Instructions to use Rustem/roberta-base-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rustem/roberta-base-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rustem/roberta-base-trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Rustem/roberta-base-trained") model = AutoModelForMaskedLM.from_pretrained("Rustem/roberta-base-trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Rustem/roberta-base-trained: direct link, hf CLI and curl.
- Browser
- Download file 998 MB
-
https://huggingface.co/Rustem/roberta-base-trained/resolve/main/optimizer.pt
- Command line
-
hf download hf://Rustem/roberta-base-trained/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Rustem/roberta-base-trained/resolve/main/optimizer.pt
998 MB
- Xet hash:
- 977a7f8b8b59c93d6eea5da89f029a37e520bdf1851fe915b84f8b76dec07ffe
- Size of remote file:
- 998 MB
- SHA256:
- ef8155c5dfe2414b151a8233b8f70da9af63d17de657bb36d48fe7fa54d99836
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