Bert Base Arabic Camelbert Msa Did Nadi

🚀 CAMeLBERT-MSA DID NADI模型

CAMeLBERT-MSA DID NADI模型 是一個方言識別（DID）模型，通過微調 CAMeLBERT現代標準阿拉伯語（MSA） 模型構建而成。該模型利用 NADI國家級別 數據集進行微調，此數據集包含21個標籤。微調過程和使用的超參數可在論文 "阿拉伯語預訓練語言模型中變體、規模和任務類型的相互作用" 中找到，微調代碼可在 此處 獲取。

✨ 主要特性

• 基於微調的預訓練模型，可用於阿拉伯語方言識別。
• 支持通過transformers管道使用，後續也將集成到 CAMeL Tools 中。

📦 安裝指南

使用此模型需要 transformers>=3.5.0，若版本不滿足，可手動下載模型。

💻 使用示例

基礎用法

>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'Egypt', 'score': 0.9242768287658691}, 
{'label': 'Saudi_Arabia', 'score': 0.3400847613811493}]

📚 詳細文檔

預期用途

可以將CAMeLBERT-MSA DID NADI模型作為transformers管道的一部分使用，該模型很快也將在 CAMeL Tools 中可用。

引用信息

@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.",
}

📄 許可證

本項目採用Apache-2.0許可證。