squad_v1_pt / squad_v1_pt.py
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Update files from the datasets library (from 1.8.0)
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"""TODO(squad_v1_pt): Add a description here."""
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
# TODO(squad_v1_pt): 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},
}
"""
# TODO(squad_v1_pt):
_DESCRIPTION = """\
Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API.
"""
_URL = "https://github.com/nunorc/squad-v1.1-pt/raw/master/"
_URLS = {
"train": _URL + "train-v1.1-pt.json",
"dev": _URL + "dev-v1.1-pt.json",
}
class SquadV1Pt(datasets.GeneratorBasedBuilder):
"""TODO(squad_v1_pt): Short description of my dataset."""
# TODO(squad_v1_pt): Set up version.
VERSION = datasets.Version("1.1.0")
def _info(self):
# TODO(squad_v1_pt): 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://github.com/nunorc/squad-v1.1-pt",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(squad_v1_pt): 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_v1_pt): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for example in data["data"]:
title = example.get("title", "").strip()
for paragraph in example["paragraphs"]:
context = paragraph["context"].strip()
for qa in paragraph["qas"]:
question = qa["question"].strip()
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"].strip() for answer in qa["answers"]]
yield id_, {
"title": title,
"context": context,
"question": question,
"id": id_,
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}