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--- |
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language: ar |
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datasets: |
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- oscar |
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- wikipedia |
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--- |
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# Arabic BERT Model |
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Pretrained BERT base language model for Arabic |
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_If you use this model in your work, please cite this paper:_ |
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``` |
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@inproceedings{safaya-etal-2020-kuisail, |
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title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Speech Identification in Social Media", |
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author = "Safaya, Ali and |
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Abdullatif, Moutasem and |
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Yuret, Deniz", |
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booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation", |
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month = dec, |
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year = "2020", |
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address = "Barcelona (online)", |
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publisher = "International Committee for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.semeval-1.271", |
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pages = "2054--2059", |
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} |
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``` |
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## Pretraining Corpus |
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`arabic-bert-base` model was pretrained on ~8.2 Billion words: |
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- Arabic version of [OSCAR](https://traces1.inria.fr/oscar/) - filtered from [Common Crawl](http://commoncrawl.org/) |
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- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html) |
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and other Arabic resources which sum up to ~95GB of text. |
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__Notes on training data:__ |
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- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER. |
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- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters does not have upper or lower case, there is no cased and uncased version of the model. |
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- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too. |
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## Pretraining details |
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- This model was trained using Google BERT's github [repository](https://github.com/google-research/bert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc). |
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- Our pretraining procedure follows training settings of bert with some changes: trained for 3M training steps with batchsize of 128, instead of 1M with batchsize of 256. |
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## Load Pretrained Model |
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You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("asafaya/bert-base-arabic") |
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model = AutoModelForMaskedLM.from_pretrained("asafaya/bert-base-arabic") |
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``` |
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## Results |
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For further details on the models performance or any other queries, please refer to [Arabic-BERT](https://github.com/alisafaya/Arabic-BERT) |
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## Acknowledgement |
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Thanks to Google for providing free TPU for the training process and for Huggingface for hosting this model on their servers 😊 |
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