Instructions to use citclass/citclass_small_71 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use citclass/citclass_small_71 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="citclass/citclass_small_71", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("citclass/citclass_small_71", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("citclass/citclass_small_71", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2df2edb01b4a918ec7335d64e91018594dbd60c5e05604bd4f18da8ef098c3ae
- Size of remote file:
- 267 MB
- SHA256:
- f6df5a46dbe135b0e83312597b1e11f174fda4db4ac797a248c367d876e2ccad
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