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 tf1_model.tar.gz from aubmindlab/bert-large-arabertv2: direct link, hf CLI and curl.
- Browser
- Download file 1.38 GB
-
https://huggingface.co/aubmindlab/bert-large-arabertv2/resolve/main/tf1_model.tar.gz
- Command line
-
hf download hf://aubmindlab/bert-large-arabertv2/tf1_model.tar.gz
-
curl -L -o tf1_model.tar.gz https://huggingface.co/aubmindlab/bert-large-arabertv2/resolve/main/tf1_model.tar.gz
1.38 GB
- Xet hash:
- 5c09c8ee8a08e972de99e4d9816fe465db7162211bf3d6de68597048f6da6f62
- Size of remote file:
- 1.38 GB
- SHA256:
- b510bb77d979c0cfeabc8c7904fe955c7943f3cce447287bda0315a5f7f114e3
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