Update README.md

README.md

@@ -90,16 +90,6 @@ While the model performs well on the provided dataset, it may not generalize to
 
 ## How to Use 🚀
 
-```python
-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)
-```
-
-## 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
@@ -114,9 +104,32 @@ 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(result)  # Output the result
+```
+
+### Classifying Text in a DataFrame
+
+For batch processing, you can classify multiple text entries stored in a DataFrame. This example demonstrates how to read a CSV file and add a new column with the predicted labels:
+
+```python
+# Import libraries
+from transformers import pipeline  # Import the pipeline function from the transformers library
+import pandas as pd  # Import pandas for data manipulation
+
+# Read the CSV file
+file_path = 'filename.csv'  # Define the path to the CSV file
+df = pd.read_csv(file_path)  # Read the CSV file into a DataFrame
+
+# Initialize the text classification pipeline
+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
+)
+
+# Apply the classifier to each row in the "Text" column and store results in a new column "label"
+df['label'] = df['Text'].apply(lambda text: classifier(text)[0]['label'])  # Classify each text and store the label
 
-# Print the classification result, which includes the predicted label and the confidence score
-print(result)  # Output the result to the console
+# Display the DataFrame with the new "label" column
+df.head(5)  # Display the first 5 rows of the DataFrame
+```