Bert Base Arabic Camelbert Ca Pos Egy

🚀 CAMeLBERT - CA詞性標註埃及語模型

CAMeLBERT - CA詞性標註埃及語模型 是一個埃及阿拉伯語詞性標註模型，它通過對 [CAMeLBERT - CA](https://huggingface.co/CAMeL - Lab/bert - base - arabic - camelbert - ca/) 模型進行微調而構建。該模型可用於埃及阿拉伯語的詞性標註任務，為相關自然語言處理工作提供支持。

🚀 快速開始

你可以將CAMeLBERT - CA詞性標註埃及語模型作為transformers管道的一部分來使用。該模型很快也將在 [CAMeL Tools](https://github.com/CAMeL - Lab/camel_tools) 中可用。

✨ 主要特性

• 基於預訓練的CAMeLBERT - CA模型進行微調，適用於埃及阿拉伯語的詞性標註。
• 微調過程使用了ARZTB數據集，相關的微調程序和超參數可在論文中找到。

📦 安裝指南

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。

💻 使用示例

基礎用法

>>> from transformers import pipeline
>>> pos = pipeline('token - classification', model='CAMeL - Lab/bert - base - arabic - camelbert - ca - pos - egy')
>>> text = 'عامل ايه ؟'
>>> pos(text)
[{'entity': 'adj', 'score': 0.9990943, 'index': 1, 'word': 'عامل', 'start': 0, 'end': 4}, {'entity': 'pron_interrog', 'score': 0.99863535, 'index': 2, 'word': 'ايه', 'start': 5, 'end': 8}, {'entity': 'punc', 'score': 0.99990875, 'index': 3, 'word': '؟', 'start': 9, 'end': 10}]

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

模型描述

CAMeLBERT - CA詞性標註埃及語模型 是一個埃及阿拉伯語詞性標註模型，它通過對 [CAMeLBERT - CA](https://huggingface.co/CAMeL - Lab/bert - base - arabic - camelbert - ca/) 模型進行微調而構建。在微調過程中，我們使用了ARZTB數據集。我們的微調程序和所使用的超參數可以在我們的論文 "The Interplay of Variant, Size, and Task Type in Arabic Pre - trained Language Models." 中找到。我們的微調代碼可以在 [這裡](https://github.com/CAMeL - Lab/CAMeLBERT) 找到。

預期用途

你可以將CAMeLBERT - CA詞性標註埃及語模型作為transformers管道的一部分來使用。該模型很快也將在 [CAMeL Tools](https://github.com/CAMeL - Lab/camel_tools) 中可用。

📄 許可證

本項目採用Apache - 2.0許可證。

📚 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.",
}