# tensorflow/nmt

TensorFlow Neural Machine Translation Tutorial

Repository: https://github.com/tensorflow/nmt
Canonical: https://ross.abutalabs.com/products/nmt
Language: Python
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2022-10-09T08:07:34+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": 3353, "days_push": 1424, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6454, forks 1937 (observed 2026-08-28T04:09:43.520657+00:00)

## What it is
A TensorFlow tutorial and codebase teaching how to build neural machine translation (seq2seq) models from scratch, including attention mechanisms, beam search, and benchmarks. It serves as both educational material and a reference implementation for sequence-to-sequence learning.

## Use cases
- learn how to build a seq2seq neural machine translation model
- train a translation model with attention in TensorFlow
- understand encoder-decoder architectures for NMT
- benchmark seq2seq models on WMT and IWSLT datasets
- implement beam search decoding for translation
- study attention mechanism implementation in TensorFlow

## When to choose
- learning seq2seq and attention concepts with a full worked example
- training classic RNN-based NMT models on TensorFlow 1.x-era stacks
- referencing a well-cited research baseline for machine translation

## When to avoid
- building production translation systems with modern transformers
- projects requiring actively maintained dependencies or recent TensorFlow versions
- translation tasks better served by pretrained multilingual models

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp, deep-learning
- domain: machine-learning, tutorials
- platform: python, cross-platform
- tags: seq2seq, neural-machine-translation, tensorflow, attention, tutorial, encoder-decoder, beam-search, natural-language-processing, gpu

## Member repositories
- tensorflow/nmt (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:43.520657+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 2026-08-29T17:44:38.756939+00:00, confidence not recorded.
  - readme: https://github.com/tensorflow/nmt (fetched 2026-08-28T04:09:43.520657+00:00, sha d95843535d4c)
- Data as of 2026-08-30T08:39:29.467469+00:00.