Update README.md

README.md

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----
-license: mit
----
+---
+language: ar
+license: mit
+library_name: transformers
+tags:
+- arabic
+- authorship-attribution
+- text-classification
+- arabert
+- literature
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+model-index:
+- name: arabic-authorship-classification
+  results:
+  - task:
+      type: text-classification
+      name: Authorship Attribution
+    metrics:
+    - type: accuracy
+      value: 0.7912
+      name: Accuracy
+    - type: f1
+      value: 0.7023
+      name: F1 Macro
+    - type: f1
+      value: 0.7891
+      name: F1 Weighted
+---
+
+# Arabic Authorship Classification Model
+
+## Model Description
+
+This model is fine-tuned for Arabic authorship attribution, capable of classifying texts from **21 distinguished Arabic authors**. Built on AraBERT architecture, it demonstrates strong performance in identifying literary writing styles across classical and modern Arabic literature.
+
+## Model Details
+
+- **Model Type:** Text Classification
+- **Base Model:** aubmindlab/bert-base-arabertv2
+- **Language:** Arabic (ar)
+- **Task:** Multi-class Authorship Attribution
+- **Classes:** 21 authors
+- **Parameters:** ~163M
+- **Dataset Size:** 4,157 texts
+
+## Performance
+
+| Metric | Score |
+|--------|-------|
+| Accuracy | 79.12% |
+| F1 Macro | 70.23% |
+| F1 Micro | 79.12% |
+| F1 Weighted | 78.91% |
+| Training Loss | 0.3439 |
+| Validation Loss | 0.7434 |
+
+## Supported Authors
+
+The model identifies texts from these 21 authors:
+
+**Arabic Literature:**
+- حسن حنفي (Hassan Hanafi) - 548 samples
+- عبد الغفار مكاوي (Abdul Ghaffar Makawi) - 396 samples  
+- نجيب محفوظ (Naguib Mahfouz) - 327 samples
+- جُرجي زيدان (Jurji Zaydan) - 327 samples
+- نوال السعداوي (Nawal El Saadawi) - 295 samples
+- عباس محمود العقاد (Abbas Mahmoud al-Aqqad) - 267 samples
+- محمد حسين هيكل (Mohamed Hussein Heikal) - 260 samples
+- طه حسين (Taha Hussein) - 255 samples
+- أحمد أمين (Ahmed Amin) - 246 samples
+- أمين الريحاني (Ameen Rihani) - 142 samples
+- فؤاد زكريا (Fouad Zakaria) - 125 samples
+- يوسف إدريس (Yusuf Idris) - 120 samples
+- سلامة موسى (Salama Moussa) - 119 samples
+- ثروت أباظة (Tharwat Abaza) - 90 samples
+- أحمد شوقي (Ahmed Shawqi) - 58 samples
+- أحمد تيمور باشا (Ahmed Taymour Pasha) - 57 samples
+- جبران خليل جبران (Khalil Gibran) - 30 samples
+- كامل كيلاني (Kamel Kilani) - 25 samples
+
+**Translated Literature:**
+- ويليام شيكسبير (William Shakespeare) - 238 samples
+- غوستاف لوبون (Gustave Le Bon) - 150 samples  
+- روبرت بار (Robert Barr) - 82 samples
+
+## Usage
+
+### Direct Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model
+tokenizer = AutoTokenizer.from_pretrained("your-username/arabic-authorship-classification")
+model = AutoModelForSequenceClassification.from_pretrained("your-username/arabic-authorship-classification")
+
+# Predict
+text = "النص العربي المراد تصنيفه"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1)
+    confidence = torch.max(predictions)
+
+print(f"Predicted class: {predicted_class.item()}")
+print(f"Confidence: {confidence:.4f}")
+```
+
+### Pipeline Usage
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", 
+                     model="your-username/arabic-authorship-classification",
+                     tokenizer="your-username/arabic-authorship-classification")
+
+result = classifier("النص العربي للتصنيف")
+print(result)
+```
+
+## Training Data
+
+- **Size:** 4,157 Arabic text samples
+- **Source:** Curated Arabic literary corpus
+- **Genres:** Essays, novels, poetry, philosophical works
+- **Period:** Classical to modern Arabic literature
+- **Quality:** High-quality literary texts
+
+## Training Procedure
+
+### Training Hyperparameters
+
+- **Base Model:** aubmindlab/bert-base-arabertv2
+- **Max Length:** 512 tokens
+- **Learning Rate:** 2e-5
+- **Batch Size:** 8 (train), 16 (eval)
+- **Epochs:** 150 (with early stopping)
+- **Optimizer:** AdamW
+- **Weight Decay:** 0.01
+
+### Training Infrastructure
+
+- **Hardware:** GPU-accelerated training
+- **Framework:** PyTorch + Transformers
+- **Mixed Precision:** Enabled (fp16)
+
+## Evaluation
+
+The model achieves strong performance across all 21 author classes:
+
+- **Balanced Performance:** F1 weighted (78.91%) shows good performance across all authors
+- **High Accuracy:** 79.12% accuracy for 21-class classification
+- **Robust Generalization:** Reasonable gap between training and validation loss
+
+## Limitations
+
+- Performance may vary on non-literary Arabic texts
+- Best suited for Modern Standard Arabic (MSA)
+- May struggle with very short texts (<50 words)
+- Not tested on dialectical Arabic variations
+- Limited to the 21 authors in training data
+
+## Bias and Ethical Considerations
+
+- Training data focuses on established literary figures
+- May reflect historical and cultural biases in literary canon
+- Gender representation varies across authors
+- Consider fairness when applying to contemporary texts
+
+## Citation
+
+```bibtex
+@misc{arabic-authorship-classification-2024,
+  title={Arabic Authorship Classification Model},
+  author={Your Name},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/your-username/arabic-authorship-classification}
+}
+```
+
+## Model Card Authors
+
+[Your Name]
+
+## Model Card Contact
+
+[Your Contact Information]