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:
- e8a01b4e3964a119dbc5d3e95143f7eb37567ae14a77db4ef5a09a756540a32a
- Size of remote file:
- 438 MB
- SHA256:
- aa9cac1a322ec2dfe726f788c6c787c1b4e6c259f72b5905f21c3568b7750ad6
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