Bert Base Arabic Camelbert Mix Did Madar Corpus26

🚀 CAMeLBERT-Mix方言识别马达尔语料库26模型

CAMeLBERT-Mix方言识别马达尔语料库26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。该模型旨在利用预训练语言模型的强大能力，针对阿拉伯语不同方言进行准确识别，为阿拉伯语自然语言处理任务提供更精准的支持。

🚀 快速开始

你可以将CAMeLBERT-Mix方言识别马达尔语料库26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

• 微调构建：通过微调 CAMeLBERT-Mix 模型构建而成。
• 多标签支持：使用包含26个标签的 MADAR语料库26 数据集进行微调。
• 可复现性：微调过程和超参数可在论文 "The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models" 中找到，微调代码可在 此处 获取。

📦 安装指南

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
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar26')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'CAI', 'score': 0.8751305937767029},
 {'label': 'DOH', 'score': 0.9867215156555176}]

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

模型描述

CAMeLBERT-Mix方言识别马达尔语料库26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。在微调过程中，我们使用了 MADAR语料库26 数据集，该数据集包含26个标签。我们的微调过程和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 此处 获取。

预期用途

你可以将CAMeLBERT-Mix方言识别马达尔语料库26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。

📄 许可证

本模型采用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.",
}