Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
multi-label-classification
Languages:
English
Size:
10K - 100K
License:
File size: 2,371 Bytes
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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.
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