|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""SQUAD: The Stanford Question Answering Dataset.""" |
|
|
|
|
|
import json |
|
|
|
import datasets |
|
from datasets.tasks import QuestionAnsweringExtractive |
|
|
|
|
|
logger = datasets.logging.get_logger(__name__) |
|
|
|
|
|
_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 = """\ |
|
Stanford Question Answering Dataset (SQuAD) is a reading comprehension \ |
|
dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \ |
|
articles, where the answer to every question is a segment of text, or span, \ |
|
from the corresponding reading passage, or the question might be unanswerable. |
|
""" |
|
|
|
_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" |
|
_URLS = { |
|
"train": _URL + "train-v1.1.json", |
|
"dev": "https://raw.githubusercontent.com/Yulong-W/squad/main/dev-v1.1-excerpted.json", |
|
} |
|
|
|
|
|
class SquadConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for SQUAD.""" |
|
|
|
def __init__(self, **kwargs): |
|
"""BuilderConfig for SQUAD. |
|
|
|
Args: |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(SquadConfig, self).__init__(**kwargs) |
|
|
|
|
|
class Squad(datasets.GeneratorBasedBuilder): |
|
"""SQUAD: The Stanford Question Answering Dataset. Version 1.1.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
SquadConfig( |
|
name="plain_text", |
|
version=datasets.Version("1.0.0", ""), |
|
description="Plain text", |
|
), |
|
] |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
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"), |
|
} |
|
), |
|
} |
|
), |
|
|
|
|
|
supervised_keys=None, |
|
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): |
|
downloaded_files = dl_manager.download_and_extract(_URLS) |
|
|
|
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): |
|
"""This function returns the examples in the raw (text) form.""" |
|
logger.info("generating examples from = %s", filepath) |
|
key = 0 |
|
with open(filepath, encoding="utf-8") as f: |
|
squad = json.load(f) |
|
for article in squad["data"]: |
|
title = article.get("title", "") |
|
for paragraph in article["paragraphs"]: |
|
context = paragraph["context"] |
|
for qa in paragraph["qas"]: |
|
answer_starts = [answer["answer_start"] for answer in qa["answers"]] |
|
answers = [answer["text"] for answer in qa["answers"]] |
|
|
|
|
|
yield key, { |
|
"title": title, |
|
"context": context, |
|
"question": qa["question"], |
|
"id": qa["id"], |
|
"answers": { |
|
"answer_start": answer_starts, |
|
"text": answers, |
|
}, |
|
} |
|
key += 1 |
|
|