Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
code
Size:
100K - 1M
License:
# coding=utf-8 | |
# Copyright 2022 CodeQueries Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
"""The CodeQueries benchmark.""" | |
import json | |
import os | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CODEQUERIES_CITATION = """\ | |
@article{codequeries2022, | |
title={Learning to Answer Semantic Queries over Code}, | |
author={A, B, C, D, E, F}, | |
journal={arXiv preprint arXiv:<.>}, | |
year={2022} | |
} | |
""" | |
_IDEAL_DESCRIPTION = """\ | |
CodeQueries Ideal setup. | |
""" | |
_PREFIX_DESCRIPTION = """\ | |
CodeQueries Prefix setup.""" | |
_SLIDING_WINDOW_DESCRIPTION = """\ | |
CodeQueries Sliding window setup.""" | |
_FILE_IDEAL_DESCRIPTION = """\ | |
CodeQueries File level Ideal setup.""" | |
_TWOSTEP_DESCRIPTION = """\ | |
CodeQueries Twostep setup.""" | |
class CodequeriesConfig(datasets.BuilderConfig): | |
"""BuilderConfig for Codequeries.""" | |
def __init__(self, features, citation, url, **kwargs): | |
"""BuilderConfig for Codequeries. | |
Args: | |
features: `list[string]`, list of the features that will appear in the | |
feature dict. Should not include "label". | |
citation: `string`, citation for the data set. | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
# Version history: | |
# 1.0.0: Initial version. | |
super(CodequeriesConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) | |
self.features = features | |
self.citation = citation | |
self.url = url | |
class Codequeries(datasets.GeneratorBasedBuilder): | |
"""The Codequeries benchmark.""" | |
BUILDER_CONFIGS = [ | |
CodequeriesConfig( | |
name="ideal", | |
description=_IDEAL_DESCRIPTION, | |
features=["query_name", "context_blocks", "answer_spans", | |
"supporting_fact_spans", "code_file_path", "example_type", | |
"subtokenized_input_sequence", "label_sequence"], | |
citation=_CODEQUERIES_CITATION, | |
# homepage="", | |
# data_url="", | |
url="", | |
), | |
# CodequeriesConfig( | |
# name="prefix", | |
# description=_PREFIX_DESCRIPTION, | |
# features=["query_name", "context_blocks", "answer_spans", | |
# "supporting_fact_spans", "code_file_path", "example_type", | |
# "subtokenized_input_sequence", "label_sequence"], | |
# citation=_CODEQUERIES_CITATION, | |
# url="", | |
# ), | |
# CodequeriesConfig( | |
# name="sliding_window", | |
# description=_SLIDING_WINDOW_DESCRIPTION, | |
# features=["query_name", "context_blocks", "answer_spans", | |
# "supporting_fact_spans", "code_file_path", "example_type", | |
# "subtokenized_input_sequence", "label_sequence"], | |
# citation=_CODEQUERIES_CITATION, | |
# url="", | |
# ), | |
CodequeriesConfig( | |
name="file_ideal", | |
description=_FILE_IDEAL_DESCRIPTION, | |
features=["query_name", "context_blocks", "answer_spans", | |
"supporting_fact_spans", "code_file_path", "example_type", | |
"subtokenized_input_sequence", "label_sequence"], | |
citation=_CODEQUERIES_CITATION, | |
url="", | |
), | |
# CodequeriesConfig( | |
# name="twostep", | |
# description=_TWOSTEP_DESCRIPTION, | |
# features=["query_name", "context_blocks", "answer_spans", | |
# "supporting_fact_spans", "code_file_path", "example_type", | |
# "subtokenized_input_sequence", "label_sequence"], | |
# citation=_CODEQUERIES_CITATION, | |
# url="", | |
# ), | |
] | |
# DEFAULT_CONFIG_NAME = "ideal" | |
def _info(self): | |
# features = {feature: datasets.Value("string") for feature in self.config.features} | |
features = {} | |
features["query_name"] = datasets.Value("string") | |
features["context_blocks"] = datasets.features.Sequence( | |
{ | |
"content": datasets.Value("string"), | |
"metadata": datasets.Value("string"), | |
"header": datasets.Value("string") | |
} | |
) | |
features["answer_spans"] = datasets.features.Sequence( | |
{ | |
"answer_span": { | |
'span': datasets.Value("string"), | |
'start_line': datasets.Value("int32"), | |
'start_column': datasets.Value("int32"), | |
'end_line': datasets.Value("int32"), | |
'end_column': datasets.Value("int32") | |
} | |
} | |
) | |
features["supporting_fact_spans"] = datasets.features.Sequence( | |
{ | |
"supporting_fact_span": { | |
'span': datasets.Value("string"), | |
'start_line': datasets.Value("int32"), | |
'start_column': datasets.Value("int32"), | |
'end_line': datasets.Value("int32"), | |
'end_column': datasets.Value("int32") | |
} | |
} | |
) | |
features["code_file_path"] = datasets.Value("string") | |
features["example_type"] = datasets.Value("int32") | |
features["subtokenized_input_sequence"] = datasets.features.Sequence(datasets.Value("string")) | |
features["label_sequence"] = datasets.features.Sequence(datasets.Value("int32")) | |
return datasets.DatasetInfo( | |
description=self.config.description, | |
features=datasets.Features(features), | |
homepage=self.config.url, | |
citation=_CODEQUERIES_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
dl_dir = "" | |
if self.config.name in ["prefix", "sliding_window", "file_ideal", "twostep"]: | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": os.path.join(dl_dir, self.config.name + "_test.json"), | |
"split": datasets.Split.TEST, | |
}, | |
), | |
] | |
else: | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": os.path.join(dl_dir, self.config.name + "_train.json"), | |
"split": datasets.Split.TRAIN, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepath": os.path.join(dl_dir, self.config.name + "_val.json"), | |
"split": datasets.Split.VALIDATION, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": os.path.join(dl_dir, self.config.name + "_test.json"), | |
"split": datasets.Split.TEST, | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
if self.config.name in ["prefix", "sliding_window", "file_ideal", "twostep"]: | |
assert split == datasets.Split.TEST | |
logger.info("generating examples from = %s", filepath) | |
with open(filepath, encoding="utf-8") as f: | |
key = 0 | |
for line in f: | |
row = json.loads(line) | |
instance_key = str(key) + "_" + row["query_name"] + "_" + row["code_file_path"] | |
yield instance_key, { | |
"query_name": row["query_name"], | |
"context_blocks": row["context_blocks"], | |
"answer_spans": row["answer_spans"], | |
"supporting_fact_spans": row["supporting_fact_spans"], | |
"code_file_path": row["code_file_path"], | |
"example_type": row["example_type"], | |
"subtokenized_input_sequence": row["subtokenized_input_sequence"], | |
"label_sequence": row["label_sequence"], | |
} | |
key += 1 | |