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All Course > Python Machine Learning > Natural Language Processing Nov 05, 2024

Text Classification with BERT: A TensorFlow & Keras Guide

In the previous lesson, we explored sentiment analysis using Long Short-Term Memory (LSTM) networks. LSTMs are powerful for handling sequential data like text, but they have limits when it comes to understanding context over long sentences. This is where transformer models, like BERT, come into play. BERT, which stands for Bidirectional Encoder Representations from Transformers, has changed how we handle natural language tasks. It can grasp the meaning of words based on their context, making it a game-changer for text classification.

Table of Contents

Use-Case: Building a Text Classifier with BERT

I recently worked on a project where I needed to classify customer reviews into categories like “positive,” “neutral,” and “negative.” Using LSTMs gave decent results, but I noticed the model struggled with longer reviews and complex sentences. That’s when I decided to try BERT. The difference was clear—BERT not only improved accuracy but also handled nuanced language better. For example, it correctly classified a review like “The product is good, but the delivery was terrible” as “neutral,” which LSTMs often misclassified.

Understanding Transformers and BERT

Transformers are models that use self-attention to process input data in parallel, unlike LSTMs that process data sequentially. BERT is a transformer-based model that reads text bidirectionally, meaning it looks at words before and after a given word to understand its meaning. This helps BERT capture context more effectively. For instance, in the sentence “He went to the bank to deposit money,” BERT understands that “bank” refers to a financial institution, not a riverbank.

Pre-training and Fine-Tuning BERT

BERT is pre-trained on a large corpus of text, which helps it learn general language patterns. Pre-training involves two tasks: masked language modeling (predicting missing words) and next sentence prediction (determining if one sentence follows another). Once pre-trained, BERT can be fine-tuned for specific tasks like text classification. Fine-tuning involves training the model on a smaller, task-specific dataset. For example, to classify customer reviews, I fine-tuned BERT using a dataset of labeled reviews.

Implementing Text Classification with BERT

To implement text classification with BERT, I used TensorFlow and Keras. Here’s how I did it:

  1. Install Required Libraries: First, I installed the transformers library by Hugging Face, which provides pre-trained BERT models.
pip install transformers
  1. Load Pre-trained BERT Model: I loaded a pre-trained BERT model and tokenizer.
from transformers import TFBertForSequenceClassification, BertTokenizer
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
  1. Prepare Data: I tokenized the text data and converted it into input IDs and attention masks.
inputs = tokenizer("This is a sample review.", return_tensors='tf', max_length=128, truncation=True, padding='max_length')
  1. Fine-Tune the Model: I fine-tuned the model using my labeled dataset.
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, epochs=3, validation_data=val_dataset)
  1. Evaluate the Model: After training, I evaluated the model on a test set to check its performance.
test_loss, test_accuracy = model.evaluate(test_dataset)
print(f"Test Accuracy: {test_accuracy}")

Testing the Model

Once fine-tuned, I tested the model with new reviews. For example, the review “The product is amazing and worth every penny” was correctly classified as “positive,” while “I expected more from this product” was labeled as “negative.” The model’s ability to understand context made it highly effective.

Conclusion

In this tutorial, we explored how to use BERT for text classification with TensorFlow and Keras. We learned about transformers, how BERT pre-trains and fine-tunes, and implemented a text classification task step-by-step. BERT’s ability to understand context makes it a powerful tool for NLP tasks. If you want to dive deeper into deploying machine learning models, check out the next lesson in this series. It will guide you through deploying your trained models into production, ensuring they can be used in real-world applications.

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