Arabic Model AraBertMo_base_V9 --- language: ar tags: Fill-Mask datasets: OSCAR widget: - text: " السلام عليكم ورحمة[MASK] وبركاتة" - text: " اهلا وسهلا بكم في [MASK] من سيربح المليون" - text: " مرحبا بك عزيزي الزائر [MASK] موقعنا " --- # Arabic BERT Model **AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERTMo_base uses the same BERT-Base config. AraBERTMo_base now comes in 10 new variants All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name. Checkpoints are available in PyTorch formats. ## Pretraining Corpus `AraBertMo_base_V9' model was pre-trained on ~3 million words: - [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar". ## Training results this model achieves the following results: | Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss| |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:| | Fill-Mask| 30024| 9 | 64 | 4230 | 7h 57m 42s | 7.3264 | ## Load Pretrained Model 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: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V9") model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V9") ``` ## This model was built for master's degree research in an organization: - [University of kufa](https://uokufa.edu.iq/). - [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/). - **Department of Computer Science**