Bert Base Arabic Camelbert Ca Sentiment

🚀 CAMeLBERT-CA 情感分析模型

CAMeLBERT-CA 情感分析模型是一个情感分析（SA）模型，它通过对 CAMeLBERT 古典阿拉伯语（CA） 模型进行微调而构建。在微调过程中，我们使用了 ASTD、ArSAS 和 SemEval 数据集。我们的微调过程和使用的超参数可以在我们的论文 "阿拉伯语预训练语言模型中变体、规模和任务类型的相互作用" 中找到。我们的微调代码可以在 这里 找到。

🚀 快速开始

你可以直接将 CAMeLBERT - CA 情感分析模型作为我们 CAMeL 工具 情感分析组件的一部分使用（推荐），也可以作为 transformers 管道的一部分使用。

✨ 主要特性

• 基于微调的 CAMeLBERT 古典阿拉伯语模型，适用于阿拉伯语情感分析任务。
• 使用多个公开数据集进行微调，保证了模型的有效性和泛化能力。

📦 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

使用 CAMeL 工具 情感分析组件：

>>> from camel_tools.sentiment import SentimentAnalyzer
>>> sa = SentimentAnalyzer("CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment")
>>> sentences = ['أنا بخير', 'أنا لست بخير']
>>> sa.predict(sentences)
>>> ['positive', 'negative']

高级用法

直接使用 transformers 管道：

>>> from transformers import pipeline
>>> sa = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment')
>>> sentences = ['أنا بخير', 'أنا لست بخير']
>>> sa(sentences)
[{'label': 'positive', 'score': 0.9616648554801941},
 {'label': 'negative', 'score': 0.9779177904129028}]

📚 详细文档

本模型的详细微调过程和超参数设置可参考我们的论文："阿拉伯语预训练语言模型中变体、规模和任务类型的相互作用"。

📄 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.",
}