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:
- a4ad86960e0cb232877eba1b76c3160ffa6db7b6c21880c6eeb5f9fc58b46dbc
- Size of remote file:
- 652 MB
- SHA256:
- 5d5e9de00d0ff18ec8ff5d1590473090979728b1c9f1a141a9d8fbcbcd513124
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