive': "I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?",
    }
    ```
* Collection strategy: Filtering away the "different" options from the `pair-class` subset, removing the label column, and renaming the columns.
* Deduplified: No

### `triplet-all` subset

* Columns: "anchor", "positive", "negative"
* Column types: `str`, `str`, `str`
* Examples:
    ```python
    {
      'anchor': 'Why in India do we not have one on one political debate as in USA?",
      'positive': 'Why cant we have a public debate between politicians in India like the one in US?',
      'negative': 'Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?',
    }
    ```
* Collection strategy: Taken from [embedding-training-data](https://huggingface.co./datasets/sentence-transformers/embedding-training-data), which states: "Duplicate question pairs from Quora with additional hard negatives (mined & denoised by cross-encoder)". Then, take all possible triplet pairs.
* Deduplified: No

### `triplet` subset

* Columns: "anchor", "positive", "negative"
* Column types: `str`, `str`, `str`
* Examples:
    ```python
    {
      'anchor': 'Why in India do we not have one on one political debate as in USA?",
      'positive': 'Why cant we have a public debate between politicians in India like the one in US?',
      'negative': 'Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?',
    }
    ```
* Collection strategy: Taken from [embedding-training-data](https://huggingface.co./datasets/sentence-transformers/embedding-training-data), which states: "Duplicate question pairs from Quora with additional hard negatives (mined & denoised by cross-encoder)". Then, take the anchor, positive and the first negative of each sample.
* Deduplified: No