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--- |
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language: |
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- ar |
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license: apache-2.0 |
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widget: |
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- text: 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع' |
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--- |
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# CAMeLBERT-CA POS-MSA Model |
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## Model description |
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**CAMeLBERT-CA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model. |
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For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf/10.5555/1621804.1621808) dataset. |
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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). |
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## Intended uses |
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You can use the CAMeLBERT-CA POS-MSA model as part of the transformers pipeline. |
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This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon. |
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#### How to use |
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To use the model with a transformers pipeline: |
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```python |
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>>> from transformers import pipeline |
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>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa') |
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>>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع' |
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>>> pos(text) |
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[{'entity': 'noun', 'score': 0.9999758, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.9997559, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.99996257, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.9958452, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.9999635, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.99991685, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99997497, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.9999795, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.99924207, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.99994195, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.9997414, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}] |
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``` |
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*Note*: to download our models, you would need `transformers>=3.5.0`. |
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Otherwise, you could download the models manually. |
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## Citation |
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```bibtex |
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@inproceedings{inoue-etal-2021-interplay, |
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title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", |
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author = "Inoue, Go and |
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Alhafni, Bashar and |
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Baimukan, Nurpeiis and |
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Bouamor, Houda and |
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Habash, Nizar", |
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booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", |
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month = apr, |
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year = "2021", |
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address = "Kyiv, Ukraine (Online)", |
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publisher = "Association for Computational Linguistics", |
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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.", |
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} |
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``` |
