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:
- bd2907045480eeff395e55b59106cc7abbb5417f736e86b0a0688c1ef0600a33
- Size of remote file:
- 654 MB
- SHA256:
- 7b820070d6ea59308a634c247d9e2a3ab88daad0a06ec787bd1119534e3fc5be
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