Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
code
Size:
< 1K
License:
File size: 4,768 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""HumanEval-X dataset."""
import json
import datasets
_DESCRIPTION = """
HumanEval-X is a benchmark for the evaluation of the multilingual ability of code generative models. \
It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks.
"""
_HOMEPAGE = "https://github.com/THUDM/CodeGeeX"
def get_url(name):
urls = {"test": f"data/{name}/data/humaneval.jsonl"}
return urls
def split_generator(dl_manager, name):
downloaded_files = get_url(name)
downloaded_files = dl_manager.download(get_url(name))
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
class HumanEvalXConfig(datasets.BuilderConfig):
"""BuilderConfig """
def __init__(self, name, description, features, **kwargs):
super(HumanEvalXConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
self.name = name
self.description = description
self.features = features
class HumanEvalX(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
HumanEvalXConfig(
name="python",
description="Python HumanEval",
features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
),
HumanEvalXConfig(
name="cpp",
description="C++ HumanEval",
features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
),
HumanEvalXConfig(
name="go",
description="Go HumanEval",
features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
),
HumanEvalXConfig(
name="java",
description="Java HumanEval",
features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
),
HumanEvalXConfig(
name="js",
description="JavaScript HumanEval",
features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
),
]
DEFAULT_CONFIG_NAME = "python"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({"task_id": datasets.Value("string"),
"prompt": datasets.Value("string"),
"declaration": datasets.Value("string"),
"canonical_solution": datasets.Value("string"),
"test": datasets.Value("string"),
"example_test": datasets.Value("string"),
}),
homepage=_HOMEPAGE,
)
def _split_generators(self, dl_manager):
if self.config.name == "python":
return split_generator(dl_manager, self.config.name)
elif self.config.name == "cpp":
return split_generator(dl_manager, self.config.name)
elif self.config.name == "go":
return split_generator(dl_manager, self.config.name)
elif self.config.name == "java":
return split_generator(dl_manager, self.config.name)
elif self.config.name == "js":
return split_generator(dl_manager, self.config.name)
def _generate_examples(self, filepath):
key = 0
with open(filepath) as f:
for line in f:
row = json.loads(line)
key += 1
yield key, {
"task_id": row["task_id"],
"prompt": row["prompt"],
"declaration": row["declaration"],
"canonical_solution": row["canonical_solution"],
"test": row["test"],
"example_test": row["example_test"],
}
key += 1 |