Instructions to use lukecarlate/Araci_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/Araci_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/Araci_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/Araci_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/Araci_Num") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 046b3d40bea74a89efcccae7c5d2db2413a79e22a1272492285b32e3e3d2d974
- Size of remote file:
- 438 MB
- SHA256:
- 8e03bd1c8a2cdffe8afd7e7dce265f426e4bfae5d5140e7735880627795ede8f
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