🚀 CAMeLBERT-Mix POS-MSA模型
CAMeLBERT-Mix POS-MSA模型 是一个现代标准阿拉伯语(MSA)词性标注模型,它是通过对 CAMeLBERT-Mix 模型进行微调而构建的。该模型能够准确地对现代标准阿拉伯语进行词性标注,为阿拉伯语的自然语言处理任务提供了有力支持。
🚀 快速开始
你可以将CAMeLBERT-Mix POS-MSA模型作为transformers管道的一部分来使用。该模型很快也将在 CAMeL Tools 中可用。
✨ 主要特性
- 基于微调:通过对CAMeLBERT-Mix模型进行微调构建,充分利用了预训练模型的优势。
- 数据集支撑:使用 PATB 数据集进行微调,保证了模型的准确性和可靠性。
- 多途径使用:既可以通过transformers管道使用,也将在CAMeL Tools中提供使用方式。
📦 安装指南
⚠️ 重要提示
要下载我们的模型,你需要 transformers>=3.5.0
。否则,你可以手动下载模型。
💻 使用示例
基础用法
>>> 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}]
📚 详细文档
我们的微调过程和所使用的超参数可以在我们的论文 "The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models" 中找到。我们的微调代码可以在 这里 找到。
📄 许可证
本项目采用Apache-2.0许可证。
📚 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.",
}