Fill-Mask
Transformers
PyTorch
TensorFlow
Safetensors
Arabic
bert
Arabic BERT
MSA
Twitter
Masked Langauge Model
Instructions to use UBC-NLP/ARBERTv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/ARBERTv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UBC-NLP/ARBERTv2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/ARBERTv2") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/ARBERTv2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db0a0c1475995df5047de3c96dee2ce8ac2200fa27ec01671208883d86da1f5c
- Size of remote file:
- 654 MB
- SHA256:
- bcbdd433a98c106ae2bcf182dc980ce8e3a1bb12633e9d286a02ec34e93d8d9c
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