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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""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://huggingface.co/datasets/proan/fashion/resolve/main/images.tar.gz"
class Fashion(datasets.GeneratorBasedBuilder):
"""SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int64"),
"color": datasets.Value("string"),
"description": datasets.Value("string"),
"shop_id": datasets.Value("string"),
"year": datasets.Value("float64"),
"image": datasets.Image(),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://huggingface.co/datasets/proan/fashion",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path = dl_manager.download_and_extract(_URL)
image_iters = dl_manager.iter_archive(path)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images": image_iters}),
]
def _generate_examples(self, images):
idx = 0
#iterate through images
for filepath, image in images:
yield idx, {
"image": {"path": filepath, "bytes": image.read()},
"id": datasets.Value("int64"),
"color": datasets.Value("string"),
"description": datasets.Value("string"),
"shop_id": datasets.Value("string"),
"year": datasets.Value("float64"),
"image": datasets.Image(),
}
idx +=1
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