Update README.md

README.md

@@ -1,4 +1,23 @@
 ---
+language:
+- en
+license: unknown
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-classification
+pretty_name: Java Code Readability Krod
+tags:
+- readability
+- code
+- source code
+- code readability
+- Java
+features:
+- name: code_snippet
+  dtype: string
+- name: score
+  dtype: float
 dataset_info:
   features:
   - name: code_snippet
@@ -17,6 +36,75 @@ configs:
   - split: train
     path: data/train-*
 ---
-# Dataset Card for "krod"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Java Code Readability Krod
+
+This dataset contains **63460 Java code snippets** along with a **readability score**, mined from [Github](https://github.com/) and automatically processed and labelled.
+
+You can download the dataset using Hugging Face:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("LuKrO/krod")
+```
+
+The snippets are **not** split into train and test (and validation) set. Thus, the whole dataset is in the **train** set:
+```python
+ds = ds['train']
+ds_as_list = ds.to_list() # Convert the dataset to whatever format suits you best
+
+```
+
+The dataset is structured as follows:
+
+```json
+{
+  "code_snippet": ...,  # Java source code snippet
+  "score": ...          # Readability score
+}
+```
+
+The main goal of this repository is to train code **readability classifiers for Java source code**. 
+
+## Dataset Details
+
+### Dataset Description
+
+- **Curated by:** Krodinger Lukas
+- **Shared by:** Krodinger Lukas
+- **Language(s) (NLP):** Java
+- **License:** Unknown
+
+## Uses
+
+The dataset can be used for training Java code readability classifiers.
+
+## Dataset Structure
+
+Each entry of the dataset consists of a **code_snippet** and a **score**. 
+The code_snippet (string) is the code snippet that was downloaded from GitHub. Each snippet has a readability score assigned.
+The score is based on a five point Likert scale, with 1 being very unreadable and 5 being very readable.
+
+## Dataset Creation
+
+### Curation Rationale
+
+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.
+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.
+
+### Source Data
+
+The initial source of code snippets are from various public GitHub repositories:
+TODO: Add repos
+
+
+#### 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.
+This resulted in an automatically generated training dataset for source code readability classification.