Instructions to use shepherdgroup/NuTCRacker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shepherdgroup/NuTCRacker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="shepherdgroup/NuTCRacker")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shepherdgroup/NuTCRacker") model = AutoModelForMaskedLM.from_pretrained("shepherdgroup/NuTCRacker") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f43d42bd7b99d4b7afe0ed1cfb0c84763ca6e3b780dab3cdd57b282547294f62
- Size of remote file:
- 239 MB
- SHA256:
- 59586235af768562a414d402e19efa0264c52374846e3b8e89757f193f8eb7a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.