norbert3-fine-absa-full

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-fine-absa-full")
model = AutoModelForSequenceClassification.from_pretrained("ltg/norbert3-fine-absa-full", trust_remote_code=True)

model.eval()

text = "fastlegen lytter til meg, men jeg synes ventetiden er for lang."

# tokenize input
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

# Run inference
with torch.no_grad():
    outputs = model(**inputs)

# Get predictions
threshold = 0.5
probs = torch.sigmoid(outputs.logits).squeeze()
predictions = [model.config.id2label[i] for i, prob in enumerate(probs) if prob > threshold]
print(predictions)
# -> ['ppr_pos', 'wtp_neg'] (patient-provider/staff relationships:positive, waiting time for appointment:negative)