# code-kern-ai/refinery

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

Repository: https://github.com/code-kern-ai/refinery
Canonical: https://ross.abutalabs.com/products/code-kern-ai-refinery
Homepage: https://www.kern.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: annotations, data-centric-ai, data-labeling, deep-learning, labeling, labeling-tool, machine-learning, natural-language-processing, neural-search, nlp, text-annotation, transformers, python, human-in-the-loop, spacy, artificial-intelligence, data-science, text-classification, active-learning, supervised-learning
Last push: 2024-12-09T12:46:40+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1521, "days_push": 632, "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 1469, forks 73 (observed 2026-08-28T04:04:49.009535+00:00)

## What it is
An open-source tool for scaling, assessing, and maintaining natural language training data, treating datasets like software artifacts. It provides text annotation, labeling workflows, and data-centric AI capabilities for NLP projects.

## Use cases
- label text data for NLP model training
- manage and version training datasets
- run active learning annotation workflows
- assess quality of labeled text data
- bootstrap labeled data for a text classifier
- collaborate on text annotation with a team

## When to choose
- you need labeled NLP training data but lack annotations
- you want a data-centric workflow for maintaining text datasets
- you want an open-source alternative to commercial labeling tools

## When to avoid
- you need image or audio annotation rather than text
- you want a fully automated pipeline with no human-in-the-loop

## Facets
- artifact type: application
- maturity: active
- function: machine-learning, nlp, data-science, etl
- domain: machine-learning, data-science, artificial-intelligence
- platform: python, self-hosted
- tags: data-labeling, text-annotation, data-centric-ai, active-learning, human-in-the-loop, training-data, weak-supervision, text-classification, natural-language-processing, web-server, docker

## Member repositories
- code-kern-ai/refinery (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:49.009535+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 2026-08-30T04:34:52.683388+00:00, confidence not recorded.
  - readme: https://github.com/code-kern-ai/refinery (fetched 2026-08-28T04:04:49.009535+00:00, sha e376ca50321e)
  - homepage: https://www.kern.ai (fetched 2026-08-29T11:43:05.408287+00:00, sha 076e07aa30f4)
- Data as of 2026-08-30T08:39:29.467469+00:00.