IMDb Sentiment Analysis with DistilBERT LoRA, TF-IDF Baselines, Calibration, Interpretability, Robustness Testing, and Semi-Supervised Learning

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In this tutorial, we develop an end-to-end sentiment analysis workflow using the Stanford NLP IMDb Large Movie Review Dataset and compare classical machine learning with parameter-efficient transformer fine-tuning. We begin by establishing a reproducible environment and auditing the dataset for class ordering, review-length skew, duplicate leakage, and preprocessing artifacts before training a strong TF-IDF and Logistic Regression baseline. We then fine-tune DistilBERT with LoRA through PEFT, evaluate it using accuracy, macro-F1, ROC-AUC, confusion matrices, and ROC curves, and examine threshold selection and probability calibration through Expected Calibration Error and reliability analysis. Beyond headline metrics, we investigate confident errors, performance across review lengths, word-level occlusion saliency, and head-versus-tail truncation to understand how the model reaches its predictions and where long-context limitations affect performance. Finally, we use the unlabeled IMDb split for confidence-based pseudo-labeling, compare the resulting semi-supervised model against our baseline, and save the merged transformer for reusable sentiment inference.

Copy CodeCopiedUse a different Browserimport importlib.util, subprocess, sys, os, time, random, warnings, inspect, hashlib warnings.filterwarnings("ignore") os.environ["TOKENIZERS_PARALLELISM"] = "false" os.environ["WANDB_DISABLED"] = "true" _REQUIRED = { "transformers": "transformers", "datasets": "datasets", "peft": "peft", "accelerate": "accelerate", "sklearn": "scikit-learn", } _missing = [pkg for mod, pkg in …

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• Originalquelle / Hersteller-Advisory lesen: https://www.marktechpost.com/2026/08/09/imdb-sentiment-analysis-with-distilbert-lora-tf-idf-baselines-calibration-interpretability-robustness-testing-and-semi-supervised-learning/
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