bert base arabic camelbert da sentiment

Introduction

The CAMeLBERT-DA SA Model is a sentiment analysis model tailored for Dialectal Arabic. It is built by fine-tuning the CAMeLBERT model using datasets like ASTD, ArSAS, and SemEval. This model can be integrated into the CAMeL Tools sentiment analysis component or used through the transformers pipeline.

Architecture

The model is based on the BERT architecture, specifically fine-tuned for sentiment analysis in Dialectal Arabic. It utilizes the pre-trained language model CAMeLBERT, which is optimized for Arabic language processing tasks.

Training

Training involved fine-tuning the CAMeLBERT model on specific datasets: ASTD, ArSAS, and SemEval. Details on the training process and hyperparameters used can be found in the paper, "The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."

Guide: Running Locally

1. Setup Environment: Ensure Python and necessary libraries, including transformers>=3.5.0, are installed.
2. Install CAMeL Tools:

   pip install camel-tools
   

3. Use Sentiment Analyzer:

   from camel_tools.sentiment import SentimentAnalyzer
   sa = SentimentAnalyzer("CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment")
   sentences = ['أنا بخير', 'أنا لست بخير']
   sa.predict(sentences)
   

4. Alternative: Use Transformers Pipeline:

   from transformers import pipeline
   sa = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment')
   sentences = ['أنا بخير', 'أنا لست بخير']
   sa(sentences)
   

5. Cloud GPUs: For faster processing, consider using cloud services like AWS, Google Cloud, or Azure, which offer GPU instances.

License

The CAMeLBERT-DA SA Model is released under the Apache 2.0 License.