AI Trainer for python
Project overview End-to-end data annotation and evaluation pipeline to train and test language models. In Python, I prepare datasets, build lightweight verification utilities, organize the labeling flow for text and audio, and consolidate quality metrics. The focus is clarity, consistency, and reproducibility: every decision is documented and auditable. Scope (what is included) Data curation: collection and cleaning, deduplication, encoding normalization, and formatting in comma-separated values or JavaScript Object Notation. Guideline design: label taxonomies, positive and negative examples, edge cases, and escalation policy. Labeling (text and, when applicable, audio): classification, entity extraction, evaluation of language-model outputs, side-by-side comparison, and prompt creation or translation. Quality assurance and metrics: sampling, automated checks, inter-annotator consistency, error traces, and regression tracking. Reporting and delivery: coverage spreadsheets, simpl