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--- |
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license: mit |
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task_categories: |
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- text-generation |
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tags: |
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- code |
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- dataset |
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size_categories: |
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- n<1K |
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language: |
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- en |
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pretty_name: CodeEval |
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--- |
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license: apache-2.0 |
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--- |
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# Dataset Card for Object-Oriented Programming |
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## Dataset Description |
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- **Repository:** [GitHub Repository](https://github.com/alphadl/OOP-eval) |
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- **Paper:** [Object-Oriented Programming Evaluation Benchmark for LLMs](https://arxiv.org/abs/2401.06628) |
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### Dataset Summary |
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The OOP benchmark consists of 431 instances, and contains three difficulty levels: Simple-level OOP, Moderate-level OOP, and Difficult-level OOP. |
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### Supported Tasks and Leaderboards |
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### Languages |
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The Object-Oriented Programming problems are written in Python and contain English natural text in comments and docstrings. |
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## Dataset Structure |
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```python |
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from datasets import load_dataset |
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load_dataset("oop") |
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DatasetDict({ |
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test: Dataset({ |
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features: ['task_id', 'question', 'canonical_solution', 'test_list', 'test_function', 'entry_point', 'test_matching', 'test_match_function'], |
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num_rows: 431 |
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}) |
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}) |
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``` |
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### Data Instances |
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#### OOP benchmark |
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``` |
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{ |
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'task_id': 'OOP/0', |
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'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.', |
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'test_function': 'def test_run(content1):\n return WDS().without_duplicates(content1)', |
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'test_list': [ |
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'assert candidate("abcabcbb")==3', |
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'assert candidate("bbbbb")==1', |
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'assert candidate("pwwkew")==3'], |
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'entry_point': 'test_run', |
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'test_matching': 'assert candidate([["class WDS", "def without_duplicates"]]) == True', |
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'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' |
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} |
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``` |
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### Data Fields |
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- `task_id`: identifier for the data sample |
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- `question`: description of programming task |
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- `test_function`: run function for the test |
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- 'test_list': list of tests to verify solution |
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- `entry_point`: entry point for test |
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- 'test_matching': list of tests to verify solution |
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- 'test_match_function': matching function for the test |
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### Data Splits |
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The OOP dataset only consists of a test split with 431 samples. |
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## Dataset Creation |
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See section 3.2 of original [paper](https://arxiv.org/abs/2401.06628). |
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### Citation Information |
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``` |
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@inproceedings{wang2024oop, |
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title={OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models}, |
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author={Shuai Wang and Liang Ding and Li Shen and Yong Luo and Bo Du and Dacheng Tao}, |
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year={2024}, |
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booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, |
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url={https://arxiv.org/abs/2401.06628}, |
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} |
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``` |
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### Contributions |
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Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset. |
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