Update README.md

README.md

@@ -85,8 +85,9 @@ print(predict_sentiment("The acting was terrible, and the story made no sense.")
 ## Training Details
 
 ### Training Data
-- The model was fine-tuned on the **IMDB dataset** (50,000 labeled movie reviews).
-- The dataset is **balanced** (25,000 positive and 25,000 negative reviews).
+- The model was fine-tuned on the IMDB dataset (50,000 labeled movie reviews).
+- The dataset is balanced (25,000 positive and 25,000 negative reviews).
+- The training split consisted of 40,000 samples, while 5,000 samples were used for validation.
 
 ### Training Procedure
 #### Preprocessing
@@ -108,14 +109,14 @@ print(predict_sentiment("The acting was terrible, and the story made no sense.")
 
 ### Testing Data, Factors & Metrics
 #### Testing Data
-- The model was evaluated on a **10,000-sample test set** from the IMDB dataset.
+- The model was evaluated on a 5,000-sample test set from the IMDB dataset.
 
 #### Metrics
-- **Accuracy:** 92,4%
+- **Accuracy:** 90,4%
 - **Precision, Recall, F1-score:**
-  - **Precision:** 92,4%
-  - **Recall:** 92.3%
-  - **F1-score:** 92.3%
+  - **Precision:** 92,1%
+  - **Recall:** 88.2%
+  - **F1-score:** 90.0%
 
 ## Model Examination
 - The model performs well on **general sentiment classification** but may struggle with **sarcasm, irony, or very short reviews**.