Vortex-Persian-NLP

Vortex-Persian-NLP is an advanced natural language processing (NLP) model tailored for the Persian language, built with cutting-edge deep learning architectures. This project utilizes the power of transformer-based models and LSTM layers, along with attention mechanisms to perform a wide variety of NLP tasks, including but not limited to, text classification, named entity recognition, machine translation, and text generation.

The core of the system revolves around a robust feedback loop for continual self-improvement, enabling the model to adapt based on evaluation metrics like BLEU, ROUGE, and METEOR. This system is designed to continuously learn from new data, optimize performance, and provide more accurate responses over time.

Key Features

• Deep Learning Models: Utilizes the latest transformer models (LLaMA, T5) integrated with attention mechanisms and LSTM layers.
• Self-improvement: Automatic fine-tuning based on BLEU, ROUGE, and METEOR evaluation metrics to ensure the model is continuously improving.
• Persian Language Support: Optimized for Persian NLP tasks with datasets like Persian SQuAD, Persian Q&A, and WikiPersian.
• Data Augmentation: Includes multiple augmentation techniques such as synonym replacement, backtranslation, and text summarization for diverse training data.
• Optimized for Performance: Efficient processing with GPU support, reducing inference time while maintaining high accuracy.
• Modular Architecture: Designed for easy extensibility and customization to suit a wide range of NLP tasks.

Model Architecture

• LSTM (Long Short-Term Memory): Used for sequential modeling tasks, enhanced with an attention mechanism to focus on the most relevant parts of the input text.
• Self-Attention and Multi-Head Attention: These components allow the model to capture contextual relationships in the input text, improving the performance of tasks such as machine translation and text generation.
• Positional Encoding & Residual Connections: Employed for better learning of sequential data, ensuring the model can understand word order and relationships between distant words.
• Transformer (LLaMA/T5): Integrated transformer-based architecture that leverages state-of-the-art language models such as LLaMA and T5 for superior language understanding.

Installation

Requirements

• Python 3.7 or higher
• PyTorch 1.10.0 or higher
• CUDA-enabled GPU (optional, for faster training and inference)
• Other dependencies listed in requirements.txt

Steps to Install

1. Clone the repository:

      git clone https://github.com/Arash-Mansourpour/VortexAI-Persian.git
      cd VortexAI-Persian
   

2. Install the required dependencies:

   pip install -r requirements.txt

3. (Optional) If you're using GPU, ensure you have CUDA installed for PyTorch.

4. Run the model:

   python REV119.py

Datasets

• Persian SQuAD: A Persian version of the popular SQuAD dataset for training question answering systems.
• Persian Q&A Dataset: A dataset focused on Persian question-answering tasks.
• WikiPersian: Persian Wikipedia text for pre-training and fine-tuning models on large-scale text corpora.

How It Works

The Vortex-Persian-NLP model employs a transformer-based architecture combined with LSTM and attention layers. Here’s how the model works:

1. Data Preprocessing: The input text is tokenized and converted into a format suitable for training with embeddings. Augmentation techniques are applied to create diverse training samples.
2. Model Training: The model is trained using the combined datasets, including Persian-specific corpora, to ensure that it captures the nuances of the Persian language.
3. Evaluation and Feedback: Performance metrics like BLEU and ROUGE are used to evaluate the model. Based on the results, the model self-improves through regular fine-tuning.
4. Inference: Once trained, the model can be used for a variety of NLP tasks such as machine translation, text generation, and more.

Evaluation Metrics

• BLEU (Bilingual Evaluation Understudy): Used for machine translation tasks to measure the quality of text translation.
• ROUGE (Recall-Oriented Understudy for Gisting Evaluation): A metric for evaluating the quality of summaries, focusing on recall.
• METEOR (Metric for Evaluation of Translation with Explicit ORdering): Another metric for evaluating machine translation, which takes into account synonyms and word order.

Usage Examples

Text Generation

from model import VortexModel

model = VortexModel.load_pretrained() generated_text = model.generate_text("The future of NLP in Persian is") print(generated_text)

question = "Who is the president of Iran?" context = "The president of Iran is Ebrahim Raisi." answer = model.answer_question(question, context) print(answer)

Contributing We welcome contributions to Vortex-Persian-NLP! Please feel free to fork the repository, submit issues, and send pull requests. Whether it's fixing bugs, adding new features, or improving documentation, every contribution is appreciated!

How to Contribute Fork the repository Create a new branch (git checkout -b feature-branch) Commit your changes (git commit -am 'Add new feature') Push to the branch (git push origin feature-branch) Create a new pull request License This project is licensed under the MIT License. You can freely use, modify, and distribute the code, but please credit the original authors.

Acknowledgments Google Research for the T5 model and transformer architecture. OpenAI for contributions in the development of state-of-the-art language models. The Persian NLP community for the valuable datasets and research that made this project possible.

Key Sections Breakdown:

1. Introduction: Describes the project, its purpose, and the underlying technology.
2. Key Features: Outlines the major capabilities and advantages of your model.
3. Model Architecture: Explains the architecture, components, and design choices in detail.
4. Installation: Provides instructions on how to set up and run the project.
5. Datasets: Specifies the datasets used to train the model.
6. How It Works: Walkthrough of the model’s pipeline from data preprocessing to inference.
7. Evaluation Metrics: Lists the evaluation metrics used to assess the model’s performance.
8. Usage Examples: Shows sample code for running the model.
9. Contributing: Invites contributions and outlines the process for contributing to the project.
10. License: Specifies the project’s licensing terms, making it open-source under the MIT License.
11. Acknowledgments: Credits to libraries, frameworks, and individuals who contributed to the project.

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