Instructions to use citclass/citclass_large_71 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use citclass/citclass_large_71 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="citclass/citclass_large_71", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("citclass/citclass_large_71", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("citclass/citclass_large_71", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7809e8f1a5f648b69b8544f3ec2f2f3fe6f718c81b543d2e40216b0fa699a2b5
- Size of remote file:
- 267 MB
- SHA256:
- 7da4a332ea38980a709b555432df4094681263a6ec59f28bad1e005cb04ea4fe
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