Instructions to use aubmindlab/bert-large-arabertv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aubmindlab/bert-large-arabertv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aubmindlab/bert-large-arabertv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aubmindlab/bert-large-arabertv2") model = AutoModelForMaskedLM.from_pretrained("aubmindlab/bert-large-arabertv2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from aubmindlab/bert-large-arabertv2: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/aubmindlab/bert-large-arabertv2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://aubmindlab/bert-large-arabertv2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/aubmindlab/bert-large-arabertv2/resolve/main/flax_model.msgpack
1.48 GB
- Xet hash:
- 46d2f7e95414d49ffa598f261e59a333d9dee707f04be19b5a6d7c2dda122aa6
- Size of remote file:
- 1.48 GB
- SHA256:
- 185cbd41d82595749bbf6420f266a857c6f67b240474716302f9a51168c430f2
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