---
language: ar
tags: Fill-Mask 
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون "
---

# 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_V1' 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| 10010|  1  | 64 | 157  | 2m 2s | 9.0183  | 


## 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_V1")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V1")
```

 ## 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**