Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
< 1K
ArXiv:
Tags:
code-generation
License:
license: mit | |
language: | |
- en | |
size_categories: | |
- n<1K | |
tags: | |
- code-generation | |
task_categories: | |
- text2text-generation | |
pretty_name: ClassEval | |
configs: | |
- config_name: default | |
data_files: | |
- split: test | |
path: "ClassEval_data.json" | |
# Dataset Card for FudanSELab ClassEval | |
## Dataset Description | |
- **Repository:** [GitHub Repository](https://github.com/FudanSELab/ClassEval) | |
- **Paper:** [ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation](https://arxiv.org/abs/2308.01861) | |
### Dataset Summary | |
We manually build ClassEval of 100 class-level Python coding tasks, consists of 100 classes and 412 methods, and average 33.1 test cases per class. | |
For 100 class-level tasks, diversity is maintained by encompassing these tasks over a wide spectrum of topics, including Management Systems, Data Formatting, Mathematical Operations, Game Development, File Handing, Database Operations and Natural Language Processing. | |
For 412 methods, they have been constructed with diverse dependencies, including (i) Library Dependency, where the methods rely on specific external libraries; (ii) Field Dependency, in which the methods are contingent on class instance variables, or fields; (iii) Method Dependency, where the methods are dependent on other methods within the same class; and (iv) Standalone, wherein the methods operate independently without reliance on fields, other methods, or external libraries. | |
### Supported Tasks and Leaderboards | |
[More Information Needed] | |
### Languages | |
The programming language is Python. The natural language used in the comments and docstrings is English. | |
## Dataset Structure | |
```python | |
from datasets import load_dataset | |
dataset = load_dataset("FudanSELab/ClassEval") | |
DatasetDict({ | |
test: Dataset({ | |
features: ['task_id', 'skeleton', 'test', 'solution_code', 'import_statement', 'class_description', 'methods_info', | |
'class_name', 'test_classes', 'class_constructor', 'fields'], | |
num_rows: 100 | |
}) | |
}) | |
``` | |
### Data Instances | |
[More Information Needed] | |
### Data Fields | |
The specific data fields for each task are delineated as follows: | |
* task_id: the unique identifier for each task. | |
* skeleton: the class skeleton, including all input descriptions in our class-level coding tasks. | |
* test: all test cases for the whole class. | |
* solution_code: the ground-truth class-level code for each task. | |
More fine-grained class-level information from the class skeleton, including: | |
* import_statement: the import statements for each task. | |
* class_name: the name of the class. | |
* class_description: a concise description of the purpose and functionality of the class. | |
* class_constructor: the whole constructor of the class. | |
* fields: the fields defined in the class_constructor. | |
Detailed information for each method in the "methods_info" field, including: | |
* method_name: the method signature. | |
* method_input: the method contract design, including all input descriptions in the method. | |
* test_code: the test cases for the method. | |
* solution_code: the ground-truth method-level code. | |
* dependencies: the dependency information of the method. | |
### Data Splits | |
The dataset only consists of a test split with 100 samples. | |
## Dataset Creation | |
### Curation Rationale | |
[More Information Needed] | |
### Source Data | |
#### Initial Data Collection and Normalization | |
[More Information Needed] | |
#### Who are the source language producers? | |
[More Information Needed] | |
### Annotations | |
#### Annotation process | |
[More Information Needed] | |
#### Who are the annotators? | |
[More Information Needed] | |
### Personal and Sensitive Information | |
[More Information Needed] | |
## Considerations for Using the Data | |
### Social Impact of Dataset | |
[More Information Needed] | |
### Discussion of Biases | |
[More Information Needed] | |
### Other Known Limitations | |
[More Information Needed] | |
## Additional Information | |
### Dataset Curators | |
[More Information Needed] | |
### Licensing Information | |
[More Information Needed] | |
### Citation Information | |
``` | |
@misc{du2023classeval, | |
title={ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation}, | |
author={Xueying Du and Mingwei Liu and Kaixin Wang and Hanlin Wang and Junwei Liu and Yixuan Chen and Jiayi Feng and Chaofeng Sha and Xin Peng and Yiling Lou}, | |
year={2023}, | |
eprint={2308.01861}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
``` | |
### Contributions | |
[More Information Needed] |