Update README.md

README.md

@@ -1,3 +1,96 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-generation
+tags:
+- code
+- dataset
+size_categories:
+- n<1K
+language:
+- en
+pretty_name: CodeEval
+---
+license: apache-2.0
+---
+# Dataset Card for Object-Oriented Programming
+
+## Dataset Description
+
+- **Repository:** [GitHub Repository](https://github.com/alphadl/OOP-eval)
+- **Paper:** [Object-Oriented Programming Evaluation Benchmark for LLMs](https://arxiv.org/abs/2401.06628)
+
+### Dataset Summary
+
+The OOP benchmark consists of 431 instances, and contains three difficulty levels: Simple-level OOP, Moderate-level OOP, and Difficult-level OOP.
+
+### Supported Tasks and Leaderboards
+
+### Languages
+
+The Object-Oriented Programming problems are written in Python and contain English natural text in comments and docstrings.
+
+## Dataset Structure
+
+```python
+from datasets import load_dataset
+load_dataset("oop")
+
+DatasetDict({
+    test: Dataset({
+        features: ['task_id', 'question', 'canonical_solution', 'test_list', 'test_function', 'entry_point', 'test_matching', 'test_match_function'],
+        num_rows: 431
+    })
+})
+```
+
+### Data Instances
+
+#### OOP benchmark
+```
+{
+    'task_id': 'OOP/0',
+    'question': 'First, write a **WDS** class using the Python language. Then, within the WDS class, create a public function called **without_duplicates** to implement finding the length of the longest substring in a given string **s** that does not contain any duplicate characters.',
+    'test_function': 'def test_run(content1):\n    return WDS().without_duplicates(content1)',
+    'test_list': [
+        'assert candidate("abcabcbb")==3',
+        'assert candidate("bbbbb")==1',
+        'assert candidate("pwwkew")==3'],
+    'entry_point': 'test_run',
+    'test_matching': 'assert candidate([["class WDS", "def without_duplicates"]]) == True',
+    'test_match_function': 'def matching_function(content):\n    def run_match(text):\n        for task in text:\n            if task not in str_content:\n                return False\n        return True\n    len_cont = len(content)\n    if len_cont==1 and run_match(content[0]) == True:\n        return True\n    elif (len_cont==2 and run_match(content[0]) == True) or (len_cont==2 and run_match(content[1]) == True):\n        return True\n    else:\n        return False'
+}
+```
+
+### Data Fields
+
+- `task_id`: identifier for the data sample
+- `question`: description of programming task
+- `test_function`: run function for the test
+- 'test_list': list of tests to verify solution
+- `entry_point`: entry point for test
+- 'test_matching': list of tests to verify solution
+- 'test_match_function': matching function for the test
+
+### Data Splits
+
+The OOP dataset only consists of a test split with 431 samples.
+
+## Dataset Creation
+
+See section 3.2 of original [paper](https://arxiv.org/abs/2401.06628).
+
+### Citation Information
+```
+@inproceedings{wang2024oop,
+      title={OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models}, 
+      author={Shuai Wang and Liang Ding and Li Shen and Yong Luo and Bo Du and Dacheng Tao},
+      year={2024},
+      booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
+      url={https://arxiv.org/abs/2401.06628}, 
+}
+```
+
+### Contributions
+
+Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.