text-classification

Fine tuning pays off when domain language differs from general web text and you have enough labeled examples to justify it. I keep the training recipe conservative: class weighting if needed, early stopping, mixed precision when available, and metrics

Before I fine-tune transformers, I almost always try a TF-IDF baseline. It is fast, interpretable, and often surprisingly competitive for moderate text classification tasks. If a linear model over sparse features is already good enough, that is usuall