Multi-dialect-Arabic-BERT

This is a repository of the Multi-dialect Arabic BERT model.

By Mawdoo3-AI.

About our Multi-dialect-Arabic-BERT model

Instead of training the Multi-dialect Arabic BERT model from scratch, we initialized the weights of the model using Arabic-BERT and trained it on 10M arabic tweets from the unlabled data of The Nuanced Arabic Dialect Identification (NADI) shared task.

To cite this work

@misc{talafha2020multidialect,
    title={Multi-Dialect Arabic BERT for Country-Level Dialect Identification},
    author={Bashar Talafha and Mohammad Ali and Muhy Eddin Za'ter and Haitham Seelawi and Ibraheem Tuffaha and Mostafa Samir and Wael Farhan and Hussein T. Al-Natsheh},
    year={2020},
    eprint={2007.05612},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Usage

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

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic")
model = AutoModel.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic")

Example using pipeline:

from transformers import pipeline

fill_mask = pipeline(
    "fill-mask",
    model="bashar-talafha/multi-dialect-bert-base-arabic ",
    tokenizer="bashar-talafha/multi-dialect-bert-base-arabic "
)

fill_mask(" سافر الرحالة من مطار [MASK] ")

[{'sequence': '[CLS] سافر الرحالة من مطار الكويت [SEP]', 'score': 0.08296813815832138, 'token': 3226},
 {'sequence': '[CLS] سافر الرحالة من مطار دبي [SEP]', 'score': 0.05123933032155037, 'token': 4747},
 {'sequence': '[CLS] سافر الرحالة من مطار مسقط [SEP]', 'score': 0.046838656067848206, 'token': 13205},
 {'sequence': '[CLS] سافر الرحالة من مطار القاهرة [SEP]', 'score': 0.03234650194644928, 'token': 4003},
 {'sequence': '[CLS] سافر الرحالة من مطار الرياض [SEP]', 'score': 0.02606341242790222, 'token': 2200}]

Model Parameters

Parameter	Value
architecture	BertForMaskedLM
hidden_size	768
max_position_embeddings	512
num_attention_heads	12
num_hidden_layers	12
vocab_size	32000
hidden_size	768
Total number of parameters	110M