Ara DialectBERT

🚀 Ara-dialect-BERT

We utilized a pre - trained model and further trained it on the HARD - Arabic - Dataset. The weights were initialized using the "bert - base - camelbert - msa - eighth" model from CAMeL - Lab.

🚀 Quick Start

We used a pretrained model to further train it on HARD-Arabic-Dataset, the weights were initialized using CAMeL-Lab "bert-base-camelbert-msa-eighth" model

💻 Usage Examples

Basic Usage

The model weights can be loaded using transformers library by HuggingFace.

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("MutazYoune/Ara_DialectBERT")
model = AutoModel.from_pretrained("MutazYoune/Ara_DialectBERT")

Advanced Usage

Example using pipeline:

from transformers import pipeline
fill_mask = pipeline(
    "fill-mask",
    model="MutazYoune/Ara_DialectBERT",
    tokenizer="MutazYoune/Ara_DialectBERT"
)
fill_mask("الفندق جميل و لكن [MASK] بعيد")

{'sequence': 'الفندق جميل و لكن الموقع بعيد', 'score': 0.28233852982521057, 'token': 3221, 'token_str': 'الموقع'}
{'sequence': 'الفندق جميل و لكن موقعه بعيد', 'score': 0.24436227977275848, 'token': 19218, 'token_str': 'موقعه'}
{'sequence': 'الفندق جميل و لكن المكان بعيد', 'score': 0.15372352302074432, 'token': 5401, 'token_str': 'المكان'}
{'sequence': 'الفندق جميل و لكن الفندق بعيد', 'score': 0.029026474803686142, 'token': 11133, 'token_str': 'الفندق'}
{'sequence': 'الفندق جميل و لكن مكانه بعيد', 'score': 0.024554792791604996, 'token': 10701, 'token_str': 'مكانه'}