Bert Base Arabic Camelbert Da Poetry

🚀 CAMeLBERT-DA Poetry Classification Model

A poetry classification model fine - tuned from CAMeLBERT Dialectal Arabic (DA) model

🚀 Quick Start

The CAMeLBERT - DA Poetry Classification Model can be used as part of the transformers pipeline. This model will also be available in CAMeL Tools soon.

To use the model with a transformers pipeline:

>>> from transformers import pipeline
>>> poetry = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-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': 0.9874765276908875},
 {'label': 'السلسلة', 'score': 0.6877778172492981}]

⚠️ Important Note

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

✨ Features

• The model is built by fine - tuning the CAMeLBERT Dialectal Arabic (DA) model.
• It uses the APCD dataset 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."

📚 Documentation

Model description

The CAMeLBERT - DA Poetry Classification Model is a poetry classification model. It was developed by fine - tuning the CAMeLBERT Dialectal Arabic (DA) model. For the fine - tuning process, the APCD dataset was used.

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

📄 License

The model 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.",
}