---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
This is the MCQ confidence prediction model that outputs a certainty score given a math question and a full step-by-step rationale that attempts to solve the question.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Shiyao Li
- **Finetuned from model :** meta-llama/Meta-Llama-3.1-8B

### Model Example Usage
```
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)
```