Update README.md

README.md

@@ -44,4 +44,36 @@ 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.
+
+## 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.
+
+## 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.
+
+## How to Use
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="your-username/your-model-name")
+result = classifier("The meeting will take place tomorrow.")
+print(result)