Instructions to use lukecarlate/Araci_Num_33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/Araci_Num_33 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/Araci_Num_33")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/Araci_Num_33") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/Araci_Num_33") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f95733560ef975e9f2250ea9305943fb36f6b72318414325b43f88996d79f26
- Size of remote file:
- 3 kB
- SHA256:
- eeb854b074e09dd5864e9b176356cbc847cdd5a551a9326ed6a36fd7ecb109bc
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