Update README.md

README.md

@@ -30,18 +30,13 @@ It can be used in applications requiring emotional analysis in English text.
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.511         | 1.0   | 500  | 0.1797          | 0.933    |
-| 0.1495        | 2.0   | 1000 | 0.1449          | 0.938    |
-| 0.1002        | 3.0   | 1500 | 0.1350          | 0.9415   |
-| 0.0707        | 4.0   | 2000 | 0.1382          | 0.94     |
-
-- **Validation Accuracy**: 
-  - Epoch 1: 0.9275
-  - Epoch 2: 0.9345
-  - Epoch 3: 0.940
-  - Epoch 4: 0.940
-- **Test Accuracy**: 100% accuracy on the provided 10 test examples.
+| 0.5164        | 1.0   | 500  | 0.1887          | 0.9275   |
+| 0.1464        | 2.0   | 1000 | 0.1487          | 0.9345   |
+| 0.0994        | 3.0   | 1500 | 0.1389          | 0.9415   |
+| 0.0701        | 4.0   | 2000 | 0.1479          | 0.94     |
 
+- **Overall Training Loss**: 0.2081
+- **Test Accuracy**: 100% accuracy on the provided 10 test examples.
 
 ## Usage
 ```python
@@ -51,9 +46,9 @@ classifier = pipeline("text-classification", model="Zoopa/emotion-classification
 text = "I am so happy today!"
 result = classifier(text)
 print(result)
-‘‘‘
+```
 
 ## Limitations
   - The model only supports English.
   - The training dataset may contain biases, affecting model predictions on test data.
-  - Edge Cases like mixed emotions might reduce accuracy. 
+  - Edge Cases like mixed emotions might reduce accuracy.