File size: 5,275 Bytes
be9e317 90946e1 be9e317 ae8fb9b be9e317 90946e1 be9e317 ae8fb9b be9e317 fa48417 be9e317 fa48417 be9e317 fa48417 be9e317 fa48417 be9e317 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
"""TODO(squad_v2): Add a description here."""
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
# TODO(squad_v2): BibTeX citation
_CITATION = """\
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
"""
_DESCRIPTION = """\
combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and abstain from answering.
"""
_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
"train": _URL + "train-v2.0.json",
"dev": _URL + "dev-v2.0.json",
}
class SquadV2Config(datasets.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for SQUADV2.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquadV2Config, self).__init__(**kwargs)
class SquadV2(datasets.GeneratorBasedBuilder):
"""TODO(squad_v2): Short description of my dataset."""
# TODO(squad_v2): Set up version.
BUILDER_CONFIGS = [
SquadV2Config(name="squad_v2", version=datasets.Version("2.0.0"), description="SQuAD plaint text version 2"),
]
def _info(self):
# TODO(squad_v2): 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(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
# 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://rajpurkar.github.io/SQuAD-explorer/",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(squad_v2): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(squad_v2): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
squad = json.load(f)
for example in squad["data"]:
title = example.get("title", "")
for paragraph in example["paragraphs"]:
context = paragraph["context"] # do not strip leading blank spaces GH-2585
for qa in paragraph["qas"]:
question = qa["question"]
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"] for answer in qa["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield id_, {
"title": title,
"context": context,
"question": question,
"id": id_,
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
|