# moses-smt/mosesdecoder

Moses, the machine translation system

Repository: https://github.com/moses-smt/mosesdecoder
Canonical: https://ross.abutalabs.com/products/mosesdecoder
Homepage: http://www.statmt.org/moses
Language: Roff
License: LGPL-2.1
License Family: copyleft
Last push: 2025-03-28T17:01:39+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 13, release rhythm 8, longevity 100
- inputs: {"age_days": 5493, "days_push": 523, "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 1624, forks 772 (observed 2026-08-28T04:05:12.784972+00:00)

## What it is
Moses is a statistical machine translation (SMT) system that trains translation models from parallel corpora and decodes with efficient phrase-based and syntax-based search. It includes training pipelines, tuning tools, a fast decoder (plus Moses2), and integration with language models like KenLM.

## Use cases
- train a machine translation model from a parallel corpus
- translate text between language pairs with a statistical MT decoder
- build phrase-based or syntax-based translation systems
- tune translation model weights with MERT/MIRA
- run machine translation as a web service
- experiment with factored and sparse feature translation models

## When to choose
- you need a classic statistical MT system with full training and decoding pipelines
- you have a parallel corpus and want to train translation models for any language pair
- you need an efficient, customizable decoder with advanced features like factored models and sparse features
- you want to run MT as a server or integrate it into a larger pipeline

## When to avoid
- you want modern neural machine translation quality - use NMT frameworks instead
- you need a lightweight library to embed in another application
- you are starting a new MT project, since SMT is largely superseded by neural approaches

## Facets
- artifact type: application
- maturity: maintenance
- function: machine-learning, nlp, cli, http-server
- domain: machine-learning, artificial-intelligence
- platform: windows, cpp, cli
- tags: statistical-machine-translation, smt, decoder, phrase-based-translation, language-models, kenlm, natural-language-processing, linux, macos

## Member repositories
- moses-smt/mosesdecoder (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:12.784972+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 2026-08-30T03:48:47.572822+00:00, confidence not recorded.
  - readme: https://github.com/moses-smt/mosesdecoder (fetched 2026-08-28T04:05:12.784972+00:00, sha 91d6a2e96ad1)
  - homepage: http://www.statmt.org/moses (fetched 2026-08-29T11:21:40.157629+00:00, sha 5053fcda6873)
  - site_page: http://www2.statmt.org/moses?n=Moses.AdvancedFeatures (fetched 2026-08-29T11:21:40.191529+00:00, sha 638d0449f40e)
  - site_page: http://www2.statmt.org/moses?n=Moses.SparseFeatures (fetched 2026-08-29T11:21:40.193336+00:00, sha d5525606b73c)
  - site_page: http://www2.statmt.org/moses?n=Moses.FAQ (fetched 2026-08-29T11:21:40.160711+00:00, sha 697f2df6bbb7)
  - site_page: http://www2.statmt.org/moses?n=Development.GetStarted (fetched 2026-08-29T11:21:40.186096+00:00, sha bfd8d7bf665b)
  - site_page: http://www2.statmt.org/moses?n=Moses.Releases (fetched 2026-08-29T11:21:40.188858+00:00, sha 240ee3cc8a01)
  - site_page: http://www2.statmt.org/moses?n=Advanced.Obsolete (fetched 2026-08-29T11:21:40.195317+00:00, sha 75abb0540e0c)
  - site_page: http://www.statmt.org/moses/html/hierarchy.html (fetched 2026-08-29T11:21:40.197330+00:00, sha 4653099aee72)
- Data as of 2026-08-30T08:39:29.467469+00:00.