readme: ---
  language: 
  - ar
  license: apache-2.0
  widget:
   - text: "أنا بخير"
  ---
  # CAMeLBERT MSA SA Model
  ## Model description
  **CAMeLBERT MSA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
  For the fine-tuning, we used the [ASTD](https://aclanthology.org/D15-1299.pdf), [ArSAS](http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf), and [SemEval](https://aclanthology.org/S17-2088.pdf) datasets.
  Our 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](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT).
  
  ## Intended uses
  You can use the CAMeLBERT MSA SA model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component (*recommended*) or as part of the transformers pipeline.
  #### How to use
  To use the model with the [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) SA component:
  ```python
  >>> 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']
  ```
  You can also use the SA model directly with a transformers pipeline:
  ```python
  >>> 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}]
  ```
  *Note*: to download our models, you would need `transformers>=3.5.0`.
  Otherwise, you could download the models manually.
  
  ## Citation
  ```bibtex
  @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.",
  }
  ```