# hunkim/word-rnn-tensorflow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.

Repository: https://github.com/hunkim/word-rnn-tensorflow
Canonical: https://ross.abutalabs.com/products/word-rnn-tensorflow
Language: Python
License: MIT
License Family: permissive
Topics: rnn, tensorflow, rnn-tensorflow, lstm, python
Last push: 2019-10-09T18:46:51+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3779, "days_push": 2520, "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 1294, forks 484 (observed 2026-08-28T04:04:16.304749+00:00)

## What it is
A TensorFlow implementation of multi-layer recurrent neural networks (LSTM/RNN) for training word-level language models, adapted from char-rnn-tensorflow. It ships train and sample scripts that support greedy, weighted, and beam-search decoding for generating text from any training corpus.

## Use cases
- train a word-level language model on my own text corpus
- generate shakespeare-like text with an LSTM
- learn how RNN language models are implemented in TensorFlow
- sample text from a trained LSTM using beam search
- compare word-level versus character-level language models
- build a multi-layer LSTM text generator in Python

## When to choose
- you want a small, readable reference implementation of word-level LSTM language modeling
- you are working with legacy TensorFlow 1.x and need a working char-rnn-style trainer
- you want to experiment with beam search decoding for sequence generation

## When to avoid
- you need a maintained codebase - it requires TensorFlow 1.1 and has seen no releases since 2019
- you want state-of-the-art language models such as transformers
- you need production-grade text generation - output quality is research-demo level

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, nlp
- domain: machine-learning, deep-learning
- platform: python
- tags: tensorflow, lstm, rnn, language-model, text-generation, beam-search, char-rnn, recurrent-neural-networks, shakespeare, educational, natural-language-processing

## Member repositories
- hunkim/word-rnn-tensorflow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:16.304749+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 2026-08-30T04:54:05.219075+00:00, confidence not recorded.
  - readme: https://github.com/hunkim/word-rnn-tensorflow (fetched 2026-08-28T04:04:16.304749+00:00, sha 921a53d2f3fe)
- Data as of 2026-08-30T08:39:29.467469+00:00.