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	---
	language:
	- ar
	license: apache-2.0
	widget:
	- text: "إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع"
	---
	# CAMeLBERT-DA NER Model
	## Model description
	CAMeLBERT-DA NER Model is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
	For the fine-tuning, we used the [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/) 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-DA NER model directly as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER 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) NER component:
	```python
	>>> from camel_tools.ner import NERecognizer
	>>> from camel_tools.tokenizers.word import simple_word_tokenize
	>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-da-ner')
	>>> sentence = simple_word_tokenize('إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع')
	>>> ner.predict_sentence(sentence)
	>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']
	```
	You can also use the NER model directly with a transformers pipeline:
	```python
	>>> from transformers import pipeline
	>>> ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-da-ner')
	>>> ner("إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع")
	[{'word': 'أبوظبي',
	'score': 0.9895730018615723,
	'entity': 'B-LOC',
	'index': 2,
	'start': 6,
	'end': 12},
	{'word': 'الإمارات',
	'score': 0.8156259655952454,
	'entity': 'B-LOC',
	'index': 8,
	'start': 33,
	'end': 41},
	{'word': 'العربية',
	'score': 0.890906810760498,
	'entity': 'I-LOC',
	'index': 9,
	'start': 42,
	'end': 49},
	{'word': 'المتحدة',
	'score': 0.8169114589691162,
	'entity': 'I-LOC',
	'index': 10,
	'start': 50,
	'end': 57}]
	```
	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 da 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.",
	}
	```