JD-CV-Matching

A model that matches a given Job Description with multiple CVs to pick out the best candidates

CV Matcher is an AI-powered tool designed to match resumes (CVs) with job descriptions using advanced Natural Language Processing (NLP) techniques. It evaluates candidates based on skills, experience, education, projects, and achievements, providing a comprehensive score to help recruiters and hiring managers identify the best-fit candidates.

Made for ByteRunners Hackathon hosted by Griphic in IIT Delhi.

Presentation Link : https://docs.google.com/presentation/d/1FSIq27ID1_RZ8oERefp5ol6BN1nxKj3e8bi_06Popdw/edit?usp=sharing

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Features

• Skill Extraction: Identifies and matches technical and soft skills from resumes and job descriptions.
• Experience Matching: Evaluates years of experience and seniority levels.
• Education Matching: Recognizes academic qualifications and degrees.
• Semantic Matching: Uses DistilBERT embeddings to compare the semantic similarity between resumes and job descriptions.
• Keyword Matching: Matches exact keywords and their synonyms.
• Seniority Detection: Detects seniority levels (entry, mid, senior, management) from text.
• Skill Depth Analysis: Measures the depth of expertise for each skill mentioned.
• Batch Processing: Processes multiple resumes in bulk and generates a ranked list of candidates. (Not yet implemented)

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How It Works

1. Preprocessing: Extracts text from PDF, DOCX, or plain text files.
2. Section Parsing: Identifies and extracts key sections like Skills, Experience, Education, etc.
3. Skill Extraction: Uses regex patterns and spaCy's NLP pipeline to extract skills. (To-Do : Implement a custom NER model)
4. Embedding Generation: Generates sentence embeddings using DistilBERT.
5. Matching Algorithm: Combines keyword matching, semantic similarity, and embedding-based matching to compute scores.
6. Ranking: Ranks candidates based on their total match score.

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Installation

1. Install Dependencies:

   pip install -r requirements.txt

2. Download Spacy's English Model:

   python -m spacy download en_core_web_sm

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Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Open the app in your browser (usually at http://localhost:8501).

3. Log in or register to access the tool.

4. Upload a job description and candidate CVs.

5. Adjust matching weights and parameters in the sidebar.

6. View the results, including:

-Overall match scores.

-Detailed analysis of each candidate.

-Skills comparison across candidates.

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Dependencies

The project relies on the following Python libraries:

• streamlit: For building the interactive web interface.
• transformers: For using DistilBERT embeddings.
• spacy: For natural language processing tasks like entity recognition.
• numpy and torch: For handling embeddings and numerical computations.
• scikit-learn: For calculating cosine similarity between embeddings.
• PyPDF2 and python-docx: For parsing PDF and DOCX files.
• pandas: For data manipulation and analysis.
• matplotlib and seaborn: For creating visualizations like bar charts and heatmaps.
• sqlite3: For user authentication and data storage.

To install all dependencies, run:

pip install -r requirements.txt