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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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
import json
import datasets
_CITATION = '''
@inproceedings{Ammanabrolu2020AAAI,
title={Story Realization: Expanding Plot Events into Sentences},
author={Prithviraj Ammanabrolu and Ethan Tien and Wesley Cheung and Zhaochen Luo and William Ma and Lara J. Martin and Mark O. Riedl},
journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020},
volume={34},
number={05},
url={https://ojs.aaai.org//index.php/AAAI/article/view/6232}
}
'''
_DESCRIPTION = 'Loading script for the science fiction TV show plot dataset.'
URL = 'https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/'
_URLS = {
'test':'Test-Train-Val/all-sci-fi-data-test.txt',
'train':'Test-Train-Val/all-sci-fi-data-train.txt',
'val':'Test-Train-Val/all-sci-fi-data-val.txt',
'all':'all-sci-fi-data.txt',
}
_INPUT_OUTPUT = ["all-sci-fi-data-test_input.txt", "all-sci-fi-data-test_output.txt", "all-sci-fi-data-train_input.txt", "all-sci-fi-data-train_output.txt", "all-sci-fi-data-val_input.txt", "all-sci-fi-data-val_output.txt"]
class ScifiTV(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=datasets.Version('1.1.0'),
name=k,
description=f'Science fiction TV show plot summaries.'
) for k in _URLS.keys()
]
def _info(self):
features = datasets.Features({
'event': datasets.Value('list'),
'gen_event': datasets.Value('list'),
'sent': datasets.Value('string'),
'gen_sent': datasets.Value('string'),
'story_num': datasets.Value('int'),
'entities': datasets.Value('dict')
})
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
# Homepage of the dataset for documentation
homepage='https://github.com/rajammanabrolu/StoryRealization',
# License for the dataset if available
license='The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/',
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download(_URLS)
splits = []
for datatype in _URLS.keys():
splits.append(
datasets.SplitGenerator(
name=datatype,
gen_kwargs={
'filepath': downloaded_files[datatype],
"files": dl_manager.iter_archive(archive),
},
)
)
return splits
def _generate_examples(self, filepath):
story_count = 0
with open(filepath, encoding="utf-8") as f:
story = []
for id_, line in enumerate(f.readlines()):
line = line.strip()
if "%%%%%%" in line:
for l in story:
event, gen_event, sent, gen_sent = l.split("|||")
line = line.replace("%%%%%%%%%%%%%%%%%", "")
line = line.replace("defaultdict(<type 'list'>, ", "")[:-1]
entities = eval(line)
yield id_, {
'event': eval(event),
'gen_event': eval(gen_event),
'sent': sent,
'gen_sent': gen_sent,
'story_num': story_count,
'entities': entities,
}
story = []
story_count+=1
elif "<EOS>" in line:
continue
else:
story.append(line)
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