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# Copyright 2020 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.
"""ScienceQA loading script."""
import json
from pathlib import Path
import os
import datasets
_CITATION = """\
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
"""
_DESCRIPTION = """\
This is the ScienceQA dataset.
"""
_HOMEPAGE = "https://scienceqa.github.io/"
_LICENSE = "CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)"
_URLS = {
"pid_splits": "https://drive.google.com/uc?id=1OXlNBuW74dsrwYZIpQMshFqxkjcMPPgV&export=download",
"problems": "https://drive.google.com/uc?id=1nJ86OLnF2C6eDoi5UOAdTAS5Duc0wuTl&export=download",
"train": "https://drive.google.com/uc?id=1swX4Eei1ZqrXRvM-JAZxN6QVwcBLPHV8&export=download",
"val": "https://drive.google.com/uc?id=1ijThWZc1tsoqGrOCWhYYj1HUJ48Hl8Zz&export=download",
"test": "https://drive.google.com/uc?id=1eyjFaHxbvEJZzdZILn3vnTihBNDmKcIj&export=download",
}
_SUB_FOLDER_OR_FILE_NAME = {
"pid_splits": "pid_splits.json",
"problems": "problems.json",
"train": "train",
"val": "val",
"test": "test",
}
class ScienceQADataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"question": datasets.Value("string"),
"choices": datasets.Sequence(datasets.Value("string")),
"answer": datasets.Value("int32"),
"hint": datasets.Value("string"),
"image": datasets.Image(),
"task": datasets.Value("string"),
"grade": datasets.Value("string"),
"subject": datasets.Value("string"),
"topic": datasets.Value("string"),
"category": datasets.Value("string"),
"skill": datasets.Value("string"),
"lecture": datasets.Value("string"),
"solution": datasets.Value("string"),
"split": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URLS)
gen_kwargs = {}
for split_name in ["train", "val", "test"]:
gen_kwargs_per_split = {}
gen_kwargs_per_split["pid_splits_path"] = Path(data_dir["pid_splits"]) / _SUB_FOLDER_OR_FILE_NAME["pid_splits"]
gen_kwargs_per_split["problems_path"] = Path(data_dir["problems"]) / _SUB_FOLDER_OR_FILE_NAME["problems"]
gen_kwargs_per_split["images_path"] = Path(data_dir[split_name]) / _SUB_FOLDER_OR_FILE_NAME[split_name]
gen_kwargs_per_split["split_name"] = split_name
gen_kwargs[split_name] = gen_kwargs_per_split
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs=gen_kwargs["train"],
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs=gen_kwargs["val"],
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs=gen_kwargs["test"],
),
]
def _generate_examples(self, pid_splits_path, problems_path, images_path, split_name):
#pid_splits = json.load(open(pid_splits_path, "r"))
#problems = json.load(open(problems_path, "r"))
#pid_splits = {"train": ["1"], "val": ["7"], "test": ["5"]}
#problems = {"1": {"image": "image.png"}, "5": {"image": "image.png"}, "7": {"image": "image.png"}}
pid_splits = json.load(open("/gpfswork/rech/cnw/urd43gx/pid_splits.json", "r"))
problems = json.load(open("/gpfswork/rech/cnw/urd43gx/problems.json", "r"))
for idx, key in enumerate(pid_splits[split_name]):
example = problems[key]
if example["image"]:
example["image"] = os.path.join(images_path, key, example["image"])
yield idx, example
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