# farizrahman4u/seq2seq

Sequence to Sequence Learning with Keras

Repository: https://github.com/farizrahman4u/seq2seq
Canonical: https://ross.abutalabs.com/products/farizrahman4u-seq2seq
Language: Python
License: GPL-2.0
License Family: copyleft
Last push: 2022-08-20T13:57:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3952, "days_push": 1474, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3169, forks 829 (observed 2026-08-28T04:07:47.370122+00:00)

## What it is
A sequence-to-sequence learning add-on library for Keras, providing modular encoder-decoder layers and ready-made Seq2Seq models. It supports deep stacked LSTMs, attention mechanisms, and building models that can be used standalone or as layers in larger networks.

## Use cases
- build sequence-to-sequence models in Keras
- train a machine translation model
- create a chatbot with an encoder-decoder network
- implement attention-based seq2seq models
- build a neural parser for sequences
- stack LSTM layers for encoder and decoder

## When to choose
- you are using Keras and need seq2seq models with reusable encoder/decoder layers
- you want ready-made SimpleSeq2Seq or attention-based models out of the box
- you need to embed seq2seq components as layers inside a larger Keras model

## When to avoid
- you are using PyTorch, TensorFlow 2.x/Keras 3, or another framework instead of legacy Keras
- you need actively maintained code with recent updates and support
- you want modern transformer-based sequence models

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, nlp
- domain: machine-learning, deep-learning
- platform: python
- tags: seq2seq, keras, encoder-decoder, attention, machine-translation, neural-networks, natural-language-processing

## Member repositories
- farizrahman4u/seq2seq (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:47.370122+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 2026-08-29T18:45:30.637115+00:00, confidence not recorded.
  - readme: https://github.com/farizrahman4u/seq2seq (fetched 2026-08-28T04:07:47.370122+00:00, sha 0f9f38c44ece)
- Data as of 2026-08-30T08:39:29.467469+00:00.