dancanyego/Text-Generation-

For this project we will use LSTMs With Keras and Python

Text-Generation-with Neural Networks

For this project we will use LSTMs With Keras and Python This repository contains code and resources for text generation using neural networks. Text generation is a fascinating application of deep learning and natural language processing (NLP), allowing you to generate human-like text in various styles and domains. Whether you want to create creative stories, poetry, or automate content generation, this project can serve as a starting point for your text generation endeavors.

Table of Contents

• Introduction
• Installation
• Usage
• Models
• Data
• Training
• Evaluation
• Extra
• Contributing

Introduction

Text generation with neural networks involves training a model to predict the next word or character in a sequence of text, given the preceding context. This repository provides a framework for building and training text generation models using popular deep learning libraries such as TensorFlow or PyTorch.

Installation

1. Clone this repository to your local machine:

   git clone https://github.com/dancanyego/Text-Generation-.git

2. Install the required dependencies:

Usage

• To generate text using a pre-trained model, you can use the provided scripts or integrate the model into your own applications.

• If you want to train your own text generation model, load the .h5 file and the tokenizer

Models

This repository includes pre-trained models for various text generation tasks. You can find them in the models/ directory. Each model may be tailored for specific use cases, such as:

• Character-level text generation
• Word-level text generation
• Fine-tuned models for specific domains (e.g., poetry, Short stories)
• A fine tuned Chatbot that can answer Questions based on a 'story' it is given

Data

-The quality and diversity of your training data greatly affect the performance of your text generation model. You can use your own datasets or find publicly available text corpora to train your models. Ensure your data is preprocessed and organized properly before training.

• The Chatbot data is derived from the Facebook BaBi dataset

Training

To train a text generation model, follow these general steps:

1. Prepare your dataset and preprocess the text.
2. Choose a neural network architecture (e.g., LSTM, GPT, Transformer).
3. Define training hyperparameters and configurations.
4. Train the model using your dataset and configurations.
5. Save the trained model for later use.

Detailed instructions and example code can be found in the training/ directory.

Evaluation

Evaluating the quality of generated text can be challenging. Common evaluation metrics include perplexity, BLEU score, and human evaluation. Implement evaluation scripts or use established NLP libraries to assess the generated text's quality.

Extra

You can find usage examples and sample code in the chatbot/ directory. These examples demonstrate how to generate text using pre-trained models and provide a starting point for customizing the text generation process.

Contributing

Contributions to this project are welcome! Whether you want to add new features, fix bugs, or improve documentation, please read our Contributing Guidelines before getting started.

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Enjoy experimenting with text generation using neural networks!