Space: rafaelpo/everyrow_active_learning
title: How to Replace Human Annotators with LLMs for Data Labelling
sdk: docker
short_description: LLM data labelling — 96% accuracy, $0.26/run
tags: everyrow, everyrow-sdk, data-labelling, data-annotation, llm-labelling, active-learning, text-classification, agent-map, structured-output, uncertainty-sampling, replace-human-annotators, pydantic, lightgbm, nlp, machine-learning, tutorial, jupyter
datasets: fancyzhx/dbpedia_14
thumbnail: https://raw.githubusercontent.com/futuresearch/everyrow-sdk/main/docs/case_studies/active-learning/learning_curve_accuracy.png
Tags: docker, everyrow, everyrow-sdk, data-labelling, data-annotation, llm-labelling, active-learning, text-classification, agent-map, structured-output, uncertainty-sampling, replace-human-annotators, pydantic, lightgbm, nlp, machine-learning, tutorial, jupyter, region:us