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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: 24 days 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.

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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.



Follow Madhavi Sonawane for more such contents. 


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### Happy CODING...!! 💻