Instructions to use hadidev/robertaurdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadidev/robertaurdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hadidev/robertaurdu")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hadidev/robertaurdu") model = AutoModelForMaskedLM.from_pretrained("hadidev/robertaurdu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c46508a612c2a370a95facb274ab65b4cd2000ed63730f07a67659663a83895
- Size of remote file:
- 357 MB
- SHA256:
- 7047d4506f3599b0a8df685262b633cea2ed5c47d19b42a4975f1dc97c651d6f
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