AJB01/github-issues-at1_rev2

Dataset Card for GitHub-Issues_AT1_rev_a

GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository. the datset is being used to create text-classification and text-retirval FAISS model.

Dataset Details

Point of Contact: Adam Bradley

Dataset Description

GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository.
It is intended for educational purposes and can be used for semantic search or multilabel text classification.
The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.

• Curated by: title: "huggingface/datasets" Authors: "https://orcid.org/0000-0003-1727-1045" as found at {https://github.com/huggingface/datasets/blob/main/CITATION.cff}

• Language(s) (NLP): en monolingual

• License: Apache-2.0 MIT

Dataset Sources [optional]

Source: original as published at https://api.github.com/repos/huggingface/datasets/issues. Platform: GitHub

• Repository: https://api.github.com/repos/huggingface/datasets/issues Repository: huggingface/datsets
• Paper: ["Datasets: A Community Library for Natural Language Processing"] https://doi.org/10.48550/arxiv.2109.02846
• DataType: structured JSON via REST API

Uses

Used to Illustrate application of Data_retrieval using GitHub API and data processing including stripping, Regex, NER for compliance to APP regulations

Direct Use

GitHub issues are subject to the licenses of the parent repositories (e.g., Apache 2.0, MIT)

Out-of-Scope Use

Redistribution of modified version of this corpus, requires complaince to the "Attribution" or "ShareAlike" clauses of the original software projects. This data is only licensed for use in research and educational applications.

Dataset Structure

Data Instances

This section describes

Data Fields

Data reproduced from the original GitHub-Issues dataset: Title: The subject line of the issue. Often contains specific project names or error summaries. Body: The main description of the issue containing code blocks, logs, stack traces, and email signatures. Comments: (Often a list of strings) The discussion thread involving multiple users

Metadata: User(login,id,avatar_url, author_association(role), labels, state (open/closed)

Technical Fields: html_url, id/Node_id, number, repository_url

Dataset Creation

Curation Rationale

The dataset is intended for educational purposes and can be used for semantic search or multilabel text classification.

Source Data

Issues and issues resolution associated with all hugging face datasets published under the citation.

Data Collection and Processing

Sent GET Request to GitHub API Handle Pagination Store raw Json repsonses Ammended file with comments Cleaned text - Strip, Regex and NER

Data Schema

Standard GitHub Rest API Schema for issues Includes technical content and adminstrative meta data. Core content Fields: Title(string) Body(string) Comments (list of strings/dicts) is_pull_request(bool) Identity and Asociation Fields: User (Login, ID,Avatar URL)(dict) Author association(string) Assignee/Assignees(dict/string) Metadata: Labels(list) State (strong) Number(int) Milestone(Dict/Null) Technical Traceability: url: html_url: repository_url: nodes_id:(string) Timestamps: created_at: updated_at: closed at:

Who are the source data producers?

The data set is subject to Copyright. content was created by the original project members associa

Annotations [optional]

No annotation

Personal and Sensitive Information

The orginal data, provided for education purposes does include data that might be considered personal, sensitive, or private anonymization process included Regex and NER

Bias, Risks, and Limitations

The data has been extract from the "https://api.github.com/repos/huggingface/datasets/issues". The data was generated as a educational data source to enable the creation of semantic search or multilabel text classification “attribution” and “ShareAlike clauses of the original software projects apply to content. Under APP and GDPR/CCPA regulations, a person has the right to delete their data.

Recommendations

This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. Log content may include some material that may reflect development iteration and could offend.

Citation

[(https://orcid.org/0000-0003-1727-1045),]

BibTeX:

@inproceedings{lhoest-etal-2021-datasets, title = "Datasets: A Community Library for Natural Language Processing", author = "Lhoest, Quentin and Villanova del Moral, Albert and Jernite, Yacine and Thakur, Abhishek and von Platen, Patrick and Patil, Suraj and Chaumond, Julien and Drame, Mariama and Plu, Julien and Tunstall, Lewis and Davison, Joe and {\v{S}}a{\v{s}}ko, Mario and Chhablani, Gunjan and Malik, Bhavitvya and Brandeis, Simon and Le Scao, Teven and Sanh, Victor and Xu, Canwen and Patry, Nicolas and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Delangue, Cl{'e}ment and Matussi{`e}re, Th{'e}o and Debut, Lysandre and Bekman, Stas and Cistac, Pierric and Goehringer, Thibault and Mustar, Victor and Lagunas, Fran{\c{c}}ois and Rush, Alexander and Wolf, Thomas", editor = "Adel, Heike and Shi, Shuming", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-demo.21/", doi = "10.18653/v1/2021.emnlp-demo.21", pages = "175--184", abstract = "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at \url{https://github.com/huggingface/datasets}." } @misc{huggingface_datasets, author = {Hugging Face}, title = {Datasets: A community library for datasets in machine learning}, year = {2021}, howpublished = {\url{https://github.com/huggingface/datasets}}, note = {Accessed: 2026-04-25} }

APA:

Glossary [optional]

Dataset Card Authors [optional]

Adam Bradley

Dataset Card Contact

Adam Bradley