ProfessorLeVesseur
/

bert-base-cased-timeframe-classifier

@@ -46,34 +46,34 @@ 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.
-## 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
 The model was fine-tuned on the `ProfessorLeVesseur/EnglishTense` dataset, which provides a diverse set of sentences labeled with their respective tenses. The training involved optimizing the model's weights for three epochs using a learning rate of 5e-5.
-## Evaluation Results
 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
 | Class       | Precision | Recall | F1-Score | Support |
 |-------------|-----------|--------|----------|---------|
@@ -84,11 +84,11 @@ 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    |
-### 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.
-## How to Use
 ```python
 from transformers import pipeline

     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.
+## 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 🏋️‍♂️
 The model was fine-tuned on the `ProfessorLeVesseur/EnglishTense` dataset, which provides a diverse set of sentences labeled with their respective tenses. The training involved optimizing the model's weights for three epochs using a learning rate of 5e-5.
+## Evaluation Results 📊
 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 ✅
 | Class       | Precision | Recall | F1-Score | Support |
 |-------------|-----------|--------|----------|---------|
 | **Macro Avg** | 1.00    | 1.00   | 1.00     | 1998    |
 | **Weighted Avg** | 1.00 | 1.00   | 1.00     | 1998    |
+### 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.
+## How to Use 🚀
 ```python
 from transformers import pipeline