# louismullie/treat

Natural language processing framework for Ruby.

Repository: https://github.com/louismullie/treat
Canonical: https://ross.abutalabs.com/products/treat
Language: Ruby
License: NOASSERTION
License Family: other
Last push: 2025-05-16T03:25:32+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 21, release rhythm 35, longevity 100
- inputs: {"age_days": 5336, "days_push": 474, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1367, forks 122 (observed 2026-08-28T04:04:31.530125+00:00)

## What it is
Treat is a natural language processing and computational linguistics toolkit for Ruby, offering tokenization, parsing, POS tagging, NER, keyword extraction, and text extraction from many formats. It also includes lexical resources, machine learning bindings, and full-text search via Ferret.

## Use cases
- tokenize and segment text in ruby
- parse sentences with stanford parser from ruby
- extract named entities from documents
- extract text from pdfs and word documents in ruby
- stem and conjugate english words
- detect the language of a text
- extract keywords with tf-idf or lda
- index and search documents full-text in ruby

## When to choose
- you need a broad NLP toolkit in a Ruby codebase
- you want document text extraction plus linguistic annotation in one gem
- you need WordNet, stemming, or inflection from Ruby

## When to avoid
- you need an actively maintained or supported library
- you need state-of-the-art NLP accuracy or transformer-based models
- you work outside Ruby or need production-grade performance

## Facets
- artifact type: library
- maturity: abandoned
- function: nlp, parser, search-engine, machine-learning, ocr, pdf, serialization
- domain: machine-learning, text-editors
- platform: ruby
- tags: tokenization, pos-tagging, named-entity-recognition, stemming, language-detection, keyword-extraction, lda, wordnet, full-text-search, unmaintained, natural-language-processing

## Member repositories
- louismullie/treat (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:31.530125+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 2026-08-30T04:41:08.190594+00:00, confidence not recorded.
  - readme: https://github.com/louismullie/treat (fetched 2026-08-28T04:04:31.530125+00:00, sha 46bde1b6476c)
- Data as of 2026-08-30T08:39:29.467469+00:00.