Update README.md

README.md

@@ -7,4 +7,63 @@ language:
 - en
 size_categories:
 - 100K<n<1M
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+---
+# 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.