Create README.md

README.md

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+Arabic Model AraBertMo_base_V7
+
+---
+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**
+