IMDb Sentiment Analysis with DistilBERT LoRA, TF-IDF Baselines, Calibration, Interpretability, Robustness Testing, and Semi-Supervised Learning
The tutorial presents an end-to-end sentiment analysis pipeline on the IMDb dataset, comparing a TF-IDF + Logistic Regression baseline against DistilBERT fine-tuned with LoRA via PEFT Rigorous dataset auditing is emphasized: shuffling before subsampling to avoid class-ordering bias, checking review-length skew, and detecting train/test duplicate leakage Evaluation goes beyond accuracy to include macro-F1, ROC-AUC, confusion matrices, threshold selection, Expected Calibration Error (ECE), and rel
Analysis
TL;DR
- The tutorial presents an end-to-end sentiment analysis pipeline on the IMDb dataset, comparing a TF-IDF + Logistic Regression baseline against DistilBERT fine-tuned with LoRA via PEFT
- Rigorous dataset auditing is emphasized: shuffling before subsampling to avoid class-ordering bias, checking review-length skew, and detecting train/test duplicate leakage
- Evaluation goes beyond accuracy to include macro-F1, ROC-AUC, confusion matrices, threshold selection, Expected Calibration Error (ECE), and reliability diagrams
- Interpretability and robustness analyses include word-level occlusion saliency, head-versus-tail truncation experiments, and confident-error inspection to understand model behavior
- Semi-supervised learning via confidence-based pseudo-labeling on the unlabeled IMDb split is demonstrated, with the final merged transformer saved for reusable inference
Why It Matters
This workflow serves as a practical blueprint for AI practitioners who need to move beyond headline metrics and build production-ready NLP systems with proper validation, calibration, and interpretability. It highlights common pitfalls in dataset handling—such as ordering bias and data leakage—that can silently invalidate model evaluations. The combination of classical baselines, parameter-efficient fine-tuning, and robustness analysis makes it a valuable reference for teams deploying sentiment models in real-world settings.
Technical Details
- Dataset: Stanford NLP IMDb Large Movie Review Dataset; the pipeline audits for class ordering, review-length skew (median ~220 words, with ~75%+ exceeding 256 tokens), and exact duplicate leakage between train/test splits via MD5 hashing
- Baseline: TF-IDF vectorization paired with Logistic Regression using scikit-learn's pipeline, providing a strong classical ML reference point
- Transformer fine-tuning: DistilBERT-base-uncased fine-tuned with LoRA (PEFT library) using 5,000 training samples, 2,000 evaluation samples, batch size 16, learning rate 3e-4, and 2 epochs; a full-run mode scales to 25,000 samples over 3 epochs
- Evaluation suite: Accuracy, macro-F1, ROC-AUC, confusion matrices, ROC curves, ECE-based probability calibration, threshold optimization, confident-error analysis, review-length-stratified performance, word-level occlusion saliency, and head-vs-tail truncation robustness tests
- Semi-supervised extension: Confidence-based pseudo-labeling on 3,000 unlabeled IMDb reviews, compared against the supervised baseline, with the final merged LoRA adapter saved for deployment
Industry Insight
- Always audit datasets for ordering bias and leakage before training; the IMDb dataset's sorted structure can produce misleadingly optimistic results if subsampling is done without shuffling
- Parameter-efficient fine-tuning (LoRA) with a distilled model (DistilBERT) offers a compelling accuracy-efficiency tradeoff for production sentiment systems, especially when labeled data is limited
- Calibration and interpretability checks (ECE, saliency, truncation robustness) should be standard in any NLP deployment pipeline, as they reveal failure modes that accuracy alone conceals—particularly around long-context handling and confident mispredictions
Disclaimer: The above content is generated by AI and is for reference only.