refuel-ai/autolabel

Health v2 · maintenance only

Adoption not part of the score

2329 stars · 159 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Autolabel is a Python library for labeling, cleaning, and enriching text datasets using large language models like GPT-4, Claude, and open-source models. It also includes benchmarking tooling to compare LLM labeling accuracy across models on a shared dataset.

Use cases

• automatically label text data with llms
• classify sentiment of customer reviews at scale
• clean and enrich a csv dataset with gpt-4
• benchmark llm models on labeling accuracy
• generate training data for machine learning without manual annotation
• annotate text datasets with claude or openai models

When to choose

• you need large labeled text datasets without paying for manual annotation
• you want to compare multiple LLMs on labeling tasks with consistent prompts
• you prefer a Python library over a hosted data-labeling platform
• you want to clean or enrich text data using LLM pipelines

When to avoid

• you need a GUI-based annotation workflow with human review loops
• your data is images, audio, or other non-text modalities
• you want a fully managed platform with monitoring and deployment rather than a library
• you need real-time labeling inference at low latency in production

Facets

library · maturity active

machine-learning nlp llm-inference data-science etl machine-learning data-science large-language-models python data-labeling llm-labeling dataset-annotation data-cleaning data-enrichment text-classification benchmarking natural-language-processing

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