Model

🚀 Miscovery Transformer Model

A transformer-based encoder-decoder model designed for multiple NLP tasks, including text summarization, English-Arabic translation, and question-answering.

🚀 Quick Start

This model is a transformer-based encoder-decoder model that can handle various NLP tasks such as text summarization, English-Arabic translation, and question-answering.

✨ Features

• Text Summarization: Condense long texts into concise summaries.
• Translation: Translate between English and Arabic.
• Question-Answering: Provide answers to questions based on given text.

📦 Installation

1. Install the package:

pip install miscovery-model

💻 Usage Examples

Basic Usage

from miscovery_model import standard_pipeline

# Create a pipeline
model = standard_pipeline("miscovery/model")

# Use it
result = model("Translate this to Arabic: What year did World War I begin?")
print(result)

📚 Documentation

Model Architecture

Training

The model was trained in two stages:

1. Pre-training on sentence rearrangement tasks
2. Fine-tuning on downstream tasks

Limitations

⚠️ Important Note

This model was trained on specific datasets and may not generalize well to all domains.

📄 License

This project is licensed under the MIT License.