Instructions to use efederici/bertino-lsg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efederici/bertino-lsg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="efederici/bertino-lsg", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("efederici/bertino-lsg", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("efederici/bertino-lsg", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 484d3ac739cc582fdf36a3a9368a8f420668d471559a81026c9de9af4c8dde58
- Size of remote file:
- 287 MB
- SHA256:
- c625e0d1988bf68fa1eeea700ad8f6e44fcc3b6d975084b99af3949184730e30
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