Update GreaterThan_Detector_NN_ReadMe.md

GreaterThan_Detector_NN_ReadMe.md

@@ -1,16 +1,19 @@
 # GreaterThan_Detector_NN: A Challenge in Numerical Reasoning
 
-Here is a GreaterThan_Detector_NN_ReadMe.md file designed for your Hugging Face repository. 
-It reports the results, presents the core challenge, and provides the dataset generator without disclosing the proprietary model details, inviting the community to tackle the problem.
+Here is a GreaterThan_Detector_NN_ReadMe.md challenge defintion file.
+It reports the results, presents the core challenge, and provides the dataset generator,
+inviting the community to tackle the same problem.
 
 GreaterThan_Detector_NN: A Challenge in Numerical Reasoning
 
-This repository, part of the Neural_Nets_Doing_Simple_Tasks collection, explores a fundamental question: can a general-purpose neural network learn a task that is trivial for humans but requires symbolic reasoning? 
+This repository, part of the Neural_Nets_Doing_Simple_Tasks collection, explores a fundamental question: 
+can a general-purpose neural network learn a task that is trivial for humans but requires symbolic reasoning? 
 The specific task is to compare two numbers presented in a natural language format and identify the greater or lesser one.
 
 The Objective
 
-The goal is to create a model that can reliably process a text-based prompt, parse two numbers, understand the question ("Which is Greater?" or "Which is Lesser?"), and generate the correct numerical answer.
+The goal is to create a model that can reliably process a text-based prompt, parse two numbers, understand the question 
+("Which is Greater?" or "Which is Lesser?"), and generate the correct numerical answer.
 
 The input-output format is a single, continuous text sequence. For example:
 
@@ -21,7 +24,6 @@ Generated code
 
 Expected Completion:
 
-Generated code
 10.00!
 
 This makes the full, correct sequence:
@@ -29,9 +31,11 @@ This makes the full, correct sequence:
 
 The Dataset
 
-The training data is synthetically generated. This ensures an endless supply of examples but also presents a challenge: is the generated data diverse enough to teach true reasoning, or does it just encourage brittle pattern matching?
+The training data is synthetically generated. This ensures an endless supply of examples but also presents a challenge: 
+is the generated data diverse enough to teach true reasoning, or does it just encourage brittle pattern matching?
 
-You can generate your own dataset using the Python function below. This is the exact generator used for our baseline model, ensuring a level playing field.
+You can generate your own dataset using the Python function below. This is the exact generator used for our baseline model, 
+ensuring a level playing field.
 
 Dataset Generator Code
 
@@ -99,7 +103,8 @@ Prompt	Model's Completion	Result
 
 Analysis & The Challenge
 
-The baseline model demonstrates a classic problem in machine learning: it has learned to be a good pattern matcher but has not acquired a robust, generalizable algorithm for numerical comparison.
+The baseline model demonstrates a classic problem in machine learning: it has learned to be a good pattern matcher 
+but has not acquired a robust, generalizable algorithm for numerical comparison.
 
 The failures are illuminating: