Instructions to use UBC-NLP/MARBERTv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/MARBERTv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UBC-NLP/MARBERTv2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/MARBERTv2") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/MARBERTv2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 111bc5f6533e0bcefa6cfdec960c2fcba57be85467325b6cef6dde04194e2405
- Size of remote file:
- 654 MB
- SHA256:
- 5d34aca06d9ec233e8483478faec43cea7c819d1ea4f83fc99d9c8067dcf8bab
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