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:
- 6c39771c75838790d063d8cbc8e9e2a2adb800d63ee113cae934c22f213126e4
- Size of remote file:
- 3.07 kB
- SHA256:
- a38077144a620b69653431f896ceb5dda9851528001a2aa9ef1a7fc29f33a924
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