--- title: "Character LSTM" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a8e95fed-aa19-435b-b5b1-75aa7cc3f8b4" updated: "2026-11-13" --- # Character LSTM > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Character LSTM is a kit in the Education category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by keras-io ## 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. ```sh 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [keras-io/char-lstm-seq2seq on Hugging Face](https://huggingface.co/spaces/keras-io/char-lstm-seq2seq) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Character LSTM (this page) | 1 | gradio | apache-2.0 | 2022-02-07 | | [Unit 1 Quiz - AI Agent Fundementals](https://mindket.com/p/1b65056a-a2b2-4179-a81c-bdffa3636205) | 360 | gradio | apache-2.0 | 2025-06-24 | | [Unit 2.1 smolagents Code Quiz](https://mindket.com/p/c9f42604-0003-4117-9a4b-2b785f68ee99) | 63 | gradio | apache-2.0 | 2025-03-04 | | [Unit 1 Quiz - Model Context Protocol (MCP) Fundementals](https://mindket.com/p/f7210665-ca4c-423f-b45d-a1c6fa549eec) | 58 | gradio | apache-2.0 | 2025-06-03 | | [Tamazight Text-to-Speech](https://mindket.com/p/ab94f6a3-34fa-4c03-9fd0-751ff4f282eb) | 41 | gradio | cc-by-4.0 | 2023-11-25 | | [Embedding Draw Challenge](https://mindket.com/p/76690a14-5bc9-4d53-ad3e-d47e7218918f) | 30 | gradio | apache-2.0 | 2026-10-06 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-02-07 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open keras-io/char-lstm-seq2seq on Hugging Face](https://huggingface.co/spaces/keras-io/char-lstm-seq2seq) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a8e95fed-aa19-435b-b5b1-75aa7cc3f8b4) - [Its page on Mindket](https://mindket.com/p/a8e95fed-aa19-435b-b5b1-75aa7cc3f8b4)