Update README.md

README.md

@@ -46,8 +46,6 @@ evaluation_results:
     Present: 694
 ---
 
-
-
 ## Model Overview
 
 This model is a **text classification model** trained to **predict the tense of English sentences**: **Past**, **Present**, or **Future**. It is based on the `bert-base-uncased` architecture.
@@ -57,6 +55,12 @@ This model is a **text classification model** trained to **predict the tense of
 This model can be used in applications such as:
 - Identifying if statements are discussing past/present/future needs, motivations, products, etc.
 - Determining current events or situations in text.
+
+## Intended Use
+
+This model can be used in applications such as:
+- Identifying if statements are discussing past needs, motivations, products, etc.
+- Determining current events or situations in text.
 - Predicting future plans or intentions based on sentence structure.
 
 ### Example Sentences and Labels
@@ -86,13 +90,6 @@ The model achieves a **perfect accuracy of 1.00** on the test set, with **precis
 | **Macro Avg** | 1.00    | 1.00   | 1.00     | 1998    |
 | **Weighted Avg** | 1.00 | 1.00   | 1.00     | 1998    |
 
-## Intended Use
-
-This model can be used in applications such as:
-- Identifying if statements are discussing past needs, motivations, products, etc.
-- Determining current events or situations in text.
-- Predicting future plans or intentions based on sentence structure.
-
 ### Limitations
 
 While the model performs well on the provided dataset, it may not generalize to all types of English text, particularly those with ambiguous or complex sentence structures.