Update README.md

README.md

@@ -44,11 +44,26 @@ evaluation_results:
     Future: 727
     Past: 577
     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.
+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.
+
+## 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
+
+| Sentence                                                                 | Label   |
+|--------------------------------------------------------------------------|---------|
+| the fishermen had caught a variety of fish including bass and perch      | Past    |
+| medical professionals are researching the impact of social determinants on health | Present |
+| in the future robotic surgical systems will have been empowering surgeons to perform increasingly complex procedures | Future  |
 
 ## Training Details
 
@@ -58,12 +73,25 @@ The model was fine-tuned on the `ProfessorLeVesseur/EnglishTense` dataset, which
 
 The model achieves a perfect accuracy of 1.00 on the test set, with precision, recall, and F1-scores also at 1.00 for all classes. These results indicate excellent performance in classifying sentence tenses.
 
+### Classification Report
+
+### Classification Report
+
+| Class       | Precision | Recall | F1-Score | Support |
+|-------------|-----------|--------|----------|---------|
+| Future      | 1.00      | 1.00   | 1.00     | 727     |
+| Past        | 1.00      | 1.00   | 1.00     | 577     |
+| Present     | 1.00      | 1.00   | 1.00     | 694     |
+| **Accuracy**|           |        | 1.00     | 1998    |
+| **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:
-- Predicting future plans or intentions based on sentence structure.
 - 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