BERT (Bidirectional Encoder Representations from Transformers) has revolutionized natural language processing tasks, including text classification. This tutorial will walk you through the fundamentals of using BERT for classification, from model setup to implementation tips.

Why BERT for Text Classification?

  • Contextual Understanding: BERT captures nuanced relationships between words through bidirectional training.
  • Pretrained Models: Leverage massive datasets to reduce training time and improve accuracy.
  • Fine-tuning Flexibility: Adapt BERT to specific tasks with minimal modifications.

Key Steps to Implement BERT for Classification

  1. Install Dependencies 

    pip install transformers torch

    📌 Explore more about installing transformer libraries

  2. Load Pretrained Model 

    from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

  3. Prepare Training Data

    • Format: (text, label) pairs
    • Example: 
      texts = ["I love programming!", "This code is terrible."]
labels = [1, 0]  # 1 for positive, 0 for negative

  4. Tokenize and Train 

    inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
outputs = model(**inputs, labels=torch.tensor(labels))

Applications of BERT in Text Classification

  • Sentiment Analysis 😊😠
    • Example: Classify movie reviews as positive/negative
  • News Categorization 📰
    • Example: Label articles by topic (e.g., sports, politics)
  • Spam Detection 🚫
    • Example: Filter out unwanted messages

Visualizing BERT Architecture

BERT Architecture
📌 [Learn more about BERT's transformer layers](/en/resources/nlp-tutorials/bert-technical-details)

Tips for Success

  • Use bert-base-multilingual-cased for multilingual tasks
  • Experiment with distilbert-base-uncased for faster inference
  • Always validate with a separate test dataset

Would you like to dive deeper into BERT fine-tuning strategies or comparison with other models?