Skip to content

Latest commit

 

History

History
31 lines (22 loc) · 1.43 KB

README.md

File metadata and controls

31 lines (22 loc) · 1.43 KB

PII Data Detection

This repository contains the source code and resources for an NER project aimed at detecting PII Data. The project is based on the Kaggle competition and utilizes a fine-tuned DeBERTa V3 base model to achieve its goal.

Project Structure

  • data/: Contains training and test datasets in json format. The training and test set provided are placeholders and should be replaced for actual use.
  • model_checkpoints/: Stores trained models' checkpoints.
  • EDA.ipynb: Jupyter notebook for exploratory data analysis on the training set.
  • model_training.py: Fine-tunes the pre-trained DeBERTa V3 base model on the training set and saves the checkpoint to the model_checkpoints/ folder.
  • inference.py: Loads fine-tuned DeBERTa model to make predictions on the test set.

Setup

  1. Clone the Repository: 
    git clone https://github.com/q-xZzz/pii-data-detection.git
  2. Install Dependencies: 
    pip install -r requirements.txt

Running the Project

To get the project up and running, follow these steps:

  • (optional)Exploratory Data Analysis: Open EDA.ipynb with Jupyter Notebook or JupyterLab to explore the training dataset.
  • Model Training:
    • Run python model_training.py.
  • Inference:
    • Run python inference.py to load the model checkpoints and use it to make predictions on the test set.