Update README.md

README.md

@@ -19,34 +19,36 @@ This model is based on the Facebook BART (Bidirectional and Auto-Regressive Tran
 ## Usage:
 
 ### Installation:
+
 You can install the necessary libraries using pip:
-    ```bash
+
+  ```bash
     pip install transformers
     pip datasets
     pip evaluate
     pip rouge_score
 
-### Inference
-  ```bash
-     # Load model directly
-     from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-    
-     tokenizer = AutoTokenizer.from_pretrained("suriya7/text_summarize")
-     model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/text_summarize")
-
-     def generate_summary(text):
-
-       inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
-
-       summary_ids = model.generate(inputs['input_ids'],max_new_tokens=100, do_sample=False)
-  
-       summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-       return summary
-
-    text_to_summarize = "Now, there is no doubt that one of the most important aspects of any Pixel phone is its camera.
-                         And there might be good news for all camera lovers. Rumours have suggested  that the Pixel 9 could come with a telephoto lens,
-                         improving its photography capabilities even further. Google will likely continue to focus on using AI to
-                         enhance its camera performance, in order to make sure that Pixel phones remain top contenders in the world of mobile photography"
-    summary = generate_summary(text_to_summarize)
-    
-    
+## Example Usage
+
+Here's an example of how to use this model for text summarization:
+
+```python
+# Load model and tokenizer
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("your_model_name")
+model = AutoModelForSeq2SeqLM.from_pretrained("your_model_name")
+
+# Input text to be summarized
+text_to_summarize = "Insert your text to summarize here..."
+
+# Generate summary
+inputs = tokenizer([text_to_summarize], max_length=1024, return_tensors='pt', truncation=True)
+summary_ids = model.generate(inputs['input_ids'], max_length=100, num_beams=4, early_stopping=True)
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# Print the generated summary
+print("Input Text:")
+print(text_to_summarize)
+print("\nGenerated Summary:")
+print(summary)