README.md

metadata

language: multilingual
license: mit
tags:
  - transformer
  - summarization
  - translation
  - question-answering
  - english
  - arabic
datasets:
  - miscovery/arabic_egypt_english_world_facts
pipeline_tag: summarization
library_name: transformers

Miscovery Transformer Model

This model is a transformer-based encoder-decoder model for multiple NLP tasks:

• Text summarization
• Translation (English-Arabic)
• Question-answering

Model Architecture

• Model type: miscovery
• Number of parameters: 485674144
• Encoder layers: 12
• Decoder layers: 12
• Attention heads: 12
• Hidden size: 768
• Feed-forward size: 3072

Training

The model was trained in two stages:

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

Usage

1. Install the package:

pip install miscovery-model

2. Run the model using a script:

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)

Limitations

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