Session 8: Practical NLP - 2

🎓 Course Materials - Practical NLP - 2

Session 8: Fine-Tuning BERT, Few-Shot Learning, and Bias in NLP

In this hands-on session, we explore cutting-edge approaches in advanced NLP, including fine-tuning BERT, leveraging few-shot learning with SetFit, and investigating biases in NLP models (like gender biases using WinoGrad schemas).

This notebook is designed as a modular, reusable blueprint for state-of-the-art NLP techniques.

📓 Notebooks

• Session 8: BERT, Few-Shot Learning, and Bias in NLP

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🎯 Learning Objectives

1. Fine-tune BERT for text classification with a small dataset.
2. Understand and implement few-shot learning using the SetFit framework.
3. Evaluate models using standard metrics (accuracy, precision, recall, F1-score, confusion matrix, ROC/AUC).
4. Analyze and identify biases in BERT models using WinoGrad schemas.
5. Discuss model fairness and interpretability in modern NLP.

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📖 Bibliography & Recommended Reading

• Hugging Face Transformers Documentation – Link
• SetFit: Efficient Few-Shot Classification – Link
• WinoGrad Schema Challenge – Link
• Fairness in Machine Learning – Link

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💻 Practical Components

• 🏗️ Fine-tune BERT on AG News corpus using Hugging Face Transformers.
• 🔄 Train a few-shot classifier with SetFit using just 32 examples.
• 🧪 Experiment with data augmentation via prompt-based methods.
• 🕵️‍♂️ Evaluate model fairness and gender bias in predictions.
• 🎯 Compare models using quantitative metrics (ROC/AUC, F1, etc.) and qualitative outputs (example-level analysis).