README.md

metadata

license: mit
title: 🤗 GitHub Issue Hybrid Search & Auto-Label Assistant
sdk: gradio
colorFrom: blue
colorTo: green
pinned: true
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/68281bec37b367f53ec121da/jbr9Hs4azEMPGbTZ2dYIz.png
short_description: Hybrid semantic search and auto-labeling for GitHub issues
sdk_version: 6.2.0

🤗 GitHub Issue Hybrid Search & Auto-Label Assistant

Precision-first hybrid ranking for real GitHub issues using Semantic Search + Multilabel Classification.

This project demonstrates a production-oriented hybrid retrieval system that helps with GitHub issue triage by combining:

• Dense semantic search (MPNet embeddings + FAISS)
• Multilabel text classification (DistilBERT)
• A hybrid ranking strategy that fuses semantic similarity and label consistency

A live, interactive demo is available via Hugging Face Spaces.

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🚀 Live Demo

🔗 Hugging Face Space:
GitHub Issue Hybrid Search & Auto-Label Assistant

Users can describe a GitHub issue in natural language and instantly:

• See predicted issue labels (e.g. Bug, Needs Triage)
• Retrieve the most relevant existing GitHub issues
• Inspect semantic similarity, label overlap, and final hybrid scores
• Open the original GitHub issues directly

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🔍 Problem Motivation

Large open-source repositories receive thousands of issues, making it hard to:

• Find similar historical issues
• Detect duplicates
• Assign correct labels early
• Route issues to the right maintainers

Keyword search alone is often insufficient.
This project addresses that gap with semantic + label-aware retrieval.

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🧠 System Overview

The pipeline consists of four main stages:

1. Semantic Encoding

User queries are encoded using dense sentence embeddings:

• Model: sentence-transformers/all-mpnet-base-v2

Issue texts in the dataset are pre-embedded and stored for fast retrieval.

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2. Semantic Retrieval

• Index: FAISS (runtime-built)
• Retrieves the nearest issues based on vector similarity
• Optimized for dense semantic matching rather than keywords

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3. Multilabel Classification

A fine-tuned DistilBERT model predicts issue labels:

• Example labels: Bug, Needs Triage, module:ensemble, module:tree
• Multiple labels can be assigned per issue
• Outputs confidence-based label predictions

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4. Hybrid Ranking (Key Contribution)

Semantic similarity alone is not always enough.
This system uses a hybrid scoring function: final_score = α · semantic_similarity + β · label_overlap

• Semantic similarity: how close the issue texts are in embedding space
• Label overlap: how well predicted labels match existing issue labels
• α / β: tunable weights (precision-first by design)

Issues are also deduplicated by issue number to avoid repeated results.

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🎯 Design Principles

Precision-First Retrieval

• The system may return fewer than k results intentionally
• It avoids hallucinating weakly related issues
• Returning 1–4 highly relevant issues is considered a success

Runtime FAISS Indexing

• FAISS indices are created at runtime
• Keeps datasets lightweight and portable on Hugging Face Hub

Transparency

• Scores are explicitly shown:
  • Semantic similarity
  • Label overlap
  • Final hybrid score
• GitHub URLs are fully visible and clickable

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📦 Models & Data

Embedding Model

• sentence-transformers/all-mpnet-base-v2

Multilabel Classifier

• DistilBERT fine-tuned for multilabel issue classification
• Hosted on Hugging Face Hub

Dataset

• Custom GitHub issues dataset (scikit-learn)
• Pre-computed embeddings stored on Hugging Face Hub

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🧪 Example Use Cases

• GitHub issue triage
• Bug deduplication
• Support ticket analysis
• Internal engineering knowledge search
• Maintainer productivity tools

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🛠 Tech Stack

• Python
• Hugging Face Datasets & Transformers
• Sentence Transformers
• FAISS
• Gradio (UI)
• Hugging Face Spaces

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✨ What This Project Demonstrates

• End-to-end ML system design (not just a model)
• Semantic search at scale
• Multilabel NLP in a real-world setting
• Hybrid ranking strategies
• Practical UX considerations for ML products

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📌 Notes

This project goes beyond tutorial-level demos by focusing on:

• Real datasets
• Production constraints
• Explainable ranking behavior
• Clean, user-facing presentation

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🙌 Acknowledgements

Inspired by real GitHub workflows and the Hugging Face ecosystem.