Build text classifiers and extraction systems with explicit corpus, annotation, split, evaluation and serving contracts.
Learning path
- Text corpus contracts: identity, label timing and annotation rules
- Unicode and tokenization: preserve meaning at the text boundary
- Entity spans: align annotations to the original text
- Sparse text baselines: fit vocabulary inside the training boundary
- Text validation: split conversations, duplicates and time together
- Text classification evaluation: inspect slices and allow abstention
- Text inference: package tokenizer, labels and reject paths
- Project: route support tickets with auditable text evaluation
Connected foundations
Use the earlier data and model boundaries as prerequisites.
Practice
Continue into another subject: Multimodal AI Tutorial.