Emotions in Text Classification with BERT

This project aims to classify emotions in text using a DistilBERT-based model. It includes both a Jupyter notebook for training and a Django API for serving predictions.

Getting Started

Follow the steps below to set up and run the project.

Prerequisites

• Python 3.10.3 (Anaconda or Miniconda recommended)
• Jupyter Notebook
• Git

Installation

1. Clone the repository:

   git clone https://github.com/FotieMConstant/emotions-in-text-classification.git
   cd emotions-in-text-classification

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

   conda create --name myenv python=3.10.3
   conda activate myenv

3. Install project dependencies:

   pip install -r requirements.txt

Training the Model

1. Run the Jupyter Notebook:

   jupyter notebook

2. Open and run the train_model.ipynb notebook. This will generate the production model (dbert_model.h5).

Running the Django API

1. Navigate to the Django API directory:

   cd emotion_api

2. Run the Django development server:

   python manage.py runserver

3. The API will be accessible at http://localhost:8000/api/predict/.

Usage

You can make predictions by sending a POST request to the API endpoint with the text you want to classify.

Example using curl:

curl -X POST -H "Content-Type: application/json" -d '{"text": "Your text here"}' http://localhost:8000/api/predict/

• You can equally just use postman to make the request

License

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