Classical NLP & Text Classification

TF-IDF to DeBERTa to LLM-assisted labeling — the text models that still run most products

Build and defend a text classification or extraction system end to end, with numbers you can reproduce: the text pipeline and when normalization, stopword removal and stemming hurt (removing stop words cost 2.8 macro-F1 points on a bigram model in a measured test), representations (bag of words, TF-IDF with sklearn's exact smooth-idf formula hand-checked to zero difference on a four-document corpus, sublinear tf, character n-grams, the hashing trick with measured collision rates that match the closed-form 1-(1-1/m)^(V-1) estimate), linear baselines (logistic regression, linear SVM, complement Naive Bayes on a 28-class imbalanced ticket corpus, macro-F1 versus accuracy, calibration and ECE, multi-label one-vs-rest with tuned thresholds), fine-tuned encoders (BERT, RoBERTa and DeBERTa heads, learning-rate warmup, head-plus-tail truncation, sliding windows for 5,000-token documents, distilled and multilingual models), sequence labeling (NER as token classification, BIO tagging, CRF layers, subword alignment, entity-level versus token-level F1 computed by hand with seqeval-style scoring, nested entities, PII detection), other tasks (sentiment and aspect-based sentiment, intent and out-of-scope detection, LDA versus BERTopic, keyword extraction, near-duplicate detection with MinHash and LSH checked against exact Jaccard), LLMs versus small models (zero and few-shot classification, LLM-as-labeler to bootstrap a small model with a simulated silver-label experiment, structured output, an explicit assumption-based cost and latency table, and when an LLM is the wrong choice), evaluation (macro versus micro F1, per-class confusion, calibration, Cohen's kappa for annotator agreement, error analysis, vocabulary drift), production (batching, max length, caching, monitoring, multilingual coverage), and a worked support-ticket routing example including a deduplication-leakage demonstration where a random split inflated macro-F1 by 5.6 to 13 points.

Practice questions (6)

• Design a Classifier for 40 Imbalanced Support Classes

  Intermediate · Free
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• Evaluate an NER Model: Token Accuracy Versus Entity F1

  Intermediate · Free
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• Small Fine-Tuned Model or LLM: Decide With Numbers

  Advanced
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• Classify 5,000-Token Documents With a 512-Token Encoder

  Intermediate
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• Bootstrap Training Labels With an LLM Without Fooling Yourself

  Advanced
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Quizzes (1)