Add examples

README.md

@@ -22,6 +22,24 @@ It achieves the following results on the evaluation set:
 - Train Binary Accuracy: 0.9915
 - Epoch: 8
 
+## Example Usage
+```py
+from transformers import AutoTokenizer, TFBertForSequenceClassification, BertTokenizer
+import tensorflow as tf
+
+model_name = 'Chakshu/conversation_terminator_classifier' 
+
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = TFBertForSequenceClassification.from_pretrained(model_name)
+inputs = tokenizer("I will talk to you later", return_tensors="np", padding=True)
+outputs = model(inputs.input_ids, inputs.attention_mask)
+probabilities = tf.nn.sigmoid(outputs.logits)
+
+# Round the probabilities to the nearest integer to get the class prediction
+predicted_class = tf.round(probabilities)
+print("The last message by the user indicates that the conversation has", "'ENDED'" if int(predicted_class.numpy()) == 1 else "'NOT ENDED'")
+```
+
 ## Model description
 
 Classifies if the user is ending the conversation or wanting to continue it.