---
language: en
license: mit
library_name: transformers
tags:
- xlnet
- automatic-short-answer-grading
- regression
- education
- short-answer
- assessment
- grading
- transformers
pipeline_tag: text-classification
datasets:
- Meyerger/ASAG2024
metrics:
- mse
- rmse
- mae
- pearson correlation
model-index:
- name: XLENT_ASAG
  results:
  - task:
      type: regression
      name: automatic short answer grading
    metrics:
      - type: mse
        value: 0.035
      - type: rmse
        value: 0.187
      - type: mae
        value: 0.142
      - type: pearson correlation
        value: 0.912
---

# 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
- **Library:** Transformers

## Usage

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

# Load model and tokenizer
tokenizer = XLNetTokenizer.from_pretrained("kenzykhaled/XLENT_ASAG")
model = XLNetForSequenceClassification.from_pretrained("kenzykhaled/XLENT_ASAG")

# 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}")
```

## Inference API Usage

This model can be used directly with the Hugging Face Inference API:

```python
import requests

API_URL = "https://api-inference.huggingface.co/models/kenzykhaled/XLENT_ASAG"
headers = {"Authorization": "Bearer YOUR_HUGGING_FACE_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

data = {
    "inputs": {
        "source_sentence": "It is vision.",
        "sentences": ["The stimulus is seeing or hearing the cup fall."]
    }
}

result = query(data)
print(result)
```

## Training Data

This model was trained on the Meyerger/ASAG2024 dataset.

## Use Cases

- Automated grading of student short-answer responses
- Educational technology platforms
- Learning management systems
- Assessment tools
- Teacher assistance for grading

## Limitations

- The model is trained on specific educational domains and may not generalize well to all subjects
- Performance depends on the similarity of input data to the training data
- Should be used as an assistive tool for grading rather than a complete replacement for human evaluation

## Ethical Considerations

When using this model for automated grading:
- Be transparent with students about the use of AI for grading
- Consider potential biases in evaluation
- Provide human review of edge cases
- Allow students to appeal automated grades