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 pytorch_model.bin from Rustem/distilroberta-base-trained: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/Rustem/distilroberta-base-trained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Rustem/distilroberta-base-trained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rustem/distilroberta-base-trained/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- 1e43b8b79351fe4496c20628fe5a9674b6053eab23578cb90484d2c3389c2e61
- Size of remote file:
- 329 MB
- SHA256:
- 426c931b59ed25bbf0bddfd5d562d08a582738f1c30efb6945daf59163dbae37
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