README.md

---
license: mit
task_categories:
- question-answering
- sentence-similarity
language:
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for "NLQuAD"

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)

## Dataset Description

- **Homepage:** [https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb](https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb)


### Dataset Summary

This is a simplified version of [vblagoje's](https://huggingface.co/vblagoje) [lfqa_support_docs](https://huggingface.co/datasets/vblagoje/lfqa_support_docs) and [lfqa](https://huggingface.co/datasets/vblagoje/lfqa) datasets.
It was generated by me to have a more straight forward way to train Seq2Seq models on context based long form question answering tasks.

## Dataset Structure

### Data Instances

An example of 'train' looks as follows.

```json
{
    "question": "Khashoggi murder: Body 'dissolved in acid'",
    "answer": "2 November 2018",
    "context": [

    ]
}
```

### Data Fields

The data fields are the same among all splits.

- `question`: a `string` feature.
- `answer`: a `string` feature.
- `context`: a list feature containing `string` features.

### Data Splits

|   name   |train|test|validation|
|----------|----:|----:|---------:|
|          |10259| 1280|     1280|


## Additional Information

### Licensing Information

This dataset is distributed under the MIT licence.