README.md

---
language: ar
license: apache-2.0
tags:
- arabic
- regression
- arabertv02
- scoring
- education
datasets:
- AraScore
metrics:
- mse
- rmse
- mae
- r2
pipeline_tag: text-classification
library_name: transformers
---

# Arabic Text Scoring Regression Model

## Model Description

This model is fine-tuned from [AraELECTRA](https://huggingface.co/aubmindlab/bert-base-arabertv02) for the task of 
scoring Arabic text answers. It predicts a continuous score for a given Arabic text response.

## Training Data

The model was trained on the AraScore dataset, which contains Arabic text answers with corresponding scores.

## Metrics

The model achieves the following performance metrics:
- MSE (Mean Squared Error)
- RMSE (Root Mean Squared Error)
- MAE (Mean Absolute Error)
- R² (R-squared)

## Usage

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import re

# Load model and tokenizer
model_name = "kenzykhaled/arabic-answer-scoring"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Function to preprocess Arabic text
def preprocess_arabic_text(text):
    if not isinstance(text, str):
        return ""
    
    # Remove diacritics (تشكيل)
    text = re.sub(r'[ً-ٰٟ]', '', text)
    
    # Normalize Arabic letters
    text = re.sub('[إأآا]', 'ا', text)  # Normalize Alif forms
    text = re.sub('ى', 'ي', text)      # Normalize Yaa
    text = re.sub('ة', 'ه', text)      # Normalize Taa Marbouta
    
    # Remove non-Arabic characters except spaces
    text = re.sub(r'[^؀-ۿ\s]', '', text)
    
    # Remove extra spaces
    text = re.sub(r'\s+', ' ', text).strip()
    
    return text

# Define prediction function
def predict_score(text):
    # Preprocess and tokenize
    processed_text = preprocess_arabic_text(text)
    inputs = tokenizer(processed_text, return_tensors="pt", padding=True, truncation=True, max_length=256)
    
    # Move to appropriate device (GPU if available)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    # Predict
    model.eval()
    with torch.no_grad():
        outputs = model(**inputs)
        score = outputs.logits.item()
    
    return score

# Example usage
sample_text = "هذه إجابة نموذجية باللغة العربية."
score = predict_score(sample_text)
print(f"Predicted score: ")
```

## Limitations

- The model is optimized for educational answer scoring and may not perform well on other types of text.
- The model works best with text similar to that in the training data.

## Citation

If you use this model, please cite:
```
@misc{arabic-scoring-model,
  author = {Your Name},
  title = {Arabic Text Answer Scoring Model},
  year = {2025},
  publisher = {Hugging Face}
}
```