readme: ---
  language: 
  - ar
  license: apache-2.0
  widget:
   - text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
  ---
  # CAMeLBERT-CA Poetry Classification Model
  ## Model description
  **CAMeLBERT-CA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
  For the fine-tuning, we used the [APCD](https://arxiv.org/pdf/1905.05700.pdf) dataset.
  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-CA Poetry Classification model as part of the transformers pipeline.
  This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
  #### How to use
  To use the model with a transformers pipeline:
  ```python
  >>> from transformers import pipeline
  >>> poetry = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry')
  >>> # A list of verses where each verse consists of two parts.
  >>> verses = [
          ['الخيل والليل والبيداء تعرفني' ,'والسيف والرمح والقرطاس والقلم'],
          ['قم للمعلم وفه التبجيلا' ,'كاد المعلم ان يكون رسولا']
      ]
  >>> # A function that concatenates the halves of each verse by using the [SEP] token.
  >>> join_verse = lambda half: ' [SEP] '.join(half)
  >>> # Apply this to all the verses in the list.
  >>> verses = [join_verse(verse) for verse in verses]
  >>> poetry(sentences)
  [{'label': 'البسيط', 'score': 0.9845284819602966},
   {'label': 'الكامل', 'score': 0.752918004989624}]
  ```
  *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.",
  }
  ```