Mining Resume

This project provides a powerful toolkit for extracting structured information from resume files. It supports various file formats and offers two distinct parsing methodologies: a highly customizable rule-based approach using Regex, and a modern, AI-driven approach using a Large Language Model (LLM).

The primary objective is to convert unstructured resume text from formats like .pdf, .docx, and .txt into a clean, structured JSON output.

✨ Features

• Multi-Format Support: Parses resumes from .pdf, .docx, and .txt files seamlessly.
• Dual Parsing Engines:
  • Regex-Based Parser: Offers granular control over data extraction through a simple and powerful XML configuration. Ideal for resumes with consistent formatting.
  • LLM-Based Parser: Leverages a pre-trained Large Language Model (google/flan-t5-large) to intelligently identify and extract information, adapting well to varied resume layouts.
• Structured Output: Consistently outputs extracted data in a clean, easy-to-use JSON format.
• Customizable Extraction:
  • For the Regex parser, you can define your own extraction rules in regex_config.xml without changing any Python code.
  • For the LLM parser, the list of attributes to be extracted can be easily modified in the script.

📂 Src Project Structure

.  
├── data/  
│   └── YogeshKulkarniLinkedInProfile.pdf  \# Place your resume files here  
├── llm\_resume\_parser.py                   \# Main script for LLM-based parsing  
├── regex\_resume\_parser.py                 \# Main script for Regex-based parsing  
├── regex\_config.xml                       \# Configuration file for the Regex parser  
└── README.md

🚀 Getting Started

Follow these instructions to set up and run the project on your local machine.

Prerequisites

• Python 3.8+
• For the LLM Parser: A Hugging Face API Token

Installation

1. Clone the repository:

   git clone \<your-repository-url\>  
   cd \<your-repository-name\>

2. Create and activate a virtual environment (recommended):

   \# For Windows  
   python \-m venv venv  
   .\\venv\\Scripts\\activate

   \# For macOS/Linux  
   python3 \-m venv venv  
   source venv/bin/activate

3. Install the required dependencies:
   Create a requirements.txt file with the following content:

   PyPDF2  
   docx2txt  
   langchain  
   langchain-community  
   langchain-huggingface  
   transformers  
   torch  
   sentencepiece
   

   Then, install the packages:

   pip install \-r requirements.txt

4. Set up Environment Variables (for LLM Parser):
   The LLM parser requires a Hugging Face API token to interact with the model hub.
   Create a .env file in the root directory and add your token:

   HUGGINGFACEHUB\_API\_TOKEN="your\_hf\_token\_here"
   

   The script will load this variable automatically.

🏃 How to Run

Before running either parser, place the resume files you want to process inside the data folder. The scripts are pre-configured to run with a sample file named YogeshKulkarniLinkedInProfile.pdf.

1. Regex-Based Parser

This method uses the patterns defined in regex_config.xml to extract information.

• Customize (Optional): Open regex_config.xml to review or modify the regex patterns for each field you want to extract.
• Run the script:

  python regex\_resume\_parser.py

The script will process the sample resume and print the extracted structured data as a JSON object to the console.

2. LLM-Based Parser

This method uses a Hugging Face model to understand the context and extract the required fields.

• Ensure your API token is set as described in the installation steps.
• Run the script:

  python llm\_resume\_parser.py

The script will download the model (on the first run), process the sample resume, and print the extracted data in JSON format to the console.

⚙️ Configuration

The Regex-Based Parser is controlled by the regex_config.xml file. This file allows you to define:

• Terms: The specific fields to extract (e.g., Name, Email, PhoneNumber).
• Methods: The extraction logic to use (e.g., univalue_extractor for single values).
• Patterns: The specific regex patterns used to find the information.

This design allows for easy adaptation to different resume formats or extraction requirements without modifying the Python source code.

📜 Disclaimer

The author provides no guarantee for the program's results. This is a utility script with room for improvement. Do not depend on it entirely for critical applications.

Copyright (C) 2025 Yogesh H Kulkarni