Instructions to use HiTZ/BERnaT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiTZ/BERnaT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HiTZ/BERnaT-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HiTZ/BERnaT-large") model = AutoModelForMaskedLM.from_pretrained("HiTZ/BERnaT-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89f049c184d18e3b546e431605d64131e605811aa787378ddc7e6c4875d79ac2
- Size of remote file:
- 1.42 GB
- SHA256:
- 3affcb4c4890766bf5c04d074c11852ba2e6c390536e1680ff38a80caf6ce072
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