update example

README.md

@@ -38,7 +38,9 @@ question_string = ("If it takes 1 hour to dry 25 shirts under the sun, "
                    "how long will it take to dry 30 shirts under the sun? "
                    "Reason step by step")
 
-question = tg.Variable(question_string, role_description="question to the LLM", requires_grad=False)
+question = tg.Variable(question_string, 
+                       role_description="question to the LLM", 
+                       requires_grad=False)
 
 answer = model(question)
 ```
@@ -56,19 +58,26 @@ As you can see, **the model's answer is incorrect.** We can optimize the answer
 answer.set_role_description("concise and accurate answer to the question")
 
 # Step 2: Define the loss function and the optimizer, just like in PyTorch! 
-# Here, we don't have SGD, but we have TGD (Textual Gradient Descent) that works with "textual gradients". 
+# Here, we don't have SGD, but we have TGD (Textual Gradient Descent) 
+# that works with "textual gradients". 
 optimizer = tg.TGD(parameters=[answer])
-evaluation_instruction = f"Here's a question: {question_string}. Evaluate any given answer to this question, be smart, logical, and very critical. Just provide concise feedback."
-
-# TextLoss is a natural-language specified loss function that describes how we want to evaluate the reasoning.
+evaluation_instruction = (f"Here's a question: {question_string}. " 
+                           "Evaluate any given answer to this question, "
+                           "be smart, logical, and very critical. "
+                           "Just provide concise feedback.")
+
+
+# TextLoss is a natural-language specified loss function that describes 
+# how we want to evaluate the reasoning.
 loss_fn = tg.TextLoss(evaluation_instruction)
 ```
 > :brain: loss: [...] Your step-by-step reasoning is clear and logical, 
-> but it contains a critical flaw in the assumption that drying time is directly proportional 
-> to the number of shirts. [...]
+> but it contains a critical flaw in the assumption that drying time is 
+> directly proportional to the number of shirts. [...]
 
 ```python
-# Step 3: Do the loss computation, backward pass, and update the punchline. Exact same syntax as PyTorch!
+# Step 3: Do the loss computation, backward pass, and update the punchline. 
+# Exact same syntax as PyTorch!
 loss = loss_fn(answer)
 loss.backward()
 optimizer.step()