debertav3-stance-detection

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
model_name = "yassine-mhirsi/debertav3-stance-detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Predict
topic = "AI should replace human teachers"
argument = "Teachers provide emotional support that AI cannot replicate"

text = f"Topic: {{topic}} [SEP] Argument: {{argument}}"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(probs, dim=-1).item()

stance = "PRO" if predicted_class == 1 else "CON"
confidence = probs[0][predicted_class].item()

print(f"Stance: {{stance}}")
print(f"Confidence: {{confidence:.2%}}")