https://github.com/coding-enthusiast9857/automatic_text_generation

Welcome to the repository, where innovation meets language! This repository is a comprehensive collection of tools, models, and resources dedicated to the exciting field of automatic text generation. Whether you're a researcher, developer, or enthusiast, this repository provides a playground for exploring cutting-edge technology.
https://github.com/coding-enthusiast9857/automatic_text_generation

ai ann cnn deep-learning deep-neural-networks gru keras lstm ml neural-networks nlp numpy python rnn tensorflow tensorflow2 text-processing

Last synced: 2 months ago
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Welcome to the repository, where innovation meets language! This repository is a comprehensive collection of tools, models, and resources dedicated to the exciting field of automatic text generation. Whether you're a researcher, developer, or enthusiast, this repository provides a playground for exploring cutting-edge technology.

• Host: GitHub
• URL: https://github.com/coding-enthusiast9857/automatic_text_generation
• Owner: CODING-Enthusiast9857
• License: mit
• Created: 2023-12-27T13:37:32.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-11T12:43:26.000Z (over 1 year ago)
• Last Synced: 2025-01-14T04:12:32.238Z (4 months ago)
• Topics: ai, ann, cnn, deep-learning, deep-neural-networks, gru, keras, lstm, ml, neural-networks, nlp, numpy, python, rnn, tensorflow, tensorflow2, text-processing
• Language: Jupyter Notebook
• Homepage:
• Size: 11.7 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Automatic Text Generation

![TensorFlow](https://img.shields.io/badge/TensorFlow-2.0-FF6F00?style=flat-square&logo=tensorflow&logoColor=white)

![Keras](https://img.shields.io/badge/Keras-2.4.3-D00000?style=flat-square&logo=keras&logoColor=white)

![PyTorch](https://img.shields.io/badge/PyTorch-1.7.0-EE4C2C?style=flat-square&logo=pytorch&logoColor=white)

![NLTK](https://img.shields.io/badge/NLTK-3.6.2-5E8B7E?style=flat-square)

![spaCy](https://img.shields.io/badge/spaCy-3.0-09a3d5?style=flat-square&logo=spacy&logoColor=white)

![Deep Learning](https://img.shields.io/badge/Deep%20Learning-4B8BF5?logo=deeplearning.ai&logoColor=white)

![Neural Networks](https://img.shields.io/badge/Neural%20Networks-0098D4?logo=neuralnetworks&logoColor=white)

![Text Generation](https://github.com/CODING-Enthusiast9857/Automatic_Text_Generation/blob/main/text_generation.png)

## Overview

This repository contains code and resources for Automatic Text Generation using various libraries and techniques. The goal is to explore and implement state-of-the-art methods in natural language processing (NLP) to generate coherent and contextually relevant text.

## Table of Contents

- [Introduction](#introduction)

- [Libraries Used](#libraries-used)

- [Techniques](#techniques)

- [Getting Started](#getting-started)

- [Usage](#usage)

- [Contributing](#contributing)

- [License](#license)

## 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.

## Libraries Used

- **[TensorFlow](https://www.tensorflow.org/):** An open-source machine learning framework for various tasks, including natural language processing and text generation.

- **[PyTorch](https://pytorch.org/):** A deep learning library that is widely used in research and industry for building neural network models, including those for text generation.

- **[GPT-3](https://www.openai.com/gpt-3/):** OpenAI's powerful language model, capable of performing a wide range of natural language tasks, including text generation.

- **[NLTK (Natural Language Toolkit)](https://www.nltk.org/):** A library for the Python programming language that provides tools for working with human language data.

- **[Spacy](https://spacy.io/):** An open-source library for advanced natural language processing in Python.

## Techniques

1. **Recurrent Neural Networks (RNN):** Traditional neural network architecture used for sequence modeling, including text generation.

2. **Long Short-Term Memory (LSTM):** A type of RNN architecture designed to overcome the vanishing gradient problem, often used for improved text generation.

3. **Gated Recurrent Unit (GRU):** Another variant of RNN similar to LSTM but with a simplified architecture.

4. **Transformer Models:** State-of-the-art models like GPT-3 and BERT that leverage attention mechanisms for better contextual understanding and text generation.

5. **Fine-tuning with GPT-3:** Learn how to fine-tune OpenAI's GPT-3 model for specific text generation tasks.

## Getting Started

To get started with this project, follow these steps:

1. Clone the repository:

    ```bash

    git clone https://github.com/CODING_Enthusiast9857/Automatic_Text_Generation.git

    ```

2. Install the required dependencies:

    ```bash

    pip install -r requirements.txt

    ```

3. Explore the code and notebooks to understand the implemented techniques.

## Usage

1. Use the provided scripts and notebooks for text generation tasks.

2. Experiment with different models and parameters to observe their impact on text quality.

## Contributing

Contributions are welcome! If you have ideas for improvements or find any issues, please open an issue or submit a pull request.

## License

This project is licensed under the [MIT License](LICENSE).

## Created by

Created with 🤍 by Madhavi Sonawane.

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