https://github.com/rahulvictor12/resume-scoring-using-nlp-and-sbert-transformer
ResumeScorer is an intelligent resume screening tool that leverages Natural Language Processing (NLP) techniques to assess the relevance of resumes against a given job description. The core idea is to reduce manual screening time and improve candidate-job matching through semantic similarity.
https://github.com/rahulvictor12/resume-scoring-using-nlp-and-sbert-transformer
cosine-similarity embeddings-extraction lemmitization nlp-keywords-extraction nlp-machine-learning pdfplumber sbert
Last synced: 5 months ago
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ResumeScorer is an intelligent resume screening tool that leverages Natural Language Processing (NLP) techniques to assess the relevance of resumes against a given job description. The core idea is to reduce manual screening time and improve candidate-job matching through semantic similarity.
- Host: GitHub
- URL: https://github.com/rahulvictor12/resume-scoring-using-nlp-and-sbert-transformer
- Owner: rahulvictor12
- Created: 2025-04-01T10:15:08.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-04-05T05:46:53.000Z (6 months ago)
- Last Synced: 2025-04-05T06:26:29.313Z (6 months ago)
- Topics: cosine-similarity, embeddings-extraction, lemmitization, nlp-keywords-extraction, nlp-machine-learning, pdfplumber, sbert
- Language: Jupyter Notebook
- Homepage:
- Size: 31.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ResumeScorer
ResumeScorer is a Python-based application designed to automate the evaluation of resumes. By extracting relevant information from resumes and comparing it against predefined criteria, the tool assigns a score to each resume, aiding recruiters in the screening process.
## Features
- **Resume Parsing**: Utilizes natural language processing techniques to extract key information such as skills, experience, and education from resumes.
- **Scoring Mechanism**: Compares extracted information against predefined criteria to assign a relevance score to each resume.
- **Data Extraction Modules**: Modular design for extracting data, allowing easy customization and extension.
- **Helper Methods**: Utility functions to support data processing and scoring operations.
## Project Structure
The repository is organized as follows:
- `.idea/`: IDE-specific settings and configurations.
- `Contracts/`: Defines interfaces and contracts used across the application.
- `DataExtractors/`: Modules responsible for extracting data from resumes.
- `DependencyInjection/`: Manages dependencies and their injections throughout the application.
- `HelperMethods/`: Contains utility functions to support various operations.
- `Models/`: Data models representing the structure of extracted information.
- `data/`: Directory intended for storing sample resumes and related data files.
- `RequirementInstaller.ipynb`: Jupyter Notebook for installing required dependencies.
- `ResumeScreener.ipynb`: Jupyter Notebook demonstrating the resume screening process.
- `ResumeScreener.py`: Main script to run the resume screening application.
- `app.py`: Entry point for the application.
- `requirements.txt`: Lists all Python dependencies required to run the application.
## Installation
To set up the ResumeScorer application on your local machine, follow these steps:
1. **Clone the Repository**:
```bash
git clone https://github.com/rahulvictor12/ResumeScorer.git
2. **Navigate to the Project Directory:**
cd ResumeScorer
3. **Create a Virtual Environment (optional but recommended):**
python3 -m venv venv
source venv/bin/activate # On Windows, use 'venv\Scripts\activate'
4. **Install Dependencies:**
pip install -r requirements.txt
## Usage
After installation, you can use the ResumeScorer application as follows:
Import and Install neccesary Dependencies.
Run the Application: python app.py
Upload the Job Description and Resume You wish to score.
This will process the resume in the ResumeScreener.py and output scores based on the predefined criteria.
## Contributing
Contributions to ResumeScorer are welcome! To contribute:
1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes and commit them with descriptive messages.
4. Push your changes to your fork.
5. Submit a pull request to the main repository.
## Acknowledgments
We appreciate the contributions of all developers and maintainers of the open-source libraries used in this project.
This `README.md` provides a comprehensive overview of the ResumeScorer project, including its features, structure, installation instructions, usage guidelines, contribution steps, license information, and acknowledgments.
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