Update README.md

README.md

@@ -24,7 +24,7 @@ This is a fine-tuned version of https://huggingface.co/t5-small
 >>>   summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
 >>>   return summary
 
->>> generate_text("Should I Invest in stocks")
+>>> generate_text("Android 3 Stars!!")
 
 It's good
 ```
@@ -52,7 +52,7 @@ It's good
 
 The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): 
 
-> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
+> With T5, we propose reframing all NLP tasks into a unified text-to-text format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
 
 T5-Small is the checkpoint with 60 million parameters. 
 
@@ -79,15 +79,15 @@ See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learnin
 
 ## Out-of-Scope Use
 
-More information needed.
+More information is needed.
 
 # Bias, Risks, and Limitations
 
-More information needed.
+More information is needed.
 
 ## Recommendations
 
-More information needed.
+More information is needed.
 
 # Training Details