abir9hassini / Fine-Tune-BERT-for-Text-Classification

This project is on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow

Fine-Tune-BERT-for-Text-Classification

This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow and TensorFlow-hub.

Project Structure

The hands on project on Fine Tune BERT for Text Classification with TensorFlow is divided into following tasks:

• Task 1: Introduction to the Project
• Task 2: Setup your TensorFlow and Colab Runtime
• Task 3: Load the Quora Insincere Questions Dataset
• Task 4: Create tf.data.Datasets for Training and Evaluation
• Task 5: Download a Pre-trained BERT Model from TensorFlow Hub
• Task 6: Tokenize and Preprocess Text for BERT
• Task 7: Wrap a Python Function into a TensorFlow op for Eager Execution
• Task 8: Create a TensorFlow Input Pipeline with tf.data
• Task 9: Add a Classification Head to the BERT hub.KerasLayer
• Task 10: Fine-Tune BERT for Text Classification
• Task 11: Evaluate the BERT Text Classification Model