Fine-Tune BERT for Text Classification with TensorFlow

Overview

This project demonstrates how to fine-tune a pre-trained BERT model for text classification using TensorFlow and TF-Hub. The goal is to classify the Quora Insincere Questions dataset, which contains text data with labels indicating whether a question is insincere or sincere.

Learning Objectives

By the end of this project, you will be able to:

• Build TensorFlow input pipelines for text data using the tf.data API
• Tokenize and preprocess text for BERT input
• Fine-tune a BERT model for text classification using TensorFlow and TensorFlow Hub

Prerequisites

Before you begin, ensure you have the following:

• Python 3.x
• Basic knowledge of TensorFlow, NLP, and deep learning concepts
• Familiarity with TensorFlow Keras API
• Google Colab or local setup with GPU support (optional but recommended for faster training)

Setup Instructions

Step 1: Clone the Repository and Install Dependencies

Clone this repository and install the required packages. If you're using Google Colab, you can skip cloning the repository, but you'll need to install the required libraries.

!pip install -q tensorflow==2.3.0
!git clone --depth 1 -b v2.3.0 https://github.com/tensorflow/models.git
!pip install -Uqr models/official/requirements.txt

All the instructions and methods are given in the Python notebook with source code. .You can run it or use a Python script file also.train.csv is the training dataset.