Instructions to use Rustem/roberta-base-trained-43 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rustem/roberta-base-trained-43 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rustem/roberta-base-trained-43")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Rustem/roberta-base-trained-43") model = AutoModelForMaskedLM.from_pretrained("Rustem/roberta-base-trained-43", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download rng_state.pth from Rustem/roberta-base-trained-43: direct link, hf CLI and curl.
- Browser
- Download file 14.5 kB
-
https://huggingface.co/Rustem/roberta-base-trained-43/resolve/main/rng_state.pth
- Command line
-
hf download hf://Rustem/roberta-base-trained-43/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/Rustem/roberta-base-trained-43/resolve/main/rng_state.pth
14.5 kB
- Xet hash:
- 58a87e1f8bc943cbffc700bf6105b5596187b422021eacc23ff96f9af1f80d3c
- Size of remote file:
- 14.5 kB
- SHA256:
- b51ae3f1509da0e798aa958544d69c93a92b7dfba73b7de058489b9bbe5f8b34
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.