{"id":20259647,"url":"https://github.com/coding-enthusiast9857/automatic_text_generation","last_synced_at":"2026-04-12T09:33:03.091Z","repository":{"id":214347804,"uuid":"736284157","full_name":"CODING-Enthusiast9857/Automatic_Text_Generation","owner":"CODING-Enthusiast9857","description":"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.","archived":false,"fork":false,"pushed_at":"2024-01-11T12:43:26.000Z","size":12272,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-14T04:12:32.238Z","etag":null,"topics":["ai","ann","cnn","deep-learning","deep-neural-networks","gru","keras","lstm","ml","neural-networks","nlp","numpy","python","rnn","tensorflow","tensorflow2","text-processing"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CODING-Enthusiast9857.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-12-27T13:37:32.000Z","updated_at":"2024-11-23T18:40:54.000Z","dependencies_parsed_at":"2024-01-18T18:25:37.964Z","dependency_job_id":"47648081-6e9a-4c4a-b8c4-b58575c49f8f","html_url":"https://github.com/CODING-Enthusiast9857/Automatic_Text_Generation","commit_stats":{"total_commits":21,"total_committers":2,"mean_commits":10.5,"dds":0.1428571428571429,"last_synced_commit":"08bc4dba7fe2e852f070742782faa07708a91141"},"previous_names":["coding-enthusiast9857/automatic_text_generation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODING-Enthusiast9857%2FAutomatic_Text_Generation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODING-Enthusiast9857%2FAutomatic_Text_Generation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODING-Enthusiast9857%2FAutomatic_Text_Generation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CODING-Enthusiast9857%2FAutomatic_Text_Generation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CODING-Enthusiast9857","download_url":"https://codeload.github.com/CODING-Enthusiast9857/Automatic_Text_Generation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241720118,"owners_count":20008916,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","ann","cnn","deep-learning","deep-neural-networks","gru","keras","lstm","ml","neural-networks","nlp","numpy","python","rnn","tensorflow","tensorflow2","text-processing"],"created_at":"2024-11-14T11:15:47.370Z","updated_at":"2026-04-12T09:32:58.044Z","avatar_url":"https://github.com/CODING-Enthusiast9857.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Automatic Text Generation\n\n![TensorFlow](https://img.shields.io/badge/TensorFlow-2.0-FF6F00?style=flat-square\u0026logo=tensorflow\u0026logoColor=white)\n![Keras](https://img.shields.io/badge/Keras-2.4.3-D00000?style=flat-square\u0026logo=keras\u0026logoColor=white)\n![PyTorch](https://img.shields.io/badge/PyTorch-1.7.0-EE4C2C?style=flat-square\u0026logo=pytorch\u0026logoColor=white)\n![NLTK](https://img.shields.io/badge/NLTK-3.6.2-5E8B7E?style=flat-square)\n![spaCy](https://img.shields.io/badge/spaCy-3.0-09a3d5?style=flat-square\u0026logo=spacy\u0026logoColor=white)\n![Deep Learning](https://img.shields.io/badge/Deep%20Learning-4B8BF5?logo=deeplearning.ai\u0026logoColor=white)\n![Neural Networks](https://img.shields.io/badge/Neural%20Networks-0098D4?logo=neuralnetworks\u0026logoColor=white)\n\n![Text Generation](https://github.com/CODING-Enthusiast9857/Automatic_Text_Generation/blob/main/text_generation.png)\n\n## Overview\n\nThis 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.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Libraries Used](#libraries-used)\n- [Techniques](#techniques)\n- [Getting Started](#getting-started)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Introduction\n\nText 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.\n\n## Libraries Used\n\n- **[TensorFlow](https://www.tensorflow.org/):** An open-source machine learning framework for various tasks, including natural language processing and text generation.\n\n- **[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.\n\n- **[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.\n\n- **[NLTK (Natural Language Toolkit)](https://www.nltk.org/):** A library for the Python programming language that provides tools for working with human language data.\n\n- **[Spacy](https://spacy.io/):** An open-source library for advanced natural language processing in Python.\n\n## Techniques\n\n1. **Recurrent Neural Networks (RNN):** Traditional neural network architecture used for sequence modeling, including text generation.\n\n2. **Long Short-Term Memory (LSTM):** A type of RNN architecture designed to overcome the vanishing gradient problem, often used for improved text generation.\n\n3. **Gated Recurrent Unit (GRU):** Another variant of RNN similar to LSTM but with a simplified architecture.\n\n4. **Transformer Models:** State-of-the-art models like GPT-3 and BERT that leverage attention mechanisms for better contextual understanding and text generation.\n\n5. **Fine-tuning with GPT-3:** Learn how to fine-tune OpenAI's GPT-3 model for specific text generation tasks.\n\n## Getting Started\n\nTo get started with this project, follow these steps:\n\n1. Clone the repository:\n\n    ```bash\n    git clone https://github.com/CODING_Enthusiast9857/Automatic_Text_Generation.git\n    ```\n\n2. Install the required dependencies:\n\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n3. Explore the code and notebooks to understand the implemented techniques.\n\n## Usage\n\n1. Use the provided scripts and notebooks for text generation tasks.\n\n2. Experiment with different models and parameters to observe their impact on text quality.\n\n## Contributing\n\nContributions are welcome! If you have ideas for improvements or find any issues, please open an issue or submit a pull request.\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n\n## Created by\nCreated with \u0026#129293; by \u003ca href=\"https://github.com/CODING-Enthusiast9857\" target=\"_blank\"\u003eMadhavi Sonawane.\u003c/a\u003e\n\n\u003cb\u003eFollow \u003ca href=\"https://github.com/CODING-Enthusiast9857\" target=\"_blank\"\u003eMadhavi Sonawane\u003c/a\u003e for more such contents. \n\u003cbr\u003e 🇹​​​​​🇭​​​​​🇦​​​​​🇳​​​​​🇰​​​​​ 🇾​​​​​🇴​​​​​🇺​​​​​ for visiting...!!\u003c/b\u003e \n\u003cbr\u003e\n\n### Happy CODING...!! 💻\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoding-enthusiast9857%2Fautomatic_text_generation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcoding-enthusiast9857%2Fautomatic_text_generation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoding-enthusiast9857%2Fautomatic_text_generation/lists"}