Instructions to use SorrySalmon/distilbert-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SorrySalmon/distilbert-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SorrySalmon/distilbert-trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SorrySalmon/distilbert-trained") model = AutoModelForMaskedLM.from_pretrained("SorrySalmon/distilbert-trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SorrySalmon/distilbert-trained: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/SorrySalmon/distilbert-trained/resolve/main/training_args.bin
- Command line
-
hf download hf://SorrySalmon/distilbert-trained/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SorrySalmon/distilbert-trained/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- b1229cf8ce3b5316b7b0f670c6666e9f5749eebd7e97e381f29190f3a1c59a65
- Size of remote file:
- 5.3 kB
- SHA256:
- 5eb09b9a409c6c96cfd092eac187097b9dbc8b70343f201fac32f7c03a228e1a
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