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---
title: Resume Screener and Skill Extractor
emoji: 📄
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.31.0
app_file: app.py
pinned: false
license: mit
---
# Resume Screener and Skill Extractor
A Hugging Face Space application for efficiently screening resumes against job descriptions using a hybrid ranking approach that combines semantic similarity with keyword-based scoring.
## Features
- **Hybrid Resume Ranking**: Combines semantic similarity (via NV-Embed-v2) with keyword-based BM25 scoring
- **Skill Extraction**: Automatically identifies relevant skills from resumes based on job requirements
- **Fast Search**: Uses FAISS for efficient similarity search with large resume collections
- **Multi-format Support**: Processes PDFs, DOCX, TXT, and CSV files
- **Explanation Generation**: Provides explanations for why each resume was ranked highly
- **Visualization**: Displays comparative scores and key matches for easy analysis
- **Batch Processing**: Supports uploading multiple resumes simultaneously
## How It Works
1. **Input**: Provide a job description and upload resumes (PDF, DOCX, TXT, or CSV format)
2. **Processing**: The system creates embeddings for both the job description and resumes using the NV-Embed-v2 model
3. **Ranking**: Calculates a hybrid score based on:
- Semantic similarity (cosine similarity between embeddings)
- Keyword relevance (BM25 scoring)
4. **Results**: Returns the top 10 most suitable resumes with:
- Overall score and individual component scores
- Matched skills and key phrases
- Explanations for why each resume was ranked highly
## Technical Details
### Models Used
- **NV-Embed-v2**: State-of-the-art embedding model for semantic similarity
- **QwQ-32B**: Used for generating explanations (simulated in the current version)
### Libraries
- **FAISS**: Facebook AI Similarity Search for fast vector similarity search
- **rank_bm25**: Implementation of the BM25 algorithm for keyword-based scoring
- **Streamlit**: For the user interface
- **Hugging Face Transformers**: For accessing and using the models
## Configuration Options
The sidebar provides several configuration options:
- **Model Selection**: Choose which embedding model to use
- **Ranking Weights**: Adjust the balance between semantic similarity and keyword matching
- **Results Count**: Set how many top results to display
- **FAISS Usage**: Toggle the use of FAISS for faster searching with large resume collections
## Getting Started
### Online Usage
1. Visit the Hugging Face Space at [URL]
2. Enter a job description
3. Upload resumes (PDF, DOCX, TXT, or CSV)
4. Click "Find Top Candidates"
5. Review the results
### Local Installation
```bash
git clone https://huggingface.co/spaces/[username]/Resume_Screener_and_Skill_Extractor
cd Resume_Screener_and_Skill_Extractor
pip install -r requirements.txt
streamlit run app.py
```
## Future Enhancements
- Integration with Hugging Face datasets for loading resumes directly
- Enhanced skill extraction using more sophisticated NLP techniques
- Real-time explanation generation using QwQ-32B
- Support for additional file formats and languages
- Customizable scoring algorithms and weights
## License
MIT License
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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