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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ffc46ad423d8f54145618939aa76703e2723295dd97d7f8cfc346edc66519c7
- Size of remote file:
- 652 MB
- SHA256:
- 874773fe40d1d0449571bf3ae9c9bb1f950121eb28fbffbd285e9e34a254c2fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.