SpamEmail_Classification-using-BERT

A project leveraging BERT, a state-of-the-art language model, to classify spam and non-spam emails. Utilizing TensorFlow and TensorFlow Hub, this project implements a neural network architecture for binary classification. Explore NLP and BERT while building a robust spam detection system.

Spam Email Classification using BERT

In my quest to deepen my understanding of Natural Language Processing (NLP) and Language Model fine-tuning, I've embarked on another exciting project utilizing BERT (Bidirectional Encoder Representations from Transformers).

BERT, developed by Google, is a pre-trained language model that has shown remarkable performance in various NLP tasks. It leverages the power of Transformer architecture to capture contextual relationships in text data.

Project Overview:

This project focuses on building a spam email classifier using BERT. Spam email detection is a classic binary classification problem in the realm of text analysis. By fine-tuning a pre-trained BERT model on a dataset containing labeled examples of spam and non-spam emails, we aim to create a robust classifier capable of accurately distinguishing between the two categories.

Key Features:

• Utilizes TensorFlow and TensorFlow Hub for implementing BERT-based classification model.
• Performs data preprocessing including text cleaning, tokenization, and padding.
• Implements a neural network architecture for binary classification.
• Evaluates the model's performance using metrics like accuracy, precision, recall, and confusion matrix.
• Provides insights into model training, evaluation, and prediction through Jupyter Notebook.

By engaging in this project, I aim to gain hands-on experience in NLP, explore the capabilities of BERT, and contribute to the field of text classification.