Bert Base Arabic Camelbert Msa Did Madar Twitter5

🚀 CAMeLBERT-MSA DID MADAR Twitter-5 模型

該模型是一個方言識別（DID）模型，通過微調 CAMeLBERT-MSA 模型構建而成。它使用了特定數據集進行微調，能有效完成方言識別任務，在相關領域有重要應用價值。

🚀 快速開始

你可以將 CAMeLBERT-MSA DID MADAR Twitter-5 模型作為 transformers 管道的一部分使用。並且，該模型很快也會在 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能力。
• 特定數據集：在微調過程中使用了 MADAR Twitter - 5 數據集，該數據集包含 21 個標籤。
• 可復現性：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 此處 獲取。

📦 安裝指南

要下載該模型，你需要 transformers>=3.5.0。若不滿足此條件，也可以手動下載模型。

💻 使用示例

基礎用法

>>> from transformers import pipeline
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5')
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟']
>>> did(sentences)
[{'label': 'Egypt', 'score': 0.5741344094276428},
 {'label': 'Kuwait', 'score': 0.5225679278373718}]

注意事項

⚠️ 重要提示

要下載我們的模型，你需要 transformers>=3.5.0，否則你可以手動下載模型。

📚 詳細文檔

模型描述

CAMeLBERT-MSA DID MADAR Twitter - 5 模型 是一個方言識別（DID）模型，它是通過微調 CAMeLBERT-MSA 模型構建的。在微調時，使用了 MADAR Twitter - 5 數據集，該數據集包含 21 個標籤。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 此處 獲取。

預期用途

你可以將該模型作為 transformers 管道的一部分使用，並且該模型很快也會在 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.",
}