Instructions to use smallbenchnlp/roberta-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smallbenchnlp/roberta-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="smallbenchnlp/roberta-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("smallbenchnlp/roberta-small") model = AutoModelForMaskedLM.from_pretrained("smallbenchnlp/roberta-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f52fadd1b829e6beec5d22982e123133a61e4cc06ebff608aa77d1991ad91087
- Size of remote file:
- 90 MB
- SHA256:
- c9b3f893fa5c2d4d2e1c4104c9d1f6c7fa1c9a01b6242adb256f022cffdcbd12
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