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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.
#
# Custom DROP dataset that, unlike HF, keeps all question-answer pairs
# even if there are multiple types of answers for the same question.
"""DROP dataset."""
import json
import os
import datasets
_CITATION = """\
@misc{dua2019drop,
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
year={2019},
eprint={1903.00161},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
DROP is a QA dataset which tests comprehensive understanding of paragraphs. In
this crowdsourced, adversarially-created, 96k question-answering benchmark, a
system must resolve multiple references in a question, map them onto a paragraph,
and perform discrete operations over them (such as addition, counting, or sorting).
"""
_HOMEPAGE = "https://allenai.org/data/drop"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
_URLS = {
"drop": "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip",
}
_EMPTY_VALIDATED_ANSWER = [
{
"number": "",
"date": {
"day": "",
"month": "",
"year": "",
},
"spans": [],
"worker_id": "",
"hit_id": "",
}
]
class Drop(datasets.GeneratorBasedBuilder):
"""DROP is a QA dataset which tests comprehensive understanding of paragraphs."""
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="drop", version=VERSION, description="The DROP dataset."
),
]
def _info(self):
features = datasets.Features(
{
"section_id": datasets.Value("string"),
"passage": datasets.Value("string"),
"question": datasets.Value("string"),
"query_id": datasets.Value("string"),
"answer": {
"number": datasets.Value("string"),
"date": {
"day": datasets.Value("string"),
"month": datasets.Value("string"),
"year": datasets.Value("string"),
},
"spans": datasets.features.Sequence(datasets.Value("string")),
"worker_id": datasets.Value("string"),
"hit_id": datasets.Value("string"),
},
"validated_answers": datasets.features.Sequence(
{
"number": datasets.Value("string"),
"date": {
"day": datasets.Value("string"),
"month": datasets.Value("string"),
"year": datasets.Value("string"),
},
"spans": datasets.features.Sequence(datasets.Value("string")),
"worker_id": datasets.Value("string"),
"hit_id": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir, "drop_dataset", "drop_dataset_train.json"
),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir, "drop_dataset", "drop_dataset_dev.json"
),
"split": "validation",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
key = 0
for section_id, example in data.items():
# Each example (passage) has multiple sub-question-answer pairs.
for qa in example["qa_pairs"]:
# Build answer.
answer = qa["answer"]
answer = {
"number": answer["number"],
"date": {
"day": answer["date"].get("day", ""),
"month": answer["date"].get("month", ""),
"year": answer["date"].get("year", ""),
},
"spans": answer["spans"],
"worker_id": answer.get("worker_id", ""),
"hit_id": answer.get("hit_id", ""),
}
validated_answers = []
if "validated_answers" in qa:
for validated_answer in qa["validated_answers"]:
va = {
"number": validated_answer.get("number", ""),
"date": {
"day": validated_answer["date"].get("day", ""),
"month": validated_answer["date"].get("month", ""),
"year": validated_answer["date"].get("year", ""),
},
"spans": validated_answer.get("spans", ""),
"worker_id": validated_answer.get("worker_id", ""),
"hit_id": validated_answer.get("hit_id", ""),
}
validated_answers.append(va)
else:
validated_answers = _EMPTY_VALIDATED_ANSWER
yield key, {
"section_id": section_id,
"passage": example["passage"],
"question": qa["question"],
"query_id": qa["query_id"],
"answer": answer,
"validated_answers": validated_answers,
}
key += 1 |