Instructions to use xuantsh/distilroberta-base-Mark_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xuantsh/distilroberta-base-Mark_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xuantsh/distilroberta-base-Mark_example")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("xuantsh/distilroberta-base-Mark_example") model = AutoModelForMaskedLM.from_pretrained("xuantsh/distilroberta-base-Mark_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from xuantsh/distilroberta-base-Mark_example: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/xuantsh/distilroberta-base-Mark_example/resolve/main/training_args.bin
- Command line
-
hf download hf://xuantsh/distilroberta-base-Mark_example/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/xuantsh/distilroberta-base-Mark_example/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 565e7b829ebd5d29c4d2a76baa000ebd2f11be1ed7821f1a3ac7c4d179956a31
- Size of remote file:
- 3.31 kB
- SHA256:
- 357fb14e7088ee68c717c3c658daac5c1b18ea490a128008fc902a7d89850814
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