Text Classification with BERT

BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained language representation model that has been widely used in natural language processing tasks. In this tutorial, we will explore how to use BERT for text classification.

Introduction to Text Classification

Text classification is a common task in natural language processing, where the goal is to assign a label to a given text. This task is widely used in applications such as sentiment analysis, spam detection, and topic classification.

Preparing the Data

To use BERT for text classification, you need to prepare your dataset. Your dataset should consist of text samples and their corresponding labels. For example, a sentiment analysis dataset might have text samples like "I love this product!" and "I hate this product!" with labels like "positive" and "negative".

Preprocessing the Data

Before feeding your data into BERT, you need to preprocess it. This involves tokenizing the text, converting it into a format that BERT can understand, and padding or truncating the sequences to a fixed length.

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize the text
encoded_input = tokenizer("Hello, my dog is cute", return_tensors='pt')

# Convert to a format that BERT can understand
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']

Fine-tuning BERT

Once your data is preprocessed, you can fine-tune BERT on your specific text classification task. This involves using a pre-trained BERT model as the base and adding a classification layer on top of it.

from transformers import BertForSequenceClassification, Trainer, TrainingArguments

# Load the pre-trained BERT model for sequence classification
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Define the training arguments
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    warmup_steps=500,
    weight_decay=0.01,
    evaluate_during_training=True,
    logging_dir='./logs',
)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

# Train the model
trainer.train()

Evaluating the Model

After training the model, you can evaluate its performance on a test dataset to see how well it performs on unseen data.

# Evaluate the model
results = trainer.evaluate()

print(results.metrics)

Conclusion

In this tutorial, we have explored how to use BERT for text classification. By fine-tuning BERT on your specific dataset, you can achieve state-of-the-art performance on a wide range of natural language processing tasks.

For more information on BERT and its applications, check out our BERT tutorial.