Instructions to use stevems1/distilroberta-base-SmithsModel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevems1/distilroberta-base-SmithsModel2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="stevems1/distilroberta-base-SmithsModel2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("stevems1/distilroberta-base-SmithsModel2") model = AutoModelForMaskedLM.from_pretrained("stevems1/distilroberta-base-SmithsModel2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from stevems1/distilroberta-base-SmithsModel2: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/stevems1/distilroberta-base-SmithsModel2/resolve/main/training_args.bin
- Command line
-
hf download hf://stevems1/distilroberta-base-SmithsModel2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/stevems1/distilroberta-base-SmithsModel2/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- a0aabd806b20246eda1365c0a808e810b3e1e25d687d0726a177a1bb021c67b8
- Size of remote file:
- 3.06 kB
- SHA256:
- 463d061064ce0439a5bed0a21142266a73274b17c5aeb021270b3683f5f34a43
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