Update README.md

README.md

@@ -29,9 +29,20 @@ configs:
     path: data/train-*
 ---
 
+# Evaluation summary
+
+We introduce HumanEval for Kotlin, created from scratch by human experts.
+All HumanEval solutions and tests are written by an expert olympiad programmer with 6 years experience in Kotlin, and independently checked by a programmer with 4 years experience in Kotlin. 
+The tests we implement are eqivalent to the original HumanEval tests for Python, and we fix the prompt signatures to address the generic variable signature we describe above.
 
 # How to use 
 
+The evaluation presented as dataset which is prepared in a format suitable for MXEval and can be easily integrated into the MXEval pipeline.
+
+During the code generation step, we use early stopping on the `}\n}` sequence to expedite the process. We also perform some code post-processing before evaluation—specifically, we remove all comments and signatures.
+
+The early stopping method, post-processing steps, and evaluation code are available in the example below.
+
 ```python
 import torch
 import jsonlines
@@ -137,5 +148,9 @@ evaluate_functional_correctness(
     problem_file=problem_dict,
 )
 
-
 ```
+
+
+# Results:
+
+We evaluated multiple coding models using this benchmark, and the results are presented in the table below.