# refuel-ai/autolabel

Label, clean and enrich text datasets with LLMs.

Repository: https://github.com/refuel-ai/autolabel
Canonical: https://ross.abutalabs.com/products/autolabel
Homepage: https://docs.refuel.ai/
Language: Python
License: MIT
License Family: permissive
Topics: llm, large-language-models, huggingface-transformers, python, langchain, anthropic-claude, gpt-4, data-science, llms, machine-learning, openai
Last push: 2025-03-05T01:15:43+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 9, release rhythm 8, longevity 90
- inputs: {"age_days": 1260, "days_push": 547, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2329, forks 159 (observed 2026-08-28T04:06:37.656846+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, nlp, llm-inference, data-science, etl
- domain: machine-learning, data-science, large-language-models
- platform: python
- tags: data-labeling, llm-labeling, dataset-annotation, data-cleaning, data-enrichment, text-classification, benchmarking, natural-language-processing

## Member repositories
- refuel-ai/autolabel (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:37.656846+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:38:11.411131+00:00, confidence not recorded.
  - readme: https://github.com/refuel-ai/autolabel (fetched 2026-08-28T04:06:37.656846+00:00, sha 5c00de6a82a0)
  - homepage: https://docs.refuel.ai/ (fetched 2026-08-29T10:18:38.586935+00:00, sha deebb675089b)
  - site_page: https://docs.refuel.ai/integrations/introduction (fetched 2026-08-29T10:18:38.595841+00:00, sha c271616623be)
  - site_page: https://docs.refuel.ai/quickstart (fetched 2026-08-29T10:18:38.597647+00:00, sha 6ba40f7a9353)
- Data as of 2026-08-30T08:39:29.467469+00:00.