https://github.com/ananthakrishnan12/resume-analyzer-using-bert
Resume Analyzer Using BERT
https://github.com/ananthakrishnan12/resume-analyzer-using-bert
bert-embeddings bert-model cosine-similarity nlp-parsing pdf pdftotext pymupdf python3 spacy-nlp streamlit transformers
Last synced: 2 months ago
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Resume Analyzer Using BERT
- Host: GitHub
- URL: https://github.com/ananthakrishnan12/resume-analyzer-using-bert
- Owner: Ananthakrishnan12
- Created: 2025-03-15T11:56:05.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-15T12:31:37.000Z (2 months ago)
- Last Synced: 2025-03-15T13:19:28.261Z (2 months ago)
- Topics: bert-embeddings, bert-model, cosine-similarity, nlp-parsing, pdf, pdftotext, pymupdf, python3, spacy-nlp, streamlit, transformers
- Language: Python
- Homepage:
- Size: 8.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📄 AI Powered Resume Analyzer Using BERT - Transformer
This project is an AI-powered resume analyzer that uses BERT (Bidirectional Encoder Representations from Transformers) to compare a candidate's resume with a job description and provide a similarity score. The application is built using **Streamlit** for a user-friendly interface.
---
## 🚀 Features
- 📤 Upload your resume in PDF format
- 📝 Enter the job description text
- 🧠 Extracts and processes text from the resume
- 🔍 Uses BERT to generate embeddings for both resume and job description
- 📊 Computes similarity score based on **cosine similarity**
- 📈 Displays a **matching percentage** to help candidates assess their suitability
---
## 🛠️ Installation
### 1️⃣ Clone the repository
```bash
git clone https://github.com/Ananthakrishnan12/Resume-Analyzer-Using-BERT.git
cd Resume-Analyzer-Using-BERT
```
### 2️⃣ Create a virtual environment (Optional but recommended)
```bash
python -m venv CV_parser
source CV_parser/bin/activate # On macOS/Linux
CV_parser\Scripts\activate # On Windows
```
### 3️⃣ Install dependencies
```bash
pip install -r requirements.txt
```
---
## 📌 How to Run
Run the Streamlit app:
```bash
streamlit run main.py
```
---
## 🏗️ Tech Stack
- **Python** 🐍
- **Streamlit** 🎨 (for UI)
- **PyMuPDF (fitz)** 📄 (for PDF text extraction)
- **Transformers** 🤗 (BERT-based embeddings)
- **Torch (PyTorch)** 🔥 (for embedding computations)
---
## 🔍 How It Works
1. **Resume Text Extraction**
- The uploaded PDF file is processed using **PyMuPDF (fitz)** to extract text.
2. **BERT Embedding Generation**
- The extracted resume text and job description are tokenized and converted into embeddings using **BERT**.
3. **Cosine Similarity Computation**
- The similarity score between resume and job description embeddings is calculated using **cosine similarity**.
4. **Result Display**
- The application displays the extracted resume text (preview), job description, and **matching score** as a percentage.
---
## 📷 DEMO (Wait few seconds for loading....)
---