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IMDb Sentiment Analysis with DistilBERT LoRA, TF-IDF Baselines, Calibration, Interpretability, Robustness Testing, an...
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- DeepHorus
Machine learning practitioners now have a comprehensive new workflow for sentiment analysis, built around the Stanford IMDb Large Movie Review Dataset and a side-by-side comparison of classical and transformer-based methods. The tutorial, released as executable code, demonstrates how to train both a TF-IDF logistic regression baseline and a DistilBERT model fine-tuned with LoRA using the PEFT library. It goes beyond simple accuracy, adding calibration, interpretability, and robustness testing. This matters because organizations need cost-effective and trustworthy NLP systems for real-world sentiment classification.
The IMDb dataset remains a standard benchmark, containing 25,000 training and 25,000 test movie reviews. The workflow begins by auditing the data for class ordering, review-length skew, duplicate leakage, and preprocessing artifacts before any training. A strong baseline is established with TF-IDF vectorization and logistic regression, ensuring that the later transformer model is measured against a solid reference point.
In its default configuration, the workflow uses 5,000 training samples, 2,000 for evaluation, and 3,000 unlabeled samples, with a maximum token length of 256 and two training epochs. The DistilBERT model is fine-tuned with a batch size of 16 and a learning rate of 3e-4. Evaluation metrics include accuracy, macro-F1, ROC-AUC, confusion matrices, ROC curves, and threshold selection, along with Expected Calibration Error and reliability analysis.
The tutorial investigates confident errors and performance across different review lengths, using word-level occlusion saliency to reveal which words drive predictions. Head-versus-tail truncation tests expose how long-context limitations affect model behavior. These insights help practitioners understand when a model can be trusted and how it degrades on longer inputs, which is crucial for deployment in production systems.
The project reflects a broader industry shift toward parameter-efficient fine-tuning, which cuts computational costs while retaining competitive accuracy. LoRA adapts DistilBERT with only a small fraction of trainable parameters, making high-quality sentiment analysis accessible to teams without massive GPU budgets. By comparing classical and transformer pipelines, the workflow gives data scientists a clear, reproducible roadmap.
Finally, the workflow uses confidence-based pseudo-labeling on the unlabeled IMDb split, creating a semi-supervised model that improves over the baseline and is saved for reusable inference. This points to future progress in low-resource and domain-adaptation scenarios where labeled data remains scarce. The release sets a new standard for rigorous, end-to-end NLP evaluation.
