Update README.md

README.md

@@ -11,11 +11,11 @@ base_model: aubmindlab/bert-base-arabertv2
 library_name: transformers
 ---
 
-# 🧠 Egyptian Dialect Text Classification by Fine-tuning AraBERT 🇪🇬
+#  Egyptian Dialect Text Classification by Fine-tuning AraBERT 🇪🇬
 
 This model is a fine-tuned version of `aubmindlab/bert-base-arabertv2`, specifically trained on Egyptian Arabic text for hate speech and offensive language classification
 
-with 91% accurency. 
+with 92% accurency. 
 
 It classifies text into 6 categories :
 
@@ -30,7 +30,7 @@ It classifies text into 6 categories :
 
 ---
 
-## ⚙️ Training Details
+##  Training Details
 
 - **Base Model:** AraBERT v2 (`aubmindlab/bert-base-arabertv2`)
 - **Dataset:** Egyptian Arabic hate speech dataset with 6 labeled categories
@@ -40,7 +40,7 @@ It classifies text into 6 categories :
 
 ---
 
-## 📊 Performance Metrics
+##  Performance Metrics
 
 Training was done using a GPU (`cuda`). Below is a snapshot of model performance across the first 15 epochs:
 
@@ -63,7 +63,7 @@ Training was done using a GPU (`cuda`). Below is a snapshot of model performance
 | 15    | 0.4289     | 0.6864   | 0.9153   | 0.9171    | 0.9153 | 0.9157 |
 
 ---
-## 📢 Using the Model Without a Token (For End Users)
+##  Using the Model Without a Token (For End Users)
 
 Since the model `Woolv7007/egyptian-text-classification` and the `labels.json` file are publicly available, you can load the model, tokenizer, and labels directly without needing any Hugging Face token or special setup.