Update README.md

README.md

@@ -6,7 +6,7 @@ size_categories:
 - 10K<n<100K
 task_categories:
 - text-classification
-pretty_name: Java Code Readability Krod
+pretty_name: Java Code Readability Mined & Modified
 tags:
 - readability
 - code
@@ -39,7 +39,7 @@ configs:
     path: data/train-*
 ---
 
-# Java Code Readability Krod
+# Java Code Readability Merged & Modified
 
 This dataset contains **69276 Java code snippets** along with a **readability score**, mined from [Github](https://github.com/) and automatically processed and labelled.
 
@@ -95,7 +95,7 @@ The score is based on a five point Likert scale, with 1 being very unreadable an
 To advance code readability classification, the creation of datasets in this research field is of high importance.
 We provide a new dataset generated with a new approach. 
 Previous datasets for code readability classification are mostly generated by humans manually annotating the readability of code.
-Those datasets are relatively small, with combined only 421 samples.
+[Those datasets](https://huggingface.co/datasets/se2p/code-readability-merged) are relatively small, with combined size of only 421 samples.
 As our approach allows automation, we can provide a different scale of code snippets.
 We share this dataset on Hugging Face to share access and make the ease of usage easy.
 
@@ -106,8 +106,8 @@ The initial source of code snippets are from 100 public GitHub which can be foun
 #### Data Collection and Processing
 
 The Data Collection and Preprocessing for this Hugging Face dataset involved two main steps.
-First, GitHub repositories known for high code quality were downloaded and labeled as highly readable. The extracted methods are labeled with a score of 4.5.
-Second, the code was intentionally manipulated to reduce readability. The resulting code was labelled with a score of 1.5.
+First, GitHub repositories known for high code quality were **mined** and labeled as highly readable. The extracted methods are labeled with a score of 3.68.
+Second, the code was intentionally **modified** to reduce readability. The resulting code was labelled with a score of 3.26.
 This resulted in an automatically generated training dataset for source code readability classification.
 
 #### Who are the source data producers?
@@ -120,11 +120,13 @@ The ratings of the code snippets are automatically assigned. Thus, no personal o
 
 ## Bias, Risks, and Limitations
 
-The assigned labels are not accurate, as they are only an estimation. 
+The assigned labels are not accurate as they are only an average estimate based on a survey.
+The average score for the mined code snippets of the survey was 3.68.
+The average score for the modified code snippets of the survey was 3.26.
 
 ### Recommendations
 
-The dataset should be used to train Java code readability classifiers. We recommend fine-tuning and evaluation on manually labelled data.
+The dataset should be used to train Java code readability classifiers. We recommend fine-tuning and evaluation on [manually labeled data](https://huggingface.co/datasets/se2p/code-readability-merged).
 
 ## Dataset Card Authors
 Lukas Krodinger, [Chair of Software Engineering II](https://www.fim.uni-passau.de/en/chair-for-software-engineering-ii), [University of Passau](https://www.uni-passau.de/en/).