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README.md

@@ -4,8 +4,8 @@
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [dair-ai/emotion dataset](https://huggingface.co/datasets/dair-ai/emotion). It is designed to classify text into various emotional categories. 
 
 It achieves the following results:
-- **Validation Accuracy:** 97.68%
-- **Test Accuracy:** 94.25%
+- **Validation Accuracy:** 94.25%
+- **Test Accuracy:** 93.2%
 
 ## Model Description
 
@@ -37,9 +37,9 @@ This model uses the DistilBERT architecture, which is a lighter and faster varia
 
 ### Results
 - **Training Time:** ~226 seconds
-- **Training Loss:** 0.1987
-- **Validation Accuracy:** 97.68%
-- **Test Accuracy:** 94.25%
+- **Training Loss:** 0.0520
+- **Validation Accuracy:** 94.25%
+- **Test Accuracy:** 93.2%
 
 ## Training Procedure
 
@@ -56,17 +56,19 @@ This model uses the DistilBERT architecture, which is a lighter and faster varia
 
 | Epoch | Training Loss | Validation Loss | Validation Accuracy |
 |-------|---------------|-----------------|---------------------|
-| 1     | 0.5383        | 0.1845          | 92.9%              |
+| 1     | 0.5383        | 0.1845          | 92.90%             |
 | 2     | 0.2254        | 0.1589          | 93.55%             |
-| 3     | 0.0739        | 0.0520          | 97.68%             |
+| 3     | 0.0520        | 0.1485          | 94.25%             |
 
-### Test Results
-- **Loss:** 0.1485
-- **Accuracy:** 94.25%
+### Final Evaluation
+- **Validation Loss:** 0.1485
+- **Validation Accuracy:** 94.25%
+- **Test Loss:** 0.1758
+- **Test Accuracy:** 93.2%
 
 ### Performance Metrics
 - **Training Speed:** ~212 samples/second
-- **Evaluation Speed:** ~1149 samples/second
+- **Evaluation Speed:** ~1144 samples/second
 
 ## Usage Example