# facebookresearch/fastText

Library for fast text representation and classification.

Repository: https://github.com/facebookresearch/fastText
Canonical: https://ross.abutalabs.com/products/fasttext
Homepage: https://fasttext.cc/
Language: HTML
License: MIT
License Family: permissive
Archived: true
Last push: 2024-03-22T21:40:44+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3700, "days_push": 894, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 26534, forks 4837 (observed 2026-08-28T04:11:47.005216+00:00)

## What it is
fastText is a lightweight open-source C++ library (with Python bindings and a CLI) from Facebook Research for efficiently learning word representations and training text classifiers. It ships pre-trained word vectors for 157 languages and supports model quantization so models can run on modest hardware or mobile devices.

## Use cases
- train word embeddings for my corpus
- classify documents into categories like spam vs not spam
- get word vectors for out-of-vocabulary or misspelled words
- detect the language of a text snippet
- run sentiment analysis on product reviews
- compress a text classification model to fit on mobile
- download pre-trained word vectors for 157 languages

## When to choose
- you need fast, lightweight text classification or word vectors on CPU-only hardware
- you need embeddings for rare or misspelled words via subword (character n-gram) information
- you want pre-trained vectors for many languages or a small language-identification model
- you need models small enough for mobile or embedded deployment via quantization

## When to avoid
- you need contextual/transformer-based embeddings for state-of-the-art NLU
- you need deep learning pipelines, GPU training, or fine-tuning of large models
- you primarily develop on Windows, which is not officially supported
- you need actively developed features - the project is largely in maintenance mode

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, cli
- domain: machine-learning, developer-tools
- platform: cpp, python, cli
- tags: word-embeddings, word-vectors, text-classification, sentence-classification, subword-ngrams, pretrained-models, language-identification, model-quantization, natural-language-processing, linux, macos

## Member repositories
- facebookresearch/fastText (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:47.005216+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 2026-08-29T16:55:51.390586+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/fastText (fetched 2026-08-28T04:11:47.005216+00:00, sha 58fbebb2e675)
  - homepage: https://fasttext.cc/ (fetched 2026-08-29T07:52:14.241062+00:00, sha 1f25b24e1b14)
  - site_page: https://fasttext.cc/docs/en/support.html (fetched 2026-08-29T07:52:14.243615+00:00, sha be27eeb66a2f)
  - site_page: https://fasttext.cc/docs/en/english-vectors.html (fetched 2026-08-29T07:52:14.245385+00:00, sha f1fe16914602)
  - site_page: https://fasttext.cc/docs/en/crawl-vectors.html (fetched 2026-08-29T07:52:14.247185+00:00, sha a106decaba88)
  - site_page: https://fasttext.cc/docs/en/supervised-tutorial.html (fetched 2026-08-29T07:52:14.249072+00:00, sha 201bf4fbccf8)
  - site_page: https://fasttext.cc/docs/en/faqs.html (fetched 2026-08-29T07:52:14.251313+00:00, sha ff05b642e534)
  - site_page: https://fasttext.cc/docs/en/api.html (fetched 2026-08-29T07:52:14.253277+00:00, sha f6d54e7b738e)
  - site_page: https://fasttext.cc/docs/en/references.html (fetched 2026-08-29T07:52:14.254809+00:00, sha 1fe1cea71916)
- Data as of 2026-08-30T08:39:29.467469+00:00.