Update README.md

README.md

@@ -26,7 +26,9 @@ This model is optimized for processing larger inputs. For the most accurate resu
 
 ### How to use
 
-You can use this model in your application using the Hugging Face Transformers library. Here is an example:
+You can use this model in your application using the Hugging Face Transformers library.
+Make sure to prefix your input with "Keyword: " for the model to generate keywords.
+Here is an example:
 
 ```python
 from transformers import T5TokenizerFast, T5ForConditionalGeneration
@@ -36,7 +38,7 @@ tokenizer = T5TokenizerFast.from_pretrained('mrutyunjay-patil/keywordGen-v1')
 model = T5ForConditionalGeneration.from_pretrained('mrutyunjay-patil/keywordGen-v1')
 
 # Define the input text
-input_text = "I love going to the park."
+input_text = "Keyword: I recently purchased the new headphones and they are incredible. The sound quality is superb, providing crystal clear audio in all ranges. The noise-cancelling feature is very effective, blocking out almost all ambient noise. I also love the comfortable design - they fit perfectly over my ears and don't cause any discomfort, even after long periods of use. The battery life is also impressive, lasting up to 20 hours on a single charge. Overall, I'm extremely satisfied with this product."
 
 # Encode the input text
 input_ids = tokenizer.encode(input_text, return_tensors='pt')