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:
- 610cf6f91e77bcc2a3400d031d3f2961dce9a111c576f82d0373b447ae47c190
- Size of remote file:
- 3.07 kB
- SHA256:
- 27a8ac53cb16b570656865fa95192e5d4e1aa4396495321bfd0e8e824c071950
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