Bert Base Arabic Camelbert Mix Poetry

🚀 CAMeLBERT - Mix詩歌分類模型

CAMeLBERT - Mix詩歌分類模型是一個用於詩歌分類的模型，它通過微調CAMeLBERT Mix模型構建而成。該模型能有效對阿拉伯語詩歌進行分類，為相關研究和應用提供了有力支持。

🚀 快速開始

你可以將CAMeLBERT - Mix詩歌分類模型作為transformers管道的一部分來使用。該模型不久後也將在CAMeL Tools中可用。

✨ 主要特性

• 基於微調的CAMeLBERT Mix模型構建，具有良好的性能基礎。
• 使用APCD數據集進行微調，數據來源可靠。
• 微調過程和超參數在論文 "The Interplay of Variant, Size, and Task Type in Arabic Pre - trained Language Models" 中有詳細說明。

📦 安裝指南

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。

💻 使用示例

基礎用法

>>> from transformers import pipeline
>>> poetry = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry')
>>> # A list of verses where each verse consists of two parts.
>>> verses = [
        ['الخيل والليل والبيداء تعرفني' ,'والسيف والرمح والقرطاس والقلم'],
        ['قم للمعلم وفه التبجيلا' ,'كاد المعلم ان يكون رسولا']
    ]
>>> # A function that concatenates the halves of each verse by using the [SEP] token.
>>> join_verse = lambda half: ' [SEP] '.join(half)
>>> # Apply this to all the verses in the list.
>>> verses = [join_verse(verse) for verse in verses]
>>> poetry(sentences)
[{'label': 'البسيط', 'score': 0.9937475919723511},
 {'label': 'الكامل', 'score': 0.971284031867981}]

📚 詳細文檔

模型描述

CAMeLBERT - Mix詩歌分類模型 是一個詩歌分類模型，它通過微調CAMeLBERT Mix模型構建而成。在微調過程中，我們使用了APCD數據集。我們的微調過程和所使用的超參數可以在我們的論文 "The Interplay of Variant, Size, and Task Type in Arabic Pre - trained Language Models" 中找到。我們的微調代碼可以在這裡找到。

預期用途

你可以將CAMeLBERT - Mix詩歌分類模型作為transformers管道的一部分來使用。該模型不久後也將在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.",
}