🚀 CAMeLBERT-Mix DID NADI模型
CAMeLBERT-Mix DID NADI模型 是一个方言识别(DID)模型,它通过对 CAMeLBERT-Mix 模型进行微调而构建。该模型能够有效识别阿拉伯语不同地区的方言,在相关自然语言处理任务中具有重要价值。
✨ 主要特性
📦 安装指南
要下载本模型,你需要 transformers>=3.5.0
。若未满足此条件,你可以手动下载模型。
💻 使用示例
基础用法
你可以将CAMeLBERT-Mix DID NADI模型作为transformers管道的一部分使用。以下是使用transformers管道调用该模型的示例代码:
>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'Egypt', 'score': 0.920274019241333},
{'label': 'Saudi_Arabia', 'score': 0.26750022172927856}]
注意事项
⚠️ 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。
📚 详细文档
该模型很快也将在 CAMeL Tools 中可用。
📄 许可证
本项目采用Apache-2.0许可证。
📚 引用
如果你使用了本模型,请引用以下论文:
@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.",
}