Bert Base Arabic Camelbert Msa Sentiment

🚀 CAMeLBERT MSA SA Model

The CAMeLBERT MSA SA Model is a Sentiment Analysis (SA) model. It addresses the need for accurate sentiment analysis in Arabic text, leveraging pre - trained language models to provide reliable results.

🚀 Quick Start

You can use the CAMeLBERT MSA SA model directly as part of our CAMeL Tools SA component (recommended) or as part of the transformers pipeline.

✨ Features

• Built by fine - tuning the CAMeLBERT Modern Standard Arabic (MSA) model.
• Utilizes multiple datasets including ASTD, ArSAS, and SemEval for fine - tuning.
• The fine - tuning procedure and hyperparameters are detailed in the paper "The Interplay of Variant, Size, and Task Type in Arabic Pre - trained Language Models".

📦 Installation

Note: to download our models, you would need transformers>=3.5.0. Otherwise, you could download the models manually.

💻 Usage Examples

Basic Usage

# To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component:
>>> from camel_tools.sentiment import SentimentAnalyzer
>>> sa = SentimentAnalyzer("CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment")
>>> sentences = ['أنا بخير', 'أنا لست بخير']
>>> sa.predict(sentences)
>>> ['positive', 'negative']

Advanced Usage

# You can also use the SA model directly with a transformers pipeline:
>>> from transformers import pipeline
>>> sa = pipeline('sentiment-analysis', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment')
>>> sentences = ['أنا بخير', 'أنا لست بخير']
>>> sa(sentences)
[{'label': 'positive', 'score': 0.9616648554801941},
 {'label': 'negative', 'score': 0.9779177904129028}]

📚 Documentation

The fine - tuning procedure and the hyperparameters we used can be found in our paper "The Interplay of Variant, Size, and Task Type in Arabic Pre - trained Language Models." Our fine - tuning code can be found here.

📄 License

This project is licensed under the Apache - 2.0 license.

📄 Citation

@inproceedings{inoue-etal-2021-interplay,
    title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
    author = "Inoue, Go  and
      Alhafni, Bashar  and
      Baimukan, Nurpeiis  and
      Bouamor, Houda  and
      Habash, Nizar",
    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine (Online)",
    publisher = "Association for Computational Linguistics",
    abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}