Datasets:
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"""TODO(drop): Add a description here."""
import json
import os
import datasets
_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}
}
"""
_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 AnswerParsingError(Exception):
pass
class DropDateObject:
"""
Custom parser for date answers in DROP.
A date answer is a dict <date> with at least one of day|month|year.
Example: date == {
'day': '9',
'month': 'March',
'year': '2021'
}
This dict is parsed and flattend to '{day} {month} {year}', not including
blank values.
Example: str(DropDateObject(date)) == '9 March 2021'
"""
def __init__(self, dict_date):
self.year = dict_date.get("year", "")
self.month = dict_date.get("month", "")
self.day = dict_date.get("day", "")
def __iter__(self):
yield from [self.day, self.month, self.year]
def __bool__(self):
return any(self)
def __repr__(self):
return " ".join(self).strip()
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(
{
"section_id": datasets.Value("string"),
"query_id": datasets.Value("string"),
"passage": datasets.Value("string"),
"question": datasets.Value("string"),
"answers_spans": datasets.features.Sequence(
{"spans": datasets.Value("string"), "types": 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"), "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_dev.json"), "split": "validation"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
# TODO(drop): Yields (key, example) tuples from the dataset
with open(filepath, mode="r", encoding="utf-8") as f:
data = json.load(f)
id_ = 0
for i, (section_id, section) in enumerate(data.items()):
for j, qa in enumerate(section["qa_pairs"]):
example = {
"section_id": section_id,
"query_id": qa["query_id"],
"passage": section["passage"],
"question": qa["question"],
}
if split == "train":
answers = [qa["answer"]]
else:
answers = qa["validated_answers"]
try:
example["answers_spans"] = self.build_answers(answers)
yield id_, example
id_ += 1
except AnswerParsingError:
# This is expected for 9 examples of train
# and 1 of validation.
continue
@staticmethod
def _raise(message):
"""
Raise a custom AnswerParsingError, to be sure to only catch our own
errors. Messages are irrelavant for this script, but are written to
ease understanding the code.
"""
raise AnswerParsingError(message)
def build_answers(self, answers):
returned_answers = {
"spans": list(),
"types": list(),
}
for answer in answers:
date = DropDateObject(answer["date"])
if answer["number"] != "":
# sanity checks
if date:
self._raise("This answer is both number and date!")
if len(answer["spans"]):
self._raise("This answer is both number and text!")
returned_answers["spans"].append(answer["number"])
returned_answers["types"].append("number")
elif date:
# sanity check
if len(answer["spans"]):
self._raise("This answer is both date and text!")
returned_answers["spans"].append(str(date))
returned_answers["types"].append("date")
# won't triger if len(answer['spans']) == 0
for span in answer["spans"]:
# sanity checks
if answer["number"] != "":
self._raise("This answer is both text and number!")
if date:
self._raise("This answer is both text and date!")
returned_answers["spans"].append(span)
returned_answers["types"].append("span")
# sanity check
_len = len(returned_answers["spans"])
if not _len:
self._raise("Empty answer.")
if any(len(l) != _len for _, l in returned_answers.items()):
self._raise("Something went wrong while parsing answer values/types")
return returned_answers
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