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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"[PAD]":0,"[UNK]":1,"[CLS]":2,"[SEP]":3,"[MASK]":4,"A":5,"C":6,"D":7,"E":8,"F":9,"G":10,"H":11,"I":12,"K":13,"L":14,"M":15,"N":16,"P":17,"Q":18,"R":19,"S":20,"T":21,"V":22,"W":23,"Y":24} |