| 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** | |