metadata
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.
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 name.
Checkpoints are available in PyTorch formats.
Pretraining Corpus
`AraBertMo_base_V4' model was pre-trained on ~3 million words:
- 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 | 40032 | 4 | 64 | 2500 | 5h 10m 20s | 7.6544 |
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:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V4")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V4")
This model was built for master's degree research in an organization:
- University of kufa.
- Faculty of Computer Science and Mathematics.
- Department of Computer Science