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---
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}
}
``` |