Instructions to use KnutJaegersberg/claim_extraction_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KnutJaegersberg/claim_extraction_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KnutJaegersberg/claim_extraction_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/claim_extraction_classifier") model = AutoModelForSequenceClassification.from_pretrained("KnutJaegersberg/claim_extraction_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68077b06b7358d45ee4964285a4b2ddc2f402e5b85f7d30b1abee90973159719
- Size of remote file:
- 1.74 GB
- SHA256:
- a726a4112ab1db312072e54ac916e553551a6941bb33c7e1fb2305148eddfa85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.