# facebookresearch/UnsupervisedMT

Phrase-Based & Neural Unsupervised Machine Translation

Repository: https://github.com/facebookresearch/UnsupervisedMT
Canonical: https://ross.abutalabs.com/products/unsupervisedmt
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2021-09-15T22:00:15+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2946, "days_push": 1813, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1499, forks 261 (observed 2026-08-28T04:04:54.109899+00:00)

## What it is
The original implementation of phrase-based (PBSMT) and neural (NMT) unsupervised machine translation models from the EMNLP 2018 paper by Facebook AI Research. It supports seq2seq, biLSTM+attention, and Transformer architectures with denoising auto-encoder and back-translation training.

## Use cases
- translate text between languages without parallel training data
- train an unsupervised neural machine translation model
- run unsupervised phrase-based SMT with Moses
- reproduce the EMNLP 2018 unsupervised MT paper results
- train a Transformer translation model with back-translation
- generate cross-lingual embeddings for translation

## When to choose
- you need to reproduce the original unsupervised MT paper's PBSMT or NMT results
- you want a research reference implementation of denoising auto-encoder and back-translation training
- you need unsupervised phrase-table generation with Moses

## When to avoid
- you want state-of-the-art unsupervised NMT - use facebookresearch/XLM instead as the authors recommend
- you need a production translation system or maintained library
- you need recent PyTorch compatibility - the code was tested on PyTorch 0.5
- you want a simple pretrained translation model without training infrastructure

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, nlp, deep-learning
- domain: machine-learning, deep-learning
- platform: python, cli
- tags: machine-translation, unsupervised-learning, nmt, pbsmt, pytorch, research-code, facebook-research, natural-language-processing, linux

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:54.109899+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 2026-08-30T04:33:02.577705+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/UnsupervisedMT (fetched 2026-08-28T04:04:54.109899+00:00, sha cd904407dc80)
- Data as of 2026-08-30T08:39:29.467469+00:00.