Instructions to use MistahCase/distilroberta-base-testingSB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MistahCase/distilroberta-base-testingSB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MistahCase/distilroberta-base-testingSB")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MistahCase/distilroberta-base-testingSB") model = AutoModelForMaskedLM.from_pretrained("MistahCase/distilroberta-base-testingSB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb48c89bd7812b0c005317686156920b001dcdc43c4cfcdd6970d8a6c1736545
- Size of remote file:
- 2.86 kB
- SHA256:
- 0f99ba3612802c8a618e5e1226701ea6fae6b5363773734158c3bb6e65b82128
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