Bert Base Arabic Camelbert Mix Did Nadi

🚀 CAMeLBERT-Mix DID NADI模型

CAMeLBERT-Mix DID NADI模型 是一個方言識別（DID）模型，它通過對 CAMeLBERT-Mix 模型進行微調而構建。該模型能夠有效識別阿拉伯語不同地區的方言，在相關自然語言處理任務中具有重要價值。

✨ 主要特性

• 基於微調的 CAMeLBERT-Mix 模型構建，具有良好的語言理解能力。
• 使用 NADI Coountry-level 數據集進行微調，涵蓋21種標籤，識別範圍廣泛。
• 微調過程和超參數可在論文 "The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models" 中找到。

📦 安裝指南

要下載本模型，你需要 transformers>=3.5.0。若未滿足此條件，你可以手動下載模型。

💻 使用示例

基礎用法

你可以將CAMeLBERT-Mix DID NADI模型作為transformers管道的一部分使用。以下是使用transformers管道調用該模型的示例代碼：

>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'Egypt', 'score': 0.920274019241333},
 {'label': 'Saudi_Arabia', 'score': 0.26750022172927856}]

注意事項

⚠️ 重要提示

要下載我們的模型，你需要 transformers>=3.5.0，否則你可以手動下載模型。

📚 詳細文檔

該模型很快也將在 CAMeL Tools 中可用。

📄 許可證

本項目採用Apache-2.0許可證。

📚 引用

如果你使用了本模型，請引用以下論文：

@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.",
}