Instructions to use lukecarlate/Araci_Num_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/Araci_Num_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/Araci_Num_4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/Araci_Num_4") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/Araci_Num_4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b643d062b832255b6d028841ea2ad7cd74ceda7f916bb7268144922e3c758c4b
- Size of remote file:
- 438 MB
- SHA256:
- 98862d3abe3e4ef4f90798fc13af60c1623b95d6e799db9e9b11170a47bfc1d7
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