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**CAMeLBERT MSA NER Model** is Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) 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).
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## Intended uses
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You can use the CAMeLBERT MSA NER Model directly as part of the transformers pipeline.
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#### How to use
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You can use this model directly with a pipeline to do NER:
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```
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*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models
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## Citation
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```bibtex
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@inproceedings{inoue-etal-2021-interplay,
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**CAMeLBERT MSA NER Model** is Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) 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).
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## Intended uses
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You can use the CAMeLBERT MSA NER Model directly as part of the transformers pipeline or as part of our [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) NER component.
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#### How to use
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You can use this model directly with a pipeline to do NER:
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'start': 50,
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'end': 57}]
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```
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Here is how to use this model with our CAMeLTools toolkit:
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```python
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>>> from camel_tools.ner import NERecognizer
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>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-msa-ner')
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>>> sentence = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'.split()
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>>> ner.predict_sentence(sentence)
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>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']
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```
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*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models
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## Citation
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```bibtex
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@inproceedings{inoue-etal-2021-interplay,
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