Update README.md

README.md

@@ -54,18 +54,15 @@ This model is suitable for rapid prototyping and practical applications in autom
 ```python
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Load the tokenizer and the model
 tokenizer = AutoTokenizer.from_pretrained("your_username/model_name")
 model = AutoModelForSeq2SeqLM.from_pretrained("your_username/model_name")
 
-# Example text to summarize
 text = "Your long text that needs summarizing."
 
-# Add the prefix as during training
+# "summarize: " prefix
 input_text = "summarize: " + text
 inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
 
-# Generate the summary
 summary_ids = model.generate(inputs["input_ids"], max_length=150, num_beams=4, early_stopping=True)
 summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)