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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d6af4db112e987e23c113e3a376ee69e52e0847347b652089b15a064e50ceec
- Size of remote file:
- 3.31 kB
- SHA256:
- 43e25949b7c7161b5430d5e38a76259ad83dd78f81c570c7a080b88558f29873
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