Sentiment Analysis API Using Transformers

This repository contains the complete code for building a sentiment analysis model using BERT (a transformer model) and deploying it as a RESTful API using FastAPI.

What’s Inside:

1. End-to-end guide to preprocess customer feedback data for sentiment analysis.
2. Fine-tune a pre-trained BERT model for classifying text into Positive, Neutral, or Negative sentiments.
3. Save the model and deploy it as an API using FastAPI.
4. Test the API with Python’s requests library for real-world use.

Highlights:

1. Beginner-friendly explanations of each step, including tokenization, training, and deployment.
2. Simple, reusable code snippets to fit your own datasets.
3. Detailed guidance on testing the API locally using uvicorn and requests.
4. Run Locally: Clone this repo and follow the step-by-step instructions.
5. Perfect for students, beginners, and developers looking to add NLP-powered APIs to their projects.