# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("PaDaS-Lab/privacy-policy-relation-extraction")
model = AutoModelForSequenceClassification.from_pretrained("PaDaS-Lab/privacy-policy-relation-extraction")Quick Links
Use:
from transformers import pipeline
pipe = pipeline("text-classification", model="PaDaS-Lab/privacy-policy-relation-extraction", return_all_scores=True)
example = "We store your basic account information, including your name, username, and email address until you ask us to delete them."
results = pipe(example)
threshold = 0.5
print([result for result in results[0] if result['score'] >= threshold])
Performance:
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PaDaS-Lab/privacy-policy-relation-extraction")