Instructions to use Rustem/distilroberta-base-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rustem/distilroberta-base-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rustem/distilroberta-base-trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Rustem/distilroberta-base-trained") model = AutoModelForMaskedLM.from_pretrained("Rustem/distilroberta-base-trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from Rustem/distilroberta-base-trained: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/Rustem/distilroberta-base-trained/resolve/main/scheduler.pt
- Command line
-
hf download hf://Rustem/distilroberta-base-trained/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/Rustem/distilroberta-base-trained/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- 3171f53af4d94dd078c95240c5d6b7309bc5073149dc2ca0490545146f01675e
- Size of remote file:
- 623 Bytes
- SHA256:
- ba36cc162c40fd551a6ecaee3b3c7133736e1736563c4355116639d7a19065d7
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