Bert Base Arabic Camelbert Msa Pos Msa

🚀 CAMeLBERT-MSA詞性標註（MSA）模型

CAMeLBERT-MSA詞性標註（MSA）模型是一個用於現代標準阿拉伯語（MSA）詞性標註的模型。它通過微調CAMeLBERT-MSA模型構建而成。該模型能夠高效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 - MSA詞性標註（MSA）模型作為transformers管道的一部分使用。並且，該模型很快也將在CAMeL Tools中可用。

✨ 主要特性

• 基於微調：通過微調CAMeLBERT - MSA模型構建，利用了預訓練模型的強大語言理解能力。
• 數據支持：在微調過程中使用了PATB數據集，保證了模型訓練的質量。
• 研究支持：微調過程和使用的超參數在論文*"The Interplay of Variant, Size, and Task Type in Arabic Pre - trained Language Models"*中有詳細說明，並且微調代碼可在這裡獲取。

📦 安裝指南

使用該模型需要transformers>=3.5.0。若版本不滿足，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-msa-pos-msa')
>>> text = 'إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع'
>>> pos(text)
[{'entity': 'noun', 'score': 0.9999764, 'index': 1, 'word': 'إمارة', 'start': 0, 'end': 5}, {'entity': 'noun_prop', 'score': 0.99991846, 'index': 2, 'word': 'أبوظبي', 'start': 6, 'end': 12}, {'entity': 'pron', 'score': 0.9998356, 'index': 3, 'word': 'هي', 'start': 13, 'end': 15}, {'entity': 'noun', 'score': 0.99368894, 'index': 4, 'word': 'إحدى', 'start': 16, 'end': 20}, {'entity': 'noun', 'score': 0.9999426, 'index': 5, 'word': 'إما', 'start': 21, 'end': 24}, {'entity': 'noun', 'score': 0.9999339, 'index': 6, 'word': '##رات', 'start': 24, 'end': 27}, {'entity': 'noun', 'score': 0.99996775, 'index': 7, 'word': 'دولة', 'start': 28, 'end': 32}, {'entity': 'noun', 'score': 0.99996895, 'index': 8, 'word': 'الإمارات', 'start': 33, 'end': 41}, {'entity': 'adj', 'score': 0.99990183, 'index': 9, 'word': 'العربية', 'start': 42, 'end': 49}, {'entity': 'adj', 'score': 0.9999347, 'index': 10, 'word': 'المتحدة', 'start': 50, 'end': 57}, {'entity': 'noun_num', 'score': 0.99931145, 'index': 11, 'word': 'السبع', 'start': 58, 'end': 63}]

📚 詳細文檔

模型描述

CAMeLBERT - MSA詞性標註（MSA）模型是一個現代標準阿拉伯語（MSA）詞性標註模型。它通過微調CAMeLBERT - MSA模型構建而成。在微調過程中，使用了PATB數據集。微調過程和使用的超參數可在論文*"The Interplay of Variant, Size, and Task Type in Arabic Pre - trained Language Models"*中找到，微調代碼可在這裡獲取。

預期用途

該模型可作為transformers管道的一部分使用，並且很快將在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.",
}