Overview
Text classification model selection is driven by a variable most practitioners ignore: dataset size relative to model complexity. Fine-tuning BERT on 200 labeled examples produces a model that memorizes the training set. A TF-IDF logistic regression on 200 examples often outperforms it. Conversely, a bag-of-words model on 50,000 examples leaves significant performance on the table that a transformer would capture.
The right text classification architecture is determined by labeled dataset size, label structure (binary/multiclass/multilabel), text length, domain specificity, and inference latency requirements. Each combination points to a different architecture — and a different preprocessing pipeline.
The NLP Text Classification Model Prompt generates a complete text classification specification: architecture selection by dataset size and label structure, preprocessing pipeline, fine-tuning strategy for transformer models, confidence calibration, and an evaluation framework that tests performance on the metrics that matter for the deployment context.
What you get: - Architecture selection matrix by dataset size and label structure - Preprocessing pipeline matched to architecture - Transfer learning and fine-tuning specification - Confidence calibration for reliable probability scores - Deployment-appropriate evaluation framework
Built for: ML engineers and data scientists building text classification systems for sentiment, intent, topic, and document classification.