Bert Base Arabic Camelbert Mix Pos Glf

🚀 CAMeLBERT-Mix POS-GLF Model

CAMeLBERT-Mix POS-GLF Model is a Gulf Arabic POS tagging model. It offers accurate part - of - speech tagging for Gulf Arabic text, enhancing natural language processing tasks in this dialect.

🚀 Quick Start

You can use the CAMeLBERT-Mix POS-GLF model as part of the transformers pipeline. This model will also be available in CAMeL Tools soon.

✨ Features

• Fine - tuned Model: Built by fine - tuning the [CAMeLBERT - Mix](https://huggingface.co/CAMeL - Lab/bert - base - arabic - camelbert - mix/) model.
• Dedicated Dataset: Utilized the [Gumar](https://camel.abudhabi.nyu.edu/annotated - gumar - corpus/) dataset for fine - tuning.
• Research - Backed: 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

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

💻 Usage Examples

Basic Usage

To use the model with a transformers pipeline:

>>> from transformers import pipeline
>>> pos = pipeline('token - classification', model='CAMeL - Lab/bert - base - arabic - camelbert - mix - pos - glf')
>>> text = 'شلونك ؟ شخبارك ؟'
>>> pos(text)
[{'entity': 'pron_interrog', 'score': 0.82657206, 'index': 1, 'word': 'شلون', 'start': 0, 'end': 4}, {'entity': 'prep', 'score': 0.9771731, 'index': 2, 'word': '##ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.9999568, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.9977217, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.99993783, 'index': 5, 'word': '##خبار', 'start': 9, 'end': 13}, {'entity': 'prep', 'score': 0.5309442, 'index': 6, 'word': '##ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.9999575, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}]

📚 Documentation

Model description

CAMeLBERT - Mix POS - GLF Model is a Gulf Arabic POS tagging model that was built by fine - tuning the [CAMeLBERT - Mix](https://huggingface.co/CAMeL - Lab/bert - base - arabic - camelbert - mix/) model. For the fine - tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated - gumar - corpus/) 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." Our fine - tuning code can be found [here](https://github.com/CAMeL - Lab/CAMeLBERT).

📄 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.",
}

⚠️ Important Note

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