bert-base-arabic-camelbert-ca
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bert-base-arabic-camelbert-da
CAMeL-Lab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗**CAMeLBERT-DA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Dialectal Arabic (DA) model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models." * Our fine-tuning code can be found here. You can use the CAMeLBERT-DA SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
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View details ↗bert-base-arabic-camelbert-mix
CAMeL-Lab**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes **CAMeLBERT-Mix** (`bert-base-arabic-camelbert-mix`), a model pre-trained on a mixture of these variants: MSA, DA, and CA. You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-base-arabic-camelbert-mix-ner
CAMeL-Lab**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT-Mix NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
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View details ↗**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* Our fine-tuning code can be found here. You can use the CAMeLBERT Mix SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
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View details ↗bert-base-arabic-camelbert-msa
CAMeL-Lab**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes **CAMeLBERT-MSA** (`bert-base-arabic-camelbert-msa`), a model pre-trained on the entire MSA dataset. You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here.
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View details ↗bert-base-arabic-camelbert-msa-ner
CAMeL-Lab**CAMeLBERT MSA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT MSA NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools NER component:
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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