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 training_args.bin from Rustem/roberta-base-trained: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/Rustem/roberta-base-trained/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Rustem/roberta-base-trained@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Rustem/roberta-base-trained/resolve/refs%2Fpr%2F1/training_args.bin
2.99 kB
- Xet hash:
- 4c76e36d44860b0c86056a8087be964b2558de691d9f3df94b0c267b232c30ea
- Size of remote file:
- 2.99 kB
- SHA256:
- 0252311ecef4377f09e761e2326083aa2ffdd1508da0aa14f6443295570fa911
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