Instructions to use lukecarlate/Araci_Num_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/Araci_Num_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/Araci_Num_2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/Araci_Num_2") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/Araci_Num_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63ddc5d65d2b80c51baed64067926fb08ab79417e72f501d716bd8834415c5ad
- Size of remote file:
- 3 kB
- SHA256:
- 891afd54fc7a0f80442bc6d7088512e1ec3600c036c73e12585538d6c6d11118
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