Bert Base Arabic Camelbert Ca Ner

🚀 CAMeLBERT-CA NER Model

CAMeLBERT-CA NER Model is a Named Entity Recognition (NER) model that enhances the accuracy of identifying named entities in Classical Arabic text. It leverages a pre - trained model and a well - curated dataset for fine - tuning, making it a powerful tool for Arabic NLP tasks.

🚀 Quick Start

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

✨ Features

• Fine - tuned Model: Built by fine - tuning the CAMeLBERT Classical Arabic (CA) model.
• Quality Dataset: Utilized the ANERcorp dataset for fine - tuning.
• Well - Documented: 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."

📚 Documentation

Model description

CAMeLBERT-CA NER Model is a Named Entity Recognition (NER) model that was built by fine - tuning the CAMeLBERT Classical Arabic (CA) 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-CA NER model directly as part of our CAMeL Tools NER component (recommended) or as part of the transformers pipeline.

How to use

💻 Usage Examples

Basic Usage

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-ca-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']

Advanced Usage

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-ca-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}]

⚠️ Important Note

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

📄 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 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.",
}