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Datasets:
lara-martin
/
Scifi_TV_Shows

Tasks:
Text Generation
Text2Text Generation
Languages: English
Size Categories: 100K<n<1M
Tags: story storytelling creative summaries TV scifi
License:
Dataset card Files Community
3
Scifi_TV_Shows / Scifi_TV_Shows.py
Lara Martin
working data processing
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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
import os
_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.'
_URLS = {'Scifi_TV_Shows': "https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/resolve/main/scifiTVshows.zip"}
class Scifi_TV_Shows(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=datasets.Version('1.1.0'),
name="Scifi_TV_Shows",
description=f'Science fiction TV show plot summaries.',
)
]
def _info(self):
features = datasets.Features({
'story_num': datasets.Value('int16'),
'story_line': datasets.Value('int16'),
'event': datasets.Sequence(datasets.Value('string')),
'gen_event': datasets.Sequence(datasets.Value('string')),
'sent': datasets.Value('string'),
'gen_sent': datasets.Value('string'),
'entities': datasets.Value('string'),
})
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):
my_urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
train_filepath = os.path.join(data_dir, "scifi-train.txt")
test_filepath = os.path.join(data_dir, "scifi-test.txt")
val_filepath = os.path.join(data_dir, "scifi-val.txt")
return[
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
'filepath': train_filepath,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
'filepath': test_filepath,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
'filepath': val_filepath,
},
),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
for id_, line in enumerate(f.readlines()):
line = line.strip()
story_num, line_num, event, gen_event, sent, gen_sent, entities = line.split("|||")
yield id_, {
'story_num': story_num,
'story_line': line_num,
'event': eval(event),
'gen_event': eval(gen_event),
'sent': sent,
'gen_sent': gen_sent,
'entities': entities,
}