CarelessLee/MCQ_pooled_full_rationale_confidence_predictor

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_name_or_path = 'CarelessLee/MCQ_pooled_full_rationale_confidence_predictor'
model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)

def predict(text):
    model.eval()
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        certainty_score = logits.item()
    return certainty_score

def evaluate_predictor(label_data):
    for sample in tqdm(label_data, desc="Processing questions"):
        highest_score = -1
        best_rationale = ""
        for rationale in sample['rationales']:
            text = f"Problem: {sample['question']}\n---\nRationale Step: {rationale}"
            predicted_certainty_score = predict(text)
            print("predicted_certainty_score: ", predicted_certainty_score)

if __name__ == "__main__":
    with open("example.json", 'r') as f:
        label_data = json.load(f)
    
    evaluate_predictor(label_data)