README.md

---
language:
- en
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: Scikit-learn GitHub Issues (Multilabel)
license: apache-2.0
---

# 🧩 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. Retrieved issues up to API safety limits  
2. Removed pull requests  
3. Downloaded all issue comments  
4. Exploded issues by comments  
5. Constructed a unified `text` field:
   - title  
   - body  
   - comments  
6. 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 | Description |
|------|------------|
| `html_url` | GitHub issue URL |
| `labels` | List of labels (multilabel target) |
| `text` | Issue title + body + comments |
| `issue_number` | Original GitHub issue number |

> 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.