kenzykhaled
/

arabic-answer-scoring

@@ -1,199 +1,111 @@
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 ---
+language: ar
+license: apache-2.0
+tags:
+- arabic
+- regression
+- arabertv02
+- scoring
+- education
+datasets:
+- AraScore
+metrics:
+- mse
+- rmse
+- mae
+- r2
 ---
+# 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}
+}
+```