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--- |
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language: ar |
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tags: Fill-Mask |
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datasets: OSCAR |
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widget: |
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- text: " السلام عليكم ورحمة[MASK] وبركاتة" |
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- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون" |
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- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا " |
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--- |
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# Arabic BERT Model |
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**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert). |
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AraBERTMo_base uses the same BERT-Base config. |
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AraBERTMo_base now comes in 10 new variants |
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All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name. |
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Checkpoints are available in PyTorch formats. |
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## Pretraining Corpus |
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`AraBertMo_base_V2' model was pre-trained on ~3 million words: |
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- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar". |
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## Training results |
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this model achieves the following results: |
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| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss| |
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|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:| |
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| Fill-Mask| 20020| 2 | 64 | 626 | 19m 2s | 8.437 | |
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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("Ebtihal/AraBertMo_base_V2") |
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model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V2") |
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``` |
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## This model was built for master's degree research in an organization: |
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- [University of kufa](https://uokufa.edu.iq/). |
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- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/). |
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- **Department of Computer Science** |
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