Instructions to use alakxender/roberta-dhivehi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alakxender/roberta-dhivehi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alakxender/roberta-dhivehi")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("alakxender/roberta-dhivehi") model = AutoModelForMaskedLM.from_pretrained("alakxender/roberta-dhivehi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65a90960c5374dd0d26ee53077e40325a9438f41df7736a69ddd398a75f7fdff
- Size of remote file:
- 5.11 kB
- SHA256:
- 05ed2a0d658ef5d07b14437639f44f8b986b079c8b5b46e51d4c9980f6468089
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