readme: ---
  language: 
  - ar
  license: apache-2.0
  widget:
   - text: "عامل ايه ؟"
  ---
  # CAMeLBERT-Mix DID MADAR Corpus6 Model
  ## Model description
  **CAMeLBERT-Mix DID MADAR Corpus6 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
  For the fine-tuning, we used the [MADAR Corpus 6](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/) dataset, which includes 6 labels.
  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-Mix DID MADAR Corpus6 model as part of the transformers pipeline.
  This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon.
  
  #### How to use
  To use the model with a transformers pipeline:
  ```python
  >>> from transformers import pipeline
  >>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar6')
  >>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
  >>> did(sentences)
  [{'label': 'CAI', 'score': 0.9996405839920044},
   {'label': 'DOH', 'score': 0.9997853636741638}]
  
  ```
  *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models 
  ## 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 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.",
  }
  ```