Update README.md

README.md

@@ -95,4 +95,27 @@ from transformers import pipeline
 
 classifier = pipeline("text-classification", model="ProfessorLeVesseur/bert-base-cased-timeframe-classifier")
 result = classifier("The meeting will take place tomorrow.")
-print(result)
+print(result)
+
+## How to Use 🚀
+
+This model can be used for text classification tasks, either for individual text inputs or for batch processing via a DataFrame. Below are examples of both use cases.
+
+### Classifying Input Text
+
+To classify a single piece of text and retrieve the predicted label along with the confidence score, you can use the following code:
+
+```python
+from transformers import pipeline  # Import the pipeline function from the transformers library
+
+# Initialize a text classification pipeline using the specified model
+classifier = pipeline(
+    "text-classification",  # Specify the task type as text classification
+    model="ProfessorLeVesseur/bert-base-cased-timeframe-classifier"  # Specify the model to use from the Hugging Face Model Hub
+)
+
+# Use the classifier to predict the label for the input text
+result = classifier("MTSS.ai is the future of education, call it education².")  # Classify the input text and store the result
+
+# Print the classification result, which includes the predicted label and the confidence score
+print(result)  # Output the result to the console