语言:

• 阿拉伯语 许可证: Apache-2.0 小部件:
• 文本: "عامل ايه ؟"

CAMeLBERT-Mix DID Madar Corpus26 模型

模型描述

CAMeLBERT-Mix DID Madar Corpus26 模型 是一个方言识别（DID）模型，通过微调 CAMeLBERT-Mix 模型构建而成。在微调过程中，我们使用了包含26个标签的 MADAR Corpus 26 数据集。我们的微调过程和使用的超参数可以在我们的论文 "The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models" 中找到。微调代码可在 这里 获取。

预期用途

您可以将 CAMeLBERT-Mix DID Madar Corpus26 模型作为 transformers 管道的一部分使用。该模型也将在 CAMeL Tools 中提供。

使用方法

要使用该模型与 transformers 管道：

>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar26')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'CAI', 'score': 0.8751305937767029},
 {'label': 'DOH', 'score': 0.9867215156555176}]

注意：下载我们的模型需要 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.",
}