Fill-Mask
Transformers
PyTorch
TensorFlow
JAX
Arabic
bert
Arabic BERT
MSA
Twitter
Masked Langauge Model
Instructions to use UBC-NLP/ARBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/ARBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UBC-NLP/ARBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/ARBERT") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/ARBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d61248f226fdfcb8e3d924f31eece47224b02c628015b9ba02ca1410415c605d
- Size of remote file:
- 1.79 GB
- SHA256:
- 4295adedea124611317353cce2662118d7fb58fa89e3f17343749b0541adfacc
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