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  - text: "الهدف من الحياة هو [MASK] ."
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  ---
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- # CAMeLBERT-MSA-quarter
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  ## Model description
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- **CAMeLBERT** is a BERT model pre-trained on Arabic texts with different sizes and variants.
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- The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."*
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  We release eight models with different sizes and variants as follows:
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  ||Model|Variant|Size|#Word|
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  ||`bert-base-camelbert-msa-eighth`|MSA|14GB|1.6B|
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  ||`bert-base-camelbert-msa-sixteenth`|MSA|6GB|746M|
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- This model card describes `bert-base-camelbert-msa-quarter`, a model pre-trained on a quarter of the full MSA dataset.
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  ## Intended uses
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  You can use the released model for either masked language modeling or next sentence prediction.
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  'token_str': 'الكمال'}]
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  ```
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  Here is how to use this model to get the features of a given text in PyTorch:
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  ```python
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  from transformers import AutoTokenizer, AutoModel
 
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  - text: "الهدف من الحياة هو [MASK] ."
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  ---
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+ # CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
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  ## Model description
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+ **CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
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+ The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."*
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  We release eight models with different sizes and variants as follows:
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  ||Model|Variant|Size|#Word|
 
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  ||`bert-base-camelbert-msa-eighth`|MSA|14GB|1.6B|
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  ||`bert-base-camelbert-msa-sixteenth`|MSA|6GB|746M|
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+ This model card describes **CAMeLBERT-MSA-quarter** (`bert-base-camelbert-msa-quarter`), a model pre-trained on a quarter of the full MSA dataset.
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  ## Intended uses
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  You can use the released model for either masked language modeling or next sentence prediction.
 
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  'token_str': 'الكمال'}]
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  ```
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+ *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
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+
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  Here is how to use this model to get the features of a given text in PyTorch:
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  ```python
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  from transformers import AutoTokenizer, AutoModel