Update README.md

@misc {arabert_eou_2025,
author = {Nihad Askri},
title = {ARABERT Arabic End-of-Utterance Detection},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/nihad-ask/arabert-arabic-EOU-detection-model}}
}

README.md

@@ -1,12 +1,12 @@
-# Arabic End-of-Turn (EOU) Detection Model — MARBERT Fine-Tuned
+# Arabic End-of-Turn (EOU) Detection Model — AraBERT Fine-Tuned
 
-This model fine-tunes **MARBERT** for detecting **end-of-turn (EOU)** boundaries in Arabic dialogue.  
+This model fine-tunes **AraBERT** for detecting **end-of-turn (EOU)** boundaries in Arabic dialogue.  
 It predicts whether a given user message represents a **continuation** or an **end of turn**.
 
-- **Repository:** `nihad-ask/Arabert-EOU-detection-model`
-- **Task:** Binary End-of-Utterance Classification
-- **Language:** Arabic (MSA + saudi dilect)
-- **Base Model:** `UBC-NLP/MARBERT`
+- **Repository:** `nihad-ask/Arabert-EOU-detection-model`  
+- **Task:** Binary End-of-Utterance Classification  
+- **Language:** Arabic (MSA + Dialects)  
+- **Base Model:** `aubmindlab/bert-base-arabertv2`  
 
 ---
 
@@ -19,8 +19,6 @@ This is a **binary classification** task:
 | **0** | Speaker will continue (NOT end of turn) |
 | **1** | End of turn (EOU detected) |
 
-This helps conversational agents determine if the user has finished typing or is likely to continue.
-
 ---
 
 ## 📌 Use Cases
@@ -31,34 +29,50 @@ This helps conversational agents determine if the user has finished typing or is
 - Speech-to-text segmentation  
 - Customer support automation  
 
----
 
+---
 
 ## 📊 Evaluation
 
 ### **Balanced Validation Set**
 
-**Accuracy:** `0.9098`
+**Accuracy:** `0.9539`
 
 | Class | Precision | Recall | F1-score | Support |
 |-------|-----------|--------|----------|---------|
-| **0 – Continue** | 0.9058 | 0.9148 | 0.9103 | 1702 |
-| **1 – End of Turn** | 0.9139 | 0.9048 | 0.9094 | 1702 |
+| **0 – Continue** | 0.9494 | 0.9589 | 0.9541 | 1702 |
+| **1 – End of Turn** | 0.9585 | 0.9489 | 0.9536 | 1702 |
 
 **Overall:**
 
 | Metric | Score |
 |--------|--------|
-| Accuracy | 0.9098 |
-| Macro Avg F1 | 0.9098 |
-| Weighted Avg F1 | 0.9098 |
+| Accuracy | 0.9539 |
+| Macro Avg F1 | 0.9539 |
+| Weighted Avg F1 | 0.9539 |
 | Total Samples | 3404 |
 
+---
+
+### **Test Set**
 
+**Accuracy:** `0.8919`
 
-## 🧪 How to Use
+| Class | Precision | Recall | F1-score | Support |
+|-------|-----------|--------|----------|---------|
+| **0 – Continue** | 0.7671 | 0.9445 | 0.8466 | 3097 |
+| **1 – End of Turn** | 0.9713 | 0.8676 | 0.9165 | 6705 |
 
-### **Python (PyTorch)**
+**Overall:**
+
+| Metric | Score |
+|--------|--------|
+| Accuracy | 0.8919 |
+| Macro Avg F1 | 0.8815 |
+| Weighted Avg F1 | 0.8944 |
+| Total Samples | 9802 |
+
+---
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -79,12 +93,3 @@ if prediction == 1:
     print("End of turn")
 else:
     print("Speaker will continue")
-
-
-@misc{marbert_eou_2025,
-  author = {Nihad Askri},
-  title = {MARBERT Arabic End-of-Utterance Detection},
-  year = {2025},
-  publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/nihad-ask/marbert-arabic-EOU-detection-model}}
-}