Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language: ar
+license: apache-2.0
+library_name: transformers
+tags:
+- sentiment-analysis
+- arabic
+- marbert
+- twitter
+- text-classification
+datasets:
+- mksaad/arabic-sentiment-twitter-corpus
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+---
+
+# MARBERT Model for Arabic Sentiment Analysis (Positive/Negative)
+
+This is a fine-tuned version of `UBC-NLP/MARBERTv2` for Arabic Sentiment Analysis.
+The model is trained to classify Arabic text (specifically tweets) into two categories: **Positive (`LABEL_1`)** or **Negative (`LABEL_0`)**.
+
+## 🚀 Live Demo
+
+You can test the model live on the Hugging Face Space:
+**[https://huggingface.co/spaces/iMeshal/arabic-sentiment-app](https://huggingface.co/spaces/iMeshal/arabic-sentiment-app)**
+
+---
+
+## 📊 Model Performance
+
+The model was trained on 80% of the training data and validated on 20%. The final evaluation was performed on a separate, unseen test set.
+
+**Final Test Set Results (Accuracy: 94.40%)**
+
+| Metric | Score |
+| :--- | :---: |
+| **Accuracy** | **94.40%** |
+| F1 (Macro) | 94.40% |
+| Precision (Macro) | 94.40% |
+| Recall (Macro) | 94.40% |
+| Loss | 0.1667 |
+
+The model achieved its best validation accuracy of **93.4%** at Epoch 2, and `load_best_model_at_end` was used.
+
+---
+
+## 💻 Intended Use (How to use)
+
+You can use this model directly with the `transformers` pipeline.
+
+```python
+from transformers import pipeline
+
+# Load the pipeline
+pipe = pipeline(
+    "sentiment-analysis", 
+    model="iMeshal/arabic-sentiment-classifier-marbert"
+)
+
+# Test with new texts
+texts = [
+    "هذا المنتج رائع جداً أنصح به",
+    "أسوأ خدمة عملاء على الإطلاق",
+    "الجو اليوم جميل"
+]
+
+results = pipe(texts)
+print(results)
+# Output:
+# [
+#   {'label': 'LABEL_1', 'score': 0.99...}, # Positive
+#   {'label': 'LABEL_0', 'score': 0.99...}, # Negative
+#   {'label': 'LABEL_1', 'score': 0.98...}  # Positive
+# ]
+
+```
+
+## 📚 Training Data
+
+The model was trained on the **[Arabic Sentiment Twitter Corpus](https://www.kaggle.com/datasets/mksaad/arabic-sentiment-twitter-corpus)** dataset from Kaggle.
+
+* **Preprocessing:** Long/concatenated tweets (which appeared to be noise) were cleaned.
+* **Training Set:** ~24,163 samples.
+* **Validation Set:** ~6,041 samples.
+* **Test Set:** ~11,508 samples.
+* **Balance:** All datasets were perfectly balanced (approx. 50% Positive / 50% Negative).
+
+---
+
+## ⚙️ Training Procedure
+
+The model was trained using the `transformers.Trainer` class with the following key hyperparameters:
+
+* **Framework:** PyTorch
+* **Base Model:** `UBC-NLP/MARBERTv2`
+* **Epochs:** 3 (with Early Stopping)
+* **Early Stopping:** Patience set to 2 (training stopped at Epoch 3, but Epoch 2 was the best).
+* **Batch Size:** 16
+* **Learning Rate:** 2e-5
+* **Tokenizer:** `AutoTokenizer` (with `padding="max_length"`, `truncation=True`, `max_length=512`)
+
+---
+
+### 📞 Contact
+
+* **Name:** Meshal AL-Qushaym
+* **Email:** meshalqushim@outlook.com
+* **Kaggle:** [kaggle.com/meshalfalah](https://www.kaggle.com/meshalfalah)