CAMeLBERT-MSA DID MADAR Twitter-5 模型

模型描述

CAMeLBERT-MSA DID MADAR Twitter-5 模型是一个方言识别（DID）模型，通过微调 CAMeLBERT-MSA 模型进行构建。我们使用了 MADAR Twitter-5 数据集进行微调，该数据集包含21个标签。有关微调步骤和超参数的详细信息请参阅我们的论文" The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models "。我们的微调代码可以在 here 中找到。

预期用途

您可以将CAMeLBERT-MSA DID MADAR Twitter-5模型作为transformers pipeline的一部分使用。该模型很快也将在 CAMeL Tools 中提供。

如何使用

要在transformers pipeline中使用该模型：

>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'Egypt', 'score': 0.5741344094276428},
 {'label': 'Kuwait', 'score': 0.5225679278373718}]

注意：要下载我们的模型，您需要transformers>=3.5.0。否则，您可以手动下载模型。

引用

@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.",
}