Talip7/scikit-learn-issues-multilabel

🧩 Scikit-learn GitHub Issues – Multilabel Dataset

This dataset contains GitHub issues from the `scikit-learn` repository, prepared for multilabel NLP tasks such as issue tagging, automated triage, and semantic search.

Each row corresponds to one issue-comment context, making the dataset suitable for real-world developer tooling.

📌 Motivation

GitHub issues are a critical signal in open-source projects:

• —Bug tracking
• —Feature requests
• —Documentation improvements
• —Module-specific discussions

This dataset enables:

• —Multilabel text classification
• —Label recommendation systems
• —Semantic search over issues
• —Downstream LLM & RAG pipelines

📦 Dataset Construction

Source

• —Repository: scikit-learn/scikit-learn
• —Collected using the GitHub REST API

Included

• —Open & closed issues
• —Issue title + body
• —All comments
• —Original GitHub labels

Excluded

• —Pull requests

🔄 Preprocessing Pipeline

1. 1.Retrieved issues up to API safety limits
2. 2.Removed pull requests
3. 3.Downloaded all issue comments
4. 4.Exploded issues by comments
5. 5.Constructed a unified text field:
6. 6.title
7. 7.body
8. 8.comments
9. 9.Extracted label names into a multilabel format

📊 Dataset Overview

• —Samples: ~12,500
• —Labels per sample: 1–6
• —Unique labels: ~20+
• —Language: English

Example labels:

• —Bug
• —Documentation
• —New Feature
• —module:linear_model
• —Build / CI
• —Needs Triage

🧱 Dataset Columns

Column types are inferred automatically from the dataset files.

🚀 Intended Use

• —Multilabel classification
• —Issue triage automation
• —Semantic search
• —Developer-facing ML tools

⚠️ Limitations

• —Natural class imbalance
• —Domain-specific to scikit-learn
• —Label taxonomy evolves over time

👤 Author

Talip7 Focused on applied NLP, real-world datasets, and production ML pipelines.