What is text classification? Define labels before assigning them
Explain single-label, multilabel and scored classification and validate references, unknowns and thresholds.
Understand the concept
Text classification assigns predefined categories, such as support inquiries or faults. Labels may be exclusive or multiple. Returned labels are not verified facts and scores are not automatically reliable probabilities. Define boundaries and unknowns and test independent false positives and misses rather than nonempty labels.
Label definitions determine valid answers
Hugging Face documents classification and Google explains metrics and thresholds. Overlapping labels with forced single answers create inconsistent references; complaints differ from negative sentiment. Define meanings and unknown handling. Short text may need context and confident scores do not replace source checks. Readable labels do not establish sound categories.
Test paraphrases and boundaries against fixed references
Agree human labels for public tickets and hold out examples. Add paraphrases and multi-issue inputs and check defined multilabel behavior. Separate wrong labels from unknowns instead of forcing nearest classes. No classifier was trained or run here. Inspect per-class results because imbalance can hide missed important categories.
Maintain label changes and score interpretation
Review old cases and downstream branches when adding labels and version meanings. Consider precision, recall and consequences when changing thresholds. Include labeling and corrections. Fix ambiguous categories before repeatedly replacing models. Report languages, labels and gaps; classifications do not establish authors’ intentions or identities.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.