readme.md

Project: Fine-Tuning BERT for Text Classification (TensorFlow)

Objective:

Fine-tune a pre-trained BERT model for binary text classification (e.g., Quora insincere questions dataset) using TensorFlow and TensorFlow Hub.

Tasks:

1. Setup: Install TensorFlow and TensorFlow Model Garden.

2. Data Preparation:

   • Download and import the Quora Insincere Questions dataset.
   • Split into train, validation, and test sets, addressing class imbalance.
   • Create TensorFlow Datasets (tf.data) for efficient data loading and preprocessing.

3. BERT Model Preparation:

   • Load a pre-trained BERT model from TensorFlow Hub.
   • Tokenize and preprocess text data for BERT input (input word IDs, input mask, segment IDs).
   • Wrap Python functions for preprocessing into TensorFlow operations using tf.py_function.

4. Model Building:

   • Add a classification head (dense layer with sigmoid activation) to the BERT layer.
   • Compile the model with Adam optimizer and binary cross-entropy loss.

5. Fine-tuning:

   • Train the model on the training data for a few epochs, validating on the validation set.
   • Use early stopping to prevent overfitting.

6. Evaluation:

   • Plot training and validation loss/accuracy curves.
   • Evaluate the best model on the test set.

Key Points:

• BERT is a powerful transformer-based model for NLP tasks.
• Fine-tuning allows adapting BERT to specific classification tasks.
• TensorFlow and TensorFlow Hub provide convenient tools for working with BERT.
• tf.data API enables efficient data input pipelines for training and evaluation.

Additional Notes:

• BERT's architecture consists of multiple transformer encoder blocks.
• It produces contextualized embeddings for each token in the input sequence.
• The [CLS] token is often used as the aggregated representation for classification.

Limitations:

• BERT can be computationally expensive to train and fine-tune.
• Requires significant labelled data for effective fine-tuning.