Instructions to use indigo-ai/BERTino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indigo-ai/BERTino with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="indigo-ai/BERTino")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("indigo-ai/BERTino") model = AutoModelForMaskedLM.from_pretrained("indigo-ai/BERTino", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83e711ffc1df0e3033be146e7e8956dc3974ea8d83d09e463ea04ed8bd6c3634
- Size of remote file:
- 273 MB
- SHA256:
- e47bc22ec09f1d2b965f6ef409f06b059d0c483ec36e5bf4a3f259b59199cc05
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