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:
- aeb63b08bcf51cbafab1a6803d89fb5ebddd464a3cfdc814780492c568d4ffa2
- Size of remote file:
- 3.45 kB
- SHA256:
- db83e166f222ebb368a4b24e29390ba5cb551d6bdedbaa64214c207af0f58165
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