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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/ARBERT") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/ARBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from UBC-NLP/ARBERT: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/UBC-NLP/ARBERT/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://UBC-NLP/ARBERT/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/UBC-NLP/ARBERT/resolve/main/flax_model.msgpack
652 MB
- Xet hash:
- 438bd9d8bbac975c28240b3f205302b9165aef6a261da252eba35a429cbde4f1
- Size of remote file:
- 652 MB
- SHA256:
- 68bf06c87fea9c93ee9456cce1b7c2e6d4a5c8c7291eafd4ac137def4ca7ee36
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