---
dataset_info:
  features:
  - name: html_url
    dtype: string
  - name: title
    dtype: string
  - name: comments
    sequence: string
  - name: body
    dtype: string
  - name: number
    dtype: int64
  splits:
  - name: train
    num_bytes: 10108947
    num_examples: 2893
  download_size: 4360781
  dataset_size: 10108947
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- en
size_categories:
- 1M<n<10M
---

---
## Title: GitHub Issues Dataset for Semantic Search
---

## Dataset Summary

The differentiator is that this dataset is filtered and have selected fields that is it is a direct dataset which can be used to create sementic serach engine to resolve user quries on hf datasets repo, it doesn't have those issues which are PRs and also relevant fields only required to create engine. This dataset was created following the last two sections of Chapter 5 from the Hugging Face NLP course. The course outlined steps to create a dataset for building a semantic search system using GitHub issues from the Hugging Face `datasets` repository. However, during the creation process, several challenges were encountered, including handling null values and timestamp-related errors. This dataset was refined by focusing on fields relevant to semantic search, such as `html_url`, `title`, `body`, `comments`, and `issue number`.

### Purpose

The dataset supports the development of an asymmetric semantic search application, which involves short queries and longer paragraphs that address these queries, specifically for issues related to Hugging Face datasets.

## Dataset Info

- **Configuration Name**: default
- **Splits**:
  - Train Split
    - **Number of Bytes**: 10108947
    - **Number of Examples**: 2893
- **Download Size**: 4360781 bytes
- **Total Dataset Size**: 10108947 bytes

## Languages

This dataset is entirely in English, encompassing all titles, bodies, and comments from the issues of the Hugging Face datasets.

## Dataset Structure

This is till now and they are the issues which are not pull requests.

Dataset({
features: ['html_url', 'title', 'comments', 'body', 'number'],
num_rows: 2893
})

## Data Instances

An example data instance looks like this:

{
  "html_url": "https://github.com/huggingface/datasets/issues/7079",
  "title": "HfHubHTTPError: 500 Server Error: Internal Server Error for url:",
  "comments": ["same issue here. ... list of all comments csv"],
  "body": "### Describe the bug\n\nnewly uploaded datasets, since yesterday, yields an error.\r\n\r\n...body describing the issue",
  "number": 7079
} 

 ## Data Fields
 The dataset includes the following fields:

 html_url: URL of the GitHub issue (string).
 title: Title of the issue (string).
 comments: Sequence of comments made on the issue (list of strings).
 body: Detailed description of the issue (string).
 number: GitHub issue number (int64).


To use this data in an environment where transformers are installed , the data can be imported with 2 lines of code -

from datasets import load_dataset

ds = load_dataset("amannagrawall002/github-issues")


## Source Data
The dataset is crafted from scratch using the GitHub REST API, focusing on open issues from the Hugging Face datasets repository.

## Initial Data Collection and Normalization
The creation of this dataset involved handling over 5000 issues, which exceeds the GitHub REST API's rate limit of 5000 requests per hour. Additionally, extracting comments required significant computational resources, highlighting the involvement of both automated processes and manual oversight.

## Considerations
This dataset is tailored for creating a semantic search application centered around GitHub issues. It does not contain data on pull requests, which may limit its applicability for tasks requiring such information.

### Additional Notes

Anyone deeply engaged with the Hugging Face NLP course might attempt to create this dataset. While it's accessible remotely as described in the course, this specific version focuses solely on supporting semantic search applications. Other uses may require a dataset with broader field coverage.
This README is designed to be clear and informative, providing all necessary details about the dataset in a structured manner.

## Author: Aman Agrawal

## Contact: amannagrawall002@gmail.com