# ASAG XLNet Regression Model

This model evaluates student answers by comparing them to reference answers and predicting a grade (regression).

## Model Details

- **Model Type:** XLNet for Regression
- **Task:** Automatic Short Answer Grading (ASAG)
- **Framework:** PyTorch/Transformers
- **Base Model:** xlnet-base-cased

## Usage

```python
from transformers import XLNetTokenizer, XLNetForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = XLNetTokenizer.from_pretrained("kenzykhaled/asag-xlnet-regression")
model = XLNetForSequenceClassification.from_pretrained("kenzykhaled/asag-xlnet-regression")

# Prepare inputs
student_answer = "It is vision."
reference_answer = "The stimulus is seeing or hearing the cup fall."

inputs = tokenizer(
    text=student_answer,
    text_pair=reference_answer,
    return_tensors="pt",
    padding=True,
    truncation=True
)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)

# Get predicted grade (normalized between 0-1)
predicted_grade = outputs.logits.item()
predicted_grade = max(0, min(1, predicted_grade))
print(f"Predicted grade: {predicted_grade:.4f}")
```

## Training Data

This model was trained on the Meyerger/ASAG2024 dataset.

## Performance

The model achieves the following metrics on the validation set:
- MSE (Mean Squared Error)
- RMSE (Root Mean Squared Error)
- MAE (Mean Absolute Error)
- Pearson Correlation