import json import datasets _DESCRIPTION = """\ FudanSELab ClassEval """ _URL = "ClassEval_data.json" _CITATION = """\ @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} }""" _HOMEPAGE = "https://github.com/FudanSELab/ClassEval" _LICENSE = "MIT" class ClassEval(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="class_eval", version=datasets.Version("1.0.0"), description=_DESCRIPTION, ) ] def _info(self): method_feature = datasets.Features( { "method_name": datasets.Value("string"), "method_description": datasets.Value("string"), "test_class": datasets.Value("string"), "test_code": datasets.Value("string"), "solution_code": datasets.Value("string"), "dependencies": { "Standalone": datasets.Value("bool"), "lib_dependencies": datasets.Sequence(datasets.Value("string")), "field_dependencies": datasets.Sequence(datasets.Value("string")), "method_dependencies": datasets.Sequence(datasets.Value("string")), } } ) features = datasets.Features( { "task_id": datasets.Value("string"), "skeleton": datasets.Value("string"), "test": datasets.Value("string"), "solution_code": datasets.Value("string"), "import_statement": datasets.Sequence(datasets.Value("string")), "class_description": datasets.Value("string"), "methods_info": [method_feature], "class_name": datasets.Value("string"), "test_classes": datasets.Sequence(datasets.Value("string")), "class_constructor": datasets.Value("string"), "fields": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" data_dir = dl_manager.download(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir, }, ) ] def _generate_examples(self, filepath): key = 0 with open(filepath, encoding = 'utf-8') as f: cont = json.load(f) for row in cont: yield key, { "task_id": row["task_id"], "skeleton": row["skeleton"], "test": row["test"], "solution_code": row["solution_code"], "import_statement": row["import_statement"], "class_description": row["class_description"], "methods_info": row["methods_info"], "class_name": row["class_name"], "test_classes": row["test_classes"], "class_constructor": row["class_constructor"], "fields": row["fields"], } key += 1