Ara B E R T summarization goud

Introduction

The AraBERT-summarization model is an encoder-decoder model designed for text summarization tasks in Moroccan Arabic and Modern Standard Arabic. It is based on the BERT architecture, specifically fine-tuned using the Goud dataset.

Architecture

The model utilizes a transformer-based encoder-decoder architecture. It was initialized with the bert-base-arabertv02-twitter checkpoint. The model is capable of processing input text and generating concise summaries, benefiting from the robust language understanding capabilities of BERT tailored to Arabic dialects.

Training

The AraBERT-summarization model was fine-tuned using the Goud dataset, which is specifically designed for summarization tasks in Moroccan Arabic. The training process involved optimizing the model to generate accurate and coherent summaries for input articles.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install the Transformers Library:

   pip install transformers
   

2. Load the Model and Tokenizer:

   from transformers import EncoderDecoderModel, BertTokenizer
   
   tokenizer = BertTokenizer.from_pretrained("Goud/AraBERT-summarization-goud")
   model = EncoderDecoderModel.from_pretrained("Goud/AraBERT-summarization-goud")
   

3. Prepare Input Text:

   article = """Your article text here..."""
   

4. Generate Summary:

   input_ids = tokenizer(article, return_tensors="pt", truncation=True, padding=True).input_ids
   generated = model.generate(input_ids)[0]
   output = tokenizer.decode(generated, skip_special_tokens=True)
   print(output)
   

5. Cloud GPU Suggestions: For enhanced performance, consider using cloud services such as AWS, Google Cloud, or Azure, which offer GPU resources for model inference.

License

The model and associated datasets are provided under the terms specified by Hugging Face and the original authors. Users must ensure compliance with applicable licenses when using the model for research or commercial purposes.