Project Description: Sentiment Analysis with Fine-Tuned BERT

This project applies transfer learning to a pre-trained BERT model (Bidirectional Encoder Representations from Transformers) for sentiment analysis. The goal is to classify textual data into six emotion categories: joy, sadness, anger, fear, love, and surprise. By leveraging the power of DistilBERT Multilingual Cased, the project focuses on adapting the model to specific sentiment classification tasks through fine-tuning.

Key features of the project include:

• Data Processing: Prepares and tokenizes textual data using a domain-specific pipeline.
• Model Fine-Tuning: Customizes the BERT model for sentiment classification using labeled datasets.
• Inference Pipeline: Implements an efficient script for real-time predictions on new inputs.
• Deployment: The trained model is containerized with Docker and deployed to the AWS Cloud, enabling scalability and easy integration into applications.

The project structure emphasizes modularity and reusability, leveraging industry best practices for MLOps. Tools like PyTorch, Hugging Face Transformers, and Poetry ensure a streamlined development and deployment process.

This repository is suitable for researchers, developers, and data scientists looking to explore or extend state-of-the-art sentiment analysis solutions in a production-ready environment.