Character LSTM

Character LSTM

Runs a character-level LSTM sequence-to-sequence model from the Keras team in a Gradio page, turning input text into output text one character at a time. For machine learning learners.

Made by keras-io on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: This Gradio app runs a character-level LSTM sequence-to-sequence model from the Keras team. By its name, the model reads text one character at a time and writes text one character at a time. Two vocabulary files, one for input and one for target, ship with the app.

Who it suits: It suits machine learning learners who want to see a classic sequence-to-sequence model running, and developers who want a small Gradio example around a Keras model. The record does not say which task the model was trained for.

Industry | Who uses it | How they use it | Example
Education | Machine learning students | See a character-level sequence model in action | Type a short text and read what comes back
Research | NLP hobbyists | Study how vocabulary files map characters | Open input_vocab.json and target_vocab.json
Software | Python developers | Read a small Gradio wrapper around a Keras model | Open app.py and requirements.txt

You can skip it if you need a modern, production-grade model.

Where to use it:

• A web browser, on the Space's page on Hugging Face
• Windows, macOS or Linux with Python, from a downloaded copy

Use case | Who | What they get back
Try the model | Anyone | Output text for the text you enter
Study the vocabularies | Learners | Two JSON files for input and target characters
Reuse as a base | Developers | A small Gradio app around a Keras model

How to set it up:

• Nothing at all if you use the online Space
• Python on your computer, only if you run a copy
• The packages listed in requirements.txt

1. Open the Space's page in a browser to use it with nothing to install.
2. To run a copy, install the Hugging Face Hub package and download the Space.
3. Move into the new folder, then install Gradio and the listed requirements.
4. Start the app with Python.

pip install -U huggingface_hub
hf download keras-io/char-lstm-seq2seq --repo-type space --local-dir char-lstm-seq2seq
cd char-lstm-seq2seq
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The record does not list the controls, so look at the page.

python app.py

Example: You open the Space and type a short piece of text. The app passes it to the model and shows the text it produces. The record shows no sample, so the language and length it accepts depend on the page.

For newcomers and experts: A sequence-to-sequence model reads one sequence and writes another, like turning a sentence into a translation. Start by opening the Space, typing a short input and seeing what the model writes.

If you know the field, an LSTM encoder and decoder work on single characters, so the vocabularies stay small. The record gives no training task, data or accuracy figures.

You type a short text
Input characters are mapped with input_vocab.json
The LSTM model writes output characters
target_vocab.json turns them back into text

Good to know:

• License: Apache 2, as listed on the Space
• Cost to run: none named
• Data it reaches: the text you enter and the model it names
• Not for: the record names no exclusions, and it gives no training task or accuracy figures

On Hugging Face, 13 November 2026

• Likes: 1
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/keras-io/char-lstm-seq2seq
• Files: https://huggingface.co/spaces/keras-io/char-lstm-seq2seq/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.