Instructions to use lukecarlate/Araci_CM_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/Araci_CM_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/Araci_CM_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/Araci_CM_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/Araci_CM_Num") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa79eb04822f74ab76811b081417d5122c4744bf68060f778a00df34554f7cdd
- Size of remote file:
- 3 kB
- SHA256:
- 535c5b438733993858d19fa99ab70bdff8db37de39a88eef3e42ee8acb1e0d0e
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