Instructions to use madha98/Text_Generation_LSTM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use madha98/Text_Generation_LSTM with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://madha98/Text_Generation_LSTM") - Notebooks
- Google Colab
- Kaggle
Download README.md from madha98/Text_Generation_LSTM: direct link, hf CLI and curl.
- Browser
- Download file 1.01 kB
-
https://huggingface.co/madha98/Text_Generation_LSTM/resolve/main/README.md
- Command line
-
hf download hf://madha98/Text_Generation_LSTM/README.md
-
curl -L -o README.md https://huggingface.co/madha98/Text_Generation_LSTM/resolve/main/README.md
license: mit
datasets:
- madha98/Shakespeare
language:
- en
library_name: keras
pipeline_tag: text-generation
tags:
- text-generation-inference
Automatic Text Generation Using LSTM
Overview
It contains code and resources for Automatic Text Generation. The goal is to explore and implement state-of-the-art methods in natural language processing (NLP) to generate coherent and contextually relevant text.
Introduction
Text generation is a fascinating field within natural language processing that involves creating textual content using machine learning models. This project aims to showcase different techniques and libraries for automatic text generation, providing a starting point for enthusiasts and practitioners interested in this area. Long Short-Term Memory (LSTM): A type of RNN architecture designed to overcome the vanishing gradient problem, often used for improved text generation.
License
This project is licensed under the MIT License