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Textgenrnn

Textgenrnn is a Python 3 module built on Keras and TensorFlow for training recurrent neural networks on text datasets and generating text from trained or included pretrained models.

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Understand the project

Practical overview

When it fits

Use Textgenrnn when you want a Python-based workflow for training recurrent text-generation models on text files or generating from pretrained weights.

Who it is for

  • Python users who want to train text generators on their own text files.
  • Developers experimenting with character- or word-level recurrent models and generation settings.
  • Users who want interactive, step-by-step control over generated text.

Documented uses

  • Train a text generator on a supplied corpus.
  • Generate text using an included pretrained model or a model trained on a custom dataset.
  • Explore generated continuations by selecting among suggested next characters or words.
Before you choose

Requirements & limits

  • The README specifies Python 3 and TensorFlow 2.1.0 as the minimum TensorFlow version for the latest release.
  • Training can use a GPU, with CPU generation from a trained model also described.
  • Contextual training requires context labels alongside text documents.
  • The package includes pretrained weights and example datasets, as described in the README.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install textgenrnn and generate text with the included model

Install the Python module and generate text using its included pretrained model.

Before you start

  • Python 3
  • TensorFlow 2.1.0 or later for the latest textgenrnn version

Steps

  1. Install the package with the command below.

    Documented command
    pip3 install textgenrnn

  2. In Python, import textgenrnn from the module and create an instance with textgen = textgenrnn().

  3. Call textgen.generate() to generate text.

Expected outcome

The included pretrained model generates text.

Guide limitations

  • The stated minimum TensorFlow version applies to the latest textgenrnn version.
  • The README does not specify generated-text quality or guarantee a particular output.
Documentation 1 ↗
Source-based practical guide

Train the included model on a text file and generate text

Fine-tune the included model on a text dataset, then generate text from it.

Before you start

  • Python 3
  • TensorFlow 2.1.0 or later for the latest textgenrnn version
  • A generic input text file, such as hacker_news_2000.txt

Steps

  1. Install the package with the command below.

    Documented command
    pip3 install textgenrnn

  2. In Python, import textgenrnn and create an instance with textgen = textgenrnn().

  3. Train on the file with textgen.train_from_file('hacker_news_2000.txt', num_epochs=1), replacing the example filename with the input text file.

  4. Call textgen.generate().

Expected outcome

The included model is trained on the input text file and generates text.

Guide limitations

  • The example uses one training epoch; the README does not promise a particular output or quality.
  • The README also describes creating a new model with new_model=True, but this recipe covers only training the included model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
minimaxir/textgenrnn · Official homepage title · retained in its source language
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