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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec71cc705519f771d80b73b5d800699a6323ef2c5b58b6e9516a9daabd0eae3c
- Size of remote file:
- 654 MB
- SHA256:
- 149b38cab2246f2b6e1284dd8d7edf87cc39224463d943e674d9a6afad808bfb
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