Datasets:
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"""TODO(drop): Add a description here."""
from __future__ import absolute_import, division, print_function
import json
import os
import datasets
# TODO(drop): BibTeX citation
_CITATION = """\
@inproceedings{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
}
"""
# TODO(drop):
_DESCRIPTION = """\
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or
sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
necessary for prior datasets.
"""
_URl = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
class Drop(datasets.GeneratorBasedBuilder):
"""TODO(drop): Short description of my dataset."""
# TODO(drop): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(drop): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"passage": datasets.Value("string"),
"question": datasets.Value("string"),
"answers_spans": datasets.features.Sequence({"spans": datasets.Value("string")})
# These are the features of your dataset like images, labels ...
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://allennlp.org/drop",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(drop): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_dir = dl_manager.download_and_extract(_URl)
data_dir = os.path.join(dl_dir, "drop_dataset")
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_train.json")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_dev.json")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(drop): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for i, key in enumerate(data):
example = data[key]
qa_pairs = example["qa_pairs"]
for j, qa in enumerate(qa_pairs):
question = qa["question"]
answers = qa["answer"]["spans"]
yield str(i) + "_" + str(j), {
"passage": example["passage"],
"question": question,
"answers_spans": {"spans": answers},
}
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