Bert Base Arabic Camelbert Mix Pos Msa

🚀 CAMeLBERT-Mix POS-MSA模型

CAMeLBERT-Mix POS-MSA模型 是一個現代標準阿拉伯語（MSA）詞性標註模型，它是通過對 CAMeLBERT-Mix 模型進行微調而構建的。該模型能夠準確地對現代標準阿拉伯語進行詞性標註，為阿拉伯語的自然語言處理任務提供了有力支持。

🚀 快速開始

你可以將CAMeLBERT-Mix POS-MSA模型作為transformers管道的一部分來使用。該模型很快也將在 CAMeL Tools 中可用。

✨ 主要特性

• 基於微調：通過對CAMeLBERT-Mix模型進行微調構建，充分利用了預訓練模型的優勢。
• 數據集支撐：使用 PATB 數據集進行微調，保證了模型的準確性和可靠性。
• 多途徑使用：既可以通過transformers管道使用，也將在CAMeL Tools中提供使用方式。

📦 安裝指南

⚠️ 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。

💻 使用示例

基礎用法

>>> from transformers import pipeline
>>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa')
>>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
>>> pos(text)
[{'entity': 'noun', 'score': 0.9999592, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.9997877, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.9998405, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.9697179, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.99967164, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.99980617, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99997973, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.99995637, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.9983974, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.9999469, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.9993273, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}]

📚 詳細文檔

我們的微調過程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 找到。

📄 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.",
}