Transfer learning using BERT

At PyBooks, the company has decided to leverage the power of the BERT model, a pre-trained transformer model, for sentiment analysis. BERT has seen remarkable performance across various NLP tasks, making it a prime candidate for this use case.

You're tasked with setting up a basic workflow using the BERT model from the transformers library for binary sentiment classification.

The following has been imported for you: BertTokenizer, BertForSequenceClassification, torch. The example data texts and corresponding labels are also preloaded.

This exercise is part of the course

Deep Learning for Text with PyTorch

Exercise instructions

Load the bert-base-uncased tokenizer and model suitable for binary classification.
Tokenize your dataset and prepare it for the model, ensuring it returns PyTorch tensors using the return_tensors argument.
Setup the optimizer using model parameters.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load the BERT tokenizer and model
tokenizer = BertTokenizer.from_pretrained('____')
model = BertForSequenceClassification.from_pretrained('____', num_labels=____)

# Tokenize your data and return PyTorch tensors
inputs = tokenizer(____, padding=True, truncation=True, return_tensors="____", max_length=32)
inputs["labels"] = torch.tensor(labels)

# Setup the optimizer using model parameters
optimizer = torch.optim.AdamW(____, lr=0.00001)
model.train()
for epoch in range(2):
    outputs = model(**inputs)
    loss = outputs.loss
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()
    print(f"Epoch: {epoch+1}, Loss: {loss.item()}")

Edit and Run Code

This exercise is part of the course

Deep Learning for Text with PyTorch

AdvancedSkill Level

4.8+

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This chapter introduces you to deep learning for text and its applications. Learn how to use PyTorch for text processing and get hands-on experience with techniques such as tokenization, stemming, stopword removal, and more. Understand the importance of encoding text data and implement encoding techniques using PyTorch. Finally, consolidate your knowledge by building a text processing pipeline combining these techniques.

Exercise 1: Introduction to preprocessing for text Exercise 2: Word frequency analysis Exercise 3: Preprocessing text Exercise 4: Encoding text data Exercise 5: One-hot encoded book titles Exercise 6: Bag-of-words for book titles Exercise 7: Applying TF-IDF to book descriptions Exercise 8: Introduction to building a text processing pipeline Exercise 9: Shakespearean language preprocessing pipeline Exercise 10: Shakespearean language encoder

Explore text classification and its role in Natural Language Processing (NLP). Apply your skills to implement word embeddings and develop both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification using PyTorch, and understand how to evaluate your models using suitable metrics.

Exercise 1: Overview of Text Classification Exercise 2: Embedding in PyTorch Exercise 3: Categorizing text classification tasks Exercise 4: Convolutional neural networks for text classification Exercise 5: Build a CNN model for text Exercise 6: Train a CNN model for text Exercise 7: Testing the Sentiment Analysis CNN Model Exercise 8: Recurrent neural networks for text classification Exercise 9: Building an RNN model for text Exercise 10: Building an LSTM model for text Exercise 11: Building a GRU model for text Exercise 12: Evaluation metrics for text classification Exercise 13: Evaluating RNN classification models Exercise 14: Evaluating the model's performance Exercise 15: Comparing models

Venture into the exciting world of text generation and its applications in NLP. Understand how to leverage Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and pre-trained models for text generation tasks using PyTorch. Alongside, you'll learn to evaluate the performance of your models using relevant metrics.

Exercise 1: Introduction to text generation Exercise 2: Creating a RNN model for text generation Exercise 3: Text generation using RNN - Training and Generation Exercise 4: Generative adversarial networks for text generation Exercise 5: Building a generator and discriminator Exercise 6: Training a GAN model Exercise 7: Pre-trained models for text generation Exercise 8: Text completion with pre-trained GPT-2 models Exercise 9: Language translation with pretrained PyTorch model Exercise 10: Evaluation metrics for text generation Exercise 11: Evaluating pretrained text generation model Exercise 12: Understanding text generation metrics

Understand the concept of transfer learning and its application in text classification. Explore Transformers, their architecture, and how to use them for text classification and generation tasks. You will also delve into attention mechanisms and their role in text processing. Finally, understand the potential impacts of adversarial attacks on text classification models and learn how to protect your models.

Exercise 1: Transfer learning for text classification Exercise 2: Transfer learning using BERT

Current Exercise

Exercise 3: Evaluating the BERT model Exercise 4: Transformers for text processing Exercise 5: Creating a transformer model Exercise 6: Training and testing the Transformer model Exercise 7: Attention mechanisms for text processing Exercise 8: Creating a RNN model with attention Exercise 9: Training and testing the RNN model with attention Exercise 10: Adversarial attacks on text classification models Exercise 11: Adversarial attack classification Exercise 12: Safeguarding AI at PyBooks Exercise 13: Wrap-up