https://github.com/ameritusweb/vectortongue
VectorTongue is an innovative language model that leverages vector-based neural networks and abstract syntactic representations to efficiently process and understand language with minimal data, democratizing access to advanced AI technologies.
https://github.com/ameritusweb/vectortongue
Last synced: 2 months ago
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VectorTongue is an innovative language model that leverages vector-based neural networks and abstract syntactic representations to efficiently process and understand language with minimal data, democratizing access to advanced AI technologies.
- Host: GitHub
- URL: https://github.com/ameritusweb/vectortongue
- Owner: ameritusweb
- License: lgpl-2.1
- Created: 2024-08-24T23:10:57.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-28T12:38:54.000Z (10 months ago)
- Last Synced: 2025-02-15T11:17:03.415Z (4 months ago)
- Language: C#
- Size: 83 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# VectorTongue
*VectorTongue* is an innovative and efficient language model that leverages vector-based neural networks to process and understand language through abstract representations such as parts of speech and syntax token types. Designed to be trained on limited datasets, VectorTongue democratizes access to small, powerful language models by focusing on structural and geometric aspects of language rather than relying on massive text corpora.
## Table of Contents
- [Overview](#overview)
- [Key Features](#key-features)
- [How It Works](#how-it-works)
- [Roadmap](#roadmap)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgements](#acknowledgements)
## Overview
VectorTongue represents a new paradigm in language modeling by abstracting away from traditional word-based inputs. Instead, it focuses on the underlying structure of language and code, using vector neural networks to learn from parts of speech, syntax token types, and geometric waveforms. This approach allows VectorTongue to be trained with far fewer data than traditional language models, making it accessible to developers and researchers with limited resources.
## Key Features
- **Data-Efficient Learning**: Train powerful language models using small datasets by focusing on syntactic and structural representations.
- **Vector Neural Networks**: Leverage the power of Bezier Waveform Vectors (BWV) to represent and process language and code.
- **Multi-Head Attention**: Utilize advanced attention mechanisms to focus on different aspects of the BWV representations.
- **Custom Operations**: Employ custom matrix multiplication and geometric operations to optimize learning and inference.
- **Democratized Access**: Lower the barrier to entry for developing language models, enabling a wider range of applications and innovation.
## How It Works
### Bezier Waveform Vectors (BWV)
Each neuron in VectorTongue is represented as a Bezier waveform vector, which models a phase of a waveform through control points. The waveform follows the direction of an overall vector and dissipates over a length determined by the vector's magnitude. This approach captures complex patterns in the data, allowing for rich feature representation with limited input.
### Custom Matrix Multiplication
VectorTongue introduces a custom matrix multiplication operation where traditional multiplication is replaced by subtracting BWVs. These operations are summed up across neurons, enabling the model to integrate information across different dimensions of the input.
### Multi-Head Self-Attention
To enhance the model's ability to focus on specific aspects of the data, VectorTongue uses a multi-head self-attention mechanism. This allows different attention heads to concentrate on various phases and regions of the BWV representations, leading to more nuanced and powerful learning.
## Roadmap
Planned features and improvements:
- [ ] Expand language and syntax support (e.g., additional programming languages, more complex syntactic structures).
- [ ] Develop visualization tools for BWV operations and model behavior.
- [ ] Integrate with popular IDEs for real-time syntax analysis and code suggestions.
- [ ] Enhance support for multilingual natural language processing.
- [ ] Implement advanced transfer learning capabilities.
## Contributing
Contributions are welcome! If you’d like to contribute to VectorTongue, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/YourFeature`).
3. Commit your changes (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature/YourFeature`).
5. Open a Pull Request.
## License
VectorTongue is licensed under the LGPL 2.1 License. See the [LICENSE](LICENSE) file for more information.
## Acknowledgements
Special thanks to the following projects and tools that inspired or contributed to VectorTongue:
- [SpaCy](https://spacy.io/) for natural language processing.
- [Roslyn](https://github.com/dotnet/roslyn) and [TypeScript Compiler API](https://www.typescriptlang.org/docs/handbook/compiler-api.html) for syntax analysis.
- [MKL.NET](https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/) for efficient vectorized operations.