README.md

metadata

annotations_creators:
  - no-annotation
language:
  - en
language_creators:
  - found
license:
  - unknown
multilinguality:
  - multilingual
pretty_name: Hugging Face Github Issues
size_categories:
  - 1K<n<10K
source_datasets:
  - original
tags:
  - hugging
  - spaces
  - issues
task_categories:
  - text-classification
  - text-retrieval
task_ids:
  - multi-class-classification
  - multi-label-classification
  - document-retrieval
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: number
      dtype: int64
    - name: title
      dtype: string
    - name: state
      dtype: string
    - name: created_at
      dtype: timestamp[s]
    - name: updated_at
      dtype: timestamp[s]
    - name: closed_at
      dtype: timestamp[s]
    - name: html_url
      dtype: string
    - name: pull_request
      struct:
        - name: url
          dtype: string
        - name: html_url
          dtype: string
        - name: diff_url
          dtype: string
        - name: patch_url
          dtype: string
        - name: merged_at
          dtype: timestamp[s]
    - name: user_login
      dtype: string
    - name: is_pull_request
      dtype: bool
    - name: comments
      list: string
  splits:
    - name: train
      num_bytes: 18163393
      num_examples: 5000
  download_size: 6138126
  dataset_size: 18163393
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Hugging Face Github Issues

This dataset contains 5000 GitHub issues collected from Hugging Face repositories.
It includes issue metadata, content, labels, user information, timestamps, and comments.
The dataset is suitable for text classification, multi-label classification, and document retrieval tasks.

Dataset Structure

Columns

• id — Internal ID of the issue (int64)
• number — GitHub issue number (int64)
• title — Title of the issue (string)
• state — Issue state: open/closed (string)
• created_at — Timestamp when the issue was created (timestamp[s])
• updated_at — Timestamp when the issue was last updated (timestamp[s])
• closed_at — Timestamp when the issue was closed (timestamp[s])
• html_url — URL to the GitHub issue (string)
• pull_request — Struct containing PR info (if the issue is a PR):
  • url — URL to PR
  • html_url — HTML URL of PR
  • diff_url — Diff URL
  • patch_url — Patch URL
  • merged_at — Merge timestamp (timestamp[s])
• user_login — Login of the issue creator (string)
• is_pull_request — Whether the issue is a pull request (bool)
• comments — List of comments on the issue (list[string])

Splits

• train — 5000 examples

Supported Tasks

• Text Classification: Predict labels or categories of issues
• Multi-label Classification: Issues may have multiple labels
• Document Retrieval: Retrieve relevant issues based on a query

Languages

• English

Dataset Creation

The dataset was collected using the GitHub API, including all issue metadata and comments.

Usage Example

from datasets import load_dataset

dataset = load_dataset("cicboy/github-issues", split="train")

# Preview first 5 examples
for i, example in enumerate(dataset[:5]):
    print(f"Issue #{example['number']}: {example['title']}")
    print(f"Created at: {example['created_at']}, Closed at: {example['closed_at']}")
    print(f"User: {example['user_login']}, PR: {example['is_pull_request']}")
    print(f"Comments: {example['comments'][:3]}")  # first 3 comments
    print()

##Citation

@misc{cicboy_github_issues,
  author = {Cicboy},
  title = {Hugging Face Github Issues Dataset},
  year = {2025},
  howpublished = {\url{https://huggingface.co/datasets/cicboy/github-issues}}
}