https://github.com/nikk0001/text-generation-with-markov-chains

This repository contains an implementation of a simple text generation algorithm using Markov chains. The algorithm builds a statistical model that predicts the probability of a word or character based on the previous one(s). This model can then be used to generate new text that mimics the style and structure of the input text.
https://github.com/nikk0001/text-generation-with-markov-chains

generative-adversarial-network gpt gpt-3 markovchain textgeneration

Last synced: about 2 months ago
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This repository contains an implementation of a simple text generation algorithm using Markov chains. The algorithm builds a statistical model that predicts the probability of a word or character based on the previous one(s). This model can then be used to generate new text that mimics the style and structure of the input text.

• Host: GitHub
• URL: https://github.com/nikk0001/text-generation-with-markov-chains
• Owner: NIKK0001
• Created: 2024-08-20T17:49:26.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-20T17:50:39.000Z (6 months ago)
• Last Synced: 2024-08-20T20:09:39.677Z (6 months ago)
• Topics: generative-adversarial-network, gpt, gpt-3, markovchain, textgeneration
• Language: Python
• Homepage:
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Text Generation with Markov Chains

This repository contains an implementation of a simple text generation algorithm using Markov chains. The algorithm builds a statistical model that predicts the probability of a word or character based on the previous one(s). This model can then be used to generate new text that mimics the style and structure of the input text.

## Table of Contents

- [Introduction](#introduction)

- [Installation](#installation)

- [Usage](#usage)

- [Examples](#examples)

- [Contributing](#contributing)

- [License](#license)

## Introduction

Markov chains are a type of probabilistic model that can be used for text generation. The basic idea is to model the probability of transitioning from one state (a word or character) to another based on the observed frequencies in a training corpus. This implementation focuses on generating text that resembles the input corpus by predicting the next word or character based on the current one.

## Installation

To run this project, you need to have Python installed. You can install the necessary dependencies using pip:

```bash

pip install -r requirements.txt