Update GreaterThan_Detector_NN_ReadMe.md

GreaterThan_Detector_NN_ReadMe.md

@@ -1,10 +1,12 @@
 # GreaterThan_Detector_NN: A Challenge in Numerical Reasoning
 
-Of course. Here is a 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 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.
 
 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? The specific task is to compare two numbers presented in a natural language format and identify the greater or lesser one.
+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
 
@@ -17,16 +19,10 @@ Prompt:
 Generated code
 10.00 , 09.21 Which is Greater ?
 
-
 Expected Completion:
 
 Generated code
 10.00!
-IGNORE_WHEN_COPYING_START
-content_copy
-download
-Use code with caution.
-IGNORE_WHEN_COPYING_END
 
 This makes the full, correct sequence:
 10.00 , 09.21 Which is Greater ? 10.00!
@@ -91,7 +87,8 @@ if __name__ == '__main__':
 
 Baseline Model Performance
 
-A baseline sequence model was trained on a dataset of 8,000 examples generated by the function above. While it learned the general format and solved many cases correctly, its performance on tricky edge cases reveals a critical weakness.
+A baseline sequence model was trained on a dataset of 8,000 examples generated by the function above. 
+While it learned the general format and solved many cases correctly, its performance on tricky edge cases reveals a critical weakness.
 
 Prompt	Model's Completion	Result
 10.00 , 09.21 Which is Greater ?	10.00!	✅ Correct
@@ -99,6 +96,7 @@ Prompt	Model's Completion	Result
 54.12 , 54.13 Which is Greater ?	54.13!	✅ Correct
 99.98 , 99.99 Which is Lesser ?	99.99!	❌ Incorrect
 00.01 , 10.00 Which is Lesser ?	00.00!	❌ Incorrect
+
 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.
@@ -127,16 +125,15 @@ Inside your directory, add your notebook (.ipynb) or scripts (.py) that define,
 
 Please include a GreaterThan_Detector_NN_ReadMe.md in your directory explaining your approach, architecture, and results.
 
-Submit a "community" comment to merge your solution into this repository.
+Submit a "community" comment to link your solution to this repository.
 
-Let's see who can build and train the most reliable numerical reasoner!
+Let's see who can build and train the most reliable numerical "Greater Than" reasoner!
 
 License
 
 The def generate_synthetic_data() function is open-sourced under the MIT License. See the LICENSE file for more details.
 
 
-
 Notes: 
 Generating synthetic data for tasks: ['4digit_Greater', '4digit_Lesser']...
 Generated 8000 total sequences.