# explosion/projects

🪐 End-to-end NLP workflows from prototype to production

Repository: https://github.com/explosion/projects
Canonical: https://ross.abutalabs.com/products/explosion-projects
Homepage: https://spacy.io/usage/projects
Language: Python
License: MIT
License Family: permissive
Topics: nlp, natural-language-processing, datasets, annotations, spacy, prodigy
Last push: 2024-10-15T12:32:08+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": 2477, "days_push": 687, "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 1436, forks 473 (observed 2026-08-28T04:04:43.680510+00:00)

## What it is
A repository of project templates for Weasel (spaCy projects) providing end-to-end NLP workflows from prototype to production. Templates cover training pipelines, tutorials, integrations, benchmarks, and experimental workflows that can be cloned and customized.

## Use cases
- train a custom spaCy NLP pipeline from a template
- learn end-to-end NLP workflows with worked examples
- set up data annotation to model training to packaging workflow
- reproduce NLP benchmarks for spaCy models
- integrate spaCy with third-party ML tools
- package and deploy a trained NLP model as a Python package

## When to choose
- you use spaCy or Prodigy and want structured, reproducible NLP project scaffolding
- you need templates for training, packaging, and serving custom NLP pipelines
- you want to learn NLP workflows through complete end-to-end examples

## When to avoid
- you need a general-purpose workflow orchestrator outside NLP
- you don't use the spaCy/Weasel ecosystem
- you need a production application rather than templates and examples

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, machine-learning, workflow-automation, developer-tools, benchmarking
- domain: machine-learning, developer-tools, tutorials
- platform: python, cross-platform, cli
- tags: spacy, prodigy, weasel, project-templates, nlp-pipelines, annotation, model-training, end-to-end-workflows, natural-language-processing

## Member repositories
- explosion/projects (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:43.680510+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 2026-08-30T04:36:47.532783+00:00, confidence not recorded.
  - readme: https://github.com/explosion/projects (fetched 2026-08-28T04:04:43.680510+00:00, sha 016e01093197)
  - homepage: https://spacy.io/usage/projects (fetched 2026-08-29T11:47:42.244082+00:00, sha 33a165c16edf)
  - site_page: https://spacy.io/usage/linguistic-features (fetched 2026-08-29T11:47:42.256519+00:00, sha 15f0372dd6a7)
  - site_page: https://spacy.io/usage (fetched 2026-08-29T11:47:42.254012+00:00, sha b80b5dfe308d)
- Data as of 2026-08-30T08:39:29.467469+00:00.