Bert Base Arabic Camelbert Da Ner

🚀 CAMeLBERT-DA命名實體識別模型

CAMeLBERT-DA命名實體識別模型 是一個命名實體識別（NER）模型，它通過微調 CAMeLBERT方言阿拉伯語（DA） 模型構建而成。在微調過程中，我們使用了 ANERcorp 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 "阿拉伯語預訓練語言模型中變體、規模和任務類型的相互作用" 中找到。我們的微調代碼可以在 這裡 找到。

✨ 主要特性

• 基於微調的 CAMeLBERT方言阿拉伯語（DA） 模型，適用於阿拉伯語的命名實體識別任務。
• 使用 ANERcorp 數據集進行微調，保證了模型在阿拉伯語相關數據上的性能。

🚀 快速開始

你可以直接將CAMeLBERT-DA命名實體識別模型作為我們 CAMeL Tools 命名實體識別組件的一部分使用（推薦），也可以將其作為transformers管道的一部分使用。

💻 使用示例

基礎用法

使用 CAMeL Tools 命名實體識別組件：

>>> from camel_tools.ner import NERecognizer
>>> from camel_tools.tokenizers.word import simple_word_tokenize
>>> ner = NERecognizer('CAMeL-Lab/bert-base-arabic-camelbert-da-ner')
>>> sentence = simple_word_tokenize('إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع')
>>> ner.predict_sentence(sentence)
>>> ['O', 'B-LOC', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'I-LOC', 'O']

高級用法

直接使用transformers管道：

>>> from transformers import pipeline
>>> ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-da-ner')
>>> ner("إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع")
[{'word': 'أبوظبي',
  'score': 0.9895730018615723,
  'entity': 'B-LOC',
  'index': 2,
  'start': 6,
  'end': 12},
 {'word': 'الإمارات',
  'score': 0.8156259655952454,
  'entity': 'B-LOC',
  'index': 8,
  'start': 33,
  'end': 41},
 {'word': 'العربية',
  'score': 0.890906810760498,
  'entity': 'I-LOC',
  'index': 9,
  'start': 42,
  'end': 49},
 {'word': 'المتحدة',
  'score': 0.8169114589691162,
  'entity': 'I-LOC',
  'index': 10,
  'start': 50,
  'end': 57}]

⚠️ 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

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 da 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.",
}

📄 許可證

本項目採用Apache 2.0許可證。