Instructions to use lukecarlate/Araci_Num_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/Araci_Num_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/Araci_Num_5")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/Araci_Num_5") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/Araci_Num_5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07dd80acfb722a2be3d604d936c089e38c34c82299bf88705d6859aeefa1cceb
- Size of remote file:
- 438 MB
- SHA256:
- cded1f7a89b93511967c4d779fb547de223b155fc76a044029429dfaa9046b5e
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