Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Sub-tasks:
document-retrieval
Languages:
code
Size:
100K<n<1M
ArXiv:
Tags:
License:
File size: 5,452 Bytes
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# 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.
"""RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems"""
import gzip
import pickle
import textwrap
import datasets
_CITATION = """\
@misc{liu2023repobench,
title={RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems},
author={Tianyang Liu and Canwen Xu and Julian McAuley},
year={2023},
eprint={2306.03091},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
RepoBench is a dataset that benchmarks repository-level code auto-completion systems.
RepoBench-R denotes RepoBench for Retrieval, which is a sub-task of RepoBench,
aiming to evaluate the ability of code auto-completion systems to retrieve
relevant code snippets for next-line code completion.
"""
_HOMEPAGE = "https://github.com/Leolty/repobench"
_LICENSE = "Apache License 2.0"
_URLs = {
"java_cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cff.gz",
"java_cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cfr.gz",
"python_cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cff.gz",
"python_cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cfr.gz"
}
class RepoBenchR(datasets.GeneratorBasedBuilder):
"""RepoBench"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="python_cff",
description=textwrap.dedent(
"""
cff: cross_file_first -> mask the the line that a cross-file module is first used
"""
)
),
datasets.BuilderConfig(
name="python_cfr",
description=textwrap.dedent(
"""
cfr: cross_file_random -> mask a random line that a cross-file module is used (not the first time)
"""
)
),
datasets.BuilderConfig(
name="java_cff",
description=textwrap.dedent(
"""
cff: cross_file_first -> mask the the line that a cross-file module is first used
"""
)
),
datasets.BuilderConfig(
name="java_cfr",
description=textwrap.dedent(
"""
cfr: cross_file_random -> mask a random line that a cross-file module is used (not the first time)
"""
)
)
]
def _info(self):
features = datasets.Features(
{
"repo_name": datasets.Value("string"),
"file_path": datasets.Value("string"),
"context": [datasets.Value("string")],
"import_statement": datasets.Value("string"),
"code": datasets.Value("string"),
"next_line": datasets.Value("string"),
"gold_snippet_index": datasets.Value("int32")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
config_urls = _URLs[self.config.name]
data_dir = dl_manager.download(config_urls)
return [
datasets.SplitGenerator(
name=datasets.Split("train_easy"),
gen_kwargs={"data_dir": data_dir, "split": "train_easy"},
),
datasets.SplitGenerator(
name=datasets.Split("train_hard"),
gen_kwargs={"data_dir": data_dir, "split": "train_hard"},
),
datasets.SplitGenerator(
name=datasets.Split("test_easy"),
gen_kwargs={"data_dir": data_dir, "split": "test_easy"},
),
datasets.SplitGenerator(
name=datasets.Split("test_hard"),
gen_kwargs={"data_dir": data_dir, "split": "test_hard"},
)
]
def _generate_examples(self, data_dir, split):
""" Yields examples. """
with gzip.open(data_dir, "rb") as f:
data = pickle.load(f)
subset, level = split.split("_")
for i, example in enumerate(data[subset][level]):
yield i, {
"repo_name": example["repo_name"],
"file_path": example["file_path"],
"context": example["context"],
"import_statement": example["import_statement"],
"code": example["code"],
"next_line": example["next_line"],
"gold_snippet_index": example["golden_snippet_index"]
}
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