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CAMeLBERT-Mix NER Model

Model description

CAMeLBERT-Mix NER Model is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the 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. " Our fine-tuning code can be found here.

Intended uses

You can use the CAMeLBERT-Mix NER model directly as part of our CAMeL Tools NER component (recommended) or as part of the transformers pipeline.

How to use

To use the model with the CAMeL Tools NER component:

>>> from camel_tools.ner import NERecognizer
>>> from camel_tools.tokenizers.word import simple_word_tokenize
>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-mix-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:

>>> from transformers import pipeline
>>> ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-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',
  'sco

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