https://github.com/teleprint-me/vektor
Vektor: A personal NLP toolkit for text processing with a focus on Transformer architecture.
https://github.com/teleprint-me/vektor
artificial-intelligence machine-learning natural-language-processing neural-network
Last synced: 21 days ago
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Vektor: A personal NLP toolkit for text processing with a focus on Transformer architecture.
- Host: GitHub
- URL: https://github.com/teleprint-me/vektor
- Owner: teleprint-me
- License: mit
- Archived: true
- Created: 2023-11-09T04:55:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-26T05:35:19.000Z (over 1 year ago)
- Last Synced: 2025-02-22T16:15:36.347Z (3 months ago)
- Topics: artificial-intelligence, machine-learning, natural-language-processing, neural-network
- Language: Python
- Homepage:
- Size: 338 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Vektor
## Project Overview
Vektor is a personal project that serves as my playground for delving into the
intricacies of Natural Language Processing (NLP) and Transformer models. As a
self-initiated educational endeavor, it's primarily focused on deepening my
understanding of these complex topics from the ground up.
## Current Focus
The project initially began as an exploration into encoding, decoding, and
tokenization. However, it has since evolved into a more comprehensive study,
particularly focusing on implementing the skip gram model and understanding
Transformer architecture. This evolution stems from my realization that
mastering these concepts in Python, a language I'm more comfortable with, is
more feasible than tackling them in C/C++ as initially attempted.
## Personal Learning Journey
Vektor is not just about the final output but more about the journey and the
learning process. The project is a testament to the iterative nature of
learning, especially in the complex domain of NLP and machine learning. It's a
reflection of my hands-on approach to understanding and building these systems
from scratch, using only essential libraries and tools to reinforce fundamental
learning.
## Status and Goals
As of now, Vektor remains a work-in-progress, a testament to the ongoing journey
of learning and discovery in the field of NLP and AI. The main goals include:
- Gaining a deeper understanding of Transformer models and their related
components.
- Experimenting with different aspects of NLP, including tokenization and
encoding, memory, cognitive architecture, and more.
- Keeping the project open for new directions and learnings as they arise.
## Documentation
For a deeper dive into the Vektor project, the documentation provides
comprehensive insights and details:
- **Project Overview**: For a detailed look at the overall design, architecture,
and goals of the Vektor project, see
[Project Overview](docs/VektorProjectOverview.md).
- **Technical Documentation**: Explore various technical aspects, including
tokenization, encoding, and model implementation, in the dedicated
[technical documents](docs/). This includes:
- [Tokenization and Encoding](docs/tokenization/)
- [Model Architecture](docs/model/)
- **Upcoming Documentation**: I'm continually working on expanding the
documentation to cover more aspects of the project. Keep an eye on the `docs/`
directory for the latest updates.
The documentation is a living entity and is regularly updated to reflect the
latest progress and insights into the Vektor project.
## Contributions and Collaborations
While Vektor is a personal project, input, suggestions, and discussions are
always welcome. They can provide fresh perspectives and insights, which are
invaluable in a learning journey like this.