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:
- 333258ea2b03082e0ca9a620df053cad95b89d7f775340f8165a6066ab2dcd5c
- Size of remote file:
- 438 MB
- SHA256:
- 0a761dedd1aa17b4a97474e3c7fecb7c323d90571172300281c9b8fcdc107b0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.