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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://madha98/Text_Generation_LSTM") - Notebooks
- Google Colab
- Kaggle
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
Happy CODING...!! π»
- Downloads last month
- -