skills-diy

Analyzes NLP datasets before model training or annotation pipeline iteration. Provides insights on data volume, text content distribution, label distribution, and data quality issues. Use when: (1) Starting a new NLP project, (2) Before training a model, (3) After data collection/annotation, (4) Evaluating dataset quality for text classification, NER, QA, or other NLP tasks.

Analyzes model predictions to identify and categorize bad cases (FP/FN) with detailed metrics. Generates interactive HTML reports for human review with filtering by error type, predicted/actual class, and identified error patterns. Use when: (1) After model evaluation, (2) Analyzing classification errors, (3) Conducting error analysis for model improvement, (4) Reviewing prediction quality for NLP classification tasks.