Findup-ML

Findup-ML is a machine learning project designed to handle various tasks related to resume (CV) processing and evaluation. This repository includes tools and models for parsing CVs, scoring them, and measuring text similarity.

Features

1. CV Parser

• Purpose: Extracts and processes text from CVs.
• Implementation: Includes a notebook parsing_text_model.ipynb that demonstrates the parsing process.

2. CV Scoring

• Purpose: Scores CVs based on defined metrics or criteria.
• Implementation: The scoring logic is implemented in the notebook CV_Scoring_Model.ipynb.

3. Text Similarity

• Purpose: Measures the similarity between two pieces of text, useful for matching job descriptions with CV content.
• Implementation: The notebook Text_Similarity_Model.ipynb showcases the similarity computation.

Project Structure

Findup-ML/
|
|-- .git/                     # Git repository metadata
|-- CV Parser/                # Contains CV parsing logic
|   |-- parsing_text_model.ipynb
|   |-- README.md
|
|-- CV Scoring/               # Contains CV scoring logic
|   |-- CV_Scoring_Model.ipynb
|
|-- Text Similarity/          # Contains text similarity logic
    |-- Text_Similarity_Model.ipynb

Setup Instructions

1. Clone the Repository:

   git clone https://github.com/Timnas-Bangkit/API-CC.git
   cd Findup-ML

2. Install Dependencies: Ensure you have Python and Jupyter Notebook installed. Use the following command to install dependencies:

   pip install -r requirements.txt

3. Run Notebooks: Open each notebook in the corresponding folder and execute the cells to see the models in action.

   Example:

   jupyter notebook CV\ Parser/parsing_text_model.ipynb

Usage

1. CV Parser:

   • Load a CV file into the notebook.
   • Run the notebook to parse and extract key information.

2. CV Scoring:

   • Input a CV and the required scoring parameters.
   • Run the notebook to obtain a score for the CV.

3. Text Similarity:

   • Input two pieces of text (e.g., job description and CV content).
   • Run the notebook to compute the similarity score.

Contributing

Contributions are welcome! Please create a pull request with detailed explanations of the proposed changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.