msg4naresh/transformer_text_classifiacation

Fake news Classification using Transformer based model

Fake News Classifier Using Transformers and Web App using FastAPI

This project aims to build a Transformer Based Learning model for classifying fake news articles. We use FastAPI to serve the model as a web application.

Table of Contents

• Requirements
• Installation
  • Create a Virtual Environment
  • Activate the Virtual Environment
  • Install Libraries
• Training the Model
• Starting the FastAPI Application

Requirements

• Python 3.x
• pip
• Virtual environment (optional but recommended)

Installation

Create a Virtual Environment

To isolate the dependencies, it is recommended to create a virtual environment. Run the following command to create a new virtual environment in a directory named .venv:

python -m venv .venv

Activate the Virtual Environment

Once the virtual environment is created, you need to activate it.

• For macOS and Linux:

  source .venv/bin/activate

• For Windows:

  .\.venv\Scripts\activate

Install Libraries

After activating the virtual environment, install the required libraries by running:

pip install -r requirements.txt

Training the Model

Once the libraries are installed, execute train_transformer_distillbert_classification.py to train your Deep Learning model. The model will be saved in Models Folder.

python train_transformer_distillbert_classification.py

Starting the FastAPI Application

After training the model, start the FastAPI application by running:

uvicorn main:app --reload

The FastAPI application will now be running and you can access it via http://127.0.0.1:8000/docs.

---

Note: The sample size is currently limited to 1000 due to computational constraints.