Instructions to use citclass/citclass_large_91 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use citclass/citclass_large_91 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="citclass/citclass_large_91", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("citclass/citclass_large_91", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("citclass/citclass_large_91", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9a245b8523f9f58efbcc399ea21123e6742ca2188f80a2f660cc3f7212166fd
- Size of remote file:
- 267 MB
- SHA256:
- 2eab07d3233f22cf9f44791b78d98e4692ae054bdbfcf0eea593ca8cd86e3493
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.