This project implements a text classification system using DistilBERT to categorize customer messages into three categories: feedback, inquiries, and refunds.

Project Structure

• train.py: Script for training the DistilBERT model on the provided dataset.
• classify.py: Script for using the trained model to classify new text inputs.
• requirements.txt: List of Python dependencies required for the project.

Setup

1. Clone this repository:

   git clone https://github.com/jpe90/fine-tuned-distilbert-classifier.git
   cd fine-tuned-distilbert-classifier
   

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Data Preparation

Training data is located in the data directory:

• feedback.txt: Contains customer feedback messages
• inquiries.txt: Contains customer inquiry messages
• refunds.txt: Contains customer refund request messages

Each file contains one message per line.

Training the Model

To train the model, run:

python train.py

Using the Classifier

To use the trained model for classification, run:

python classify.py