Instructions to use citclass/citclass_small_91 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use citclass/citclass_small_91 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="citclass/citclass_small_91", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("citclass/citclass_small_91", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("citclass/citclass_small_91", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19c9e87dc5b2f6c6c81d1775ee9ea68d9f314d8400744dbf6cd43e4554f7daeb
- Size of remote file:
- 267 MB
- SHA256:
- 414df251b58724eaaae8ebf71d4882754c69c685cd2fc0a2116e6f8bc5a0a676
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