Update README.md (#1)

- Update README.md (4a231609a92176efc460a48fa8e84a20bc56225f)


Co-authored-by: Yaroslav Golubev <[email protected]>

README.md

@@ -29,19 +29,19 @@ configs:
     path: data/train-*
 ---
 
-# Evaluation summary
+# Benchmark 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.
+Solutions and tests for all 161 HumanEval tasks are written by an expert olympiad programmer with 6 years of experience in Kotlin, and independently checked by a programmer with 4 years of experience in Kotlin. 
+The tests we implement are eqivalent to the original HumanEval tests for Python.
 
 # 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.
+The benchmark 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.
+When testing models on this benchmark, 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.
+The code for running an example model on the benchmark using the early stopping and post-processing is available below.
 
 ```python
 import json
@@ -151,6 +151,6 @@ print(f'Pass rate: {correct/total}')
 ```
 
 
-# Results:
+# Results
 
 We evaluated multiple coding models using this benchmark, and the results are presented in the table below.