Datasets:
poem_sentiment

	# 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
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""Poem Sentiment: A sentiment dataset of poem verses"""


	import datasets


	_CITATION = """\
	@misc{sheng2020investigating,
	title={Investigating Societal Biases in a Poetry Composition System},
	author={Emily Sheng and David Uthus},
	year={2020},
	eprint={2011.02686},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. \
	This dataset can be used for tasks such as sentiment classification or style transfer for poems.
	"""


	_HOMEPAGE = "https://github.com/google-research-datasets/poem-sentiment"

	_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/poem-sentiment/master/data/"
	_URLS = {
	"train": f"{_BASE_URL}/train.tsv",
	"dev": f"{_BASE_URL}/dev.tsv",
	"test": f"{_BASE_URL}/test.tsv",
	}
	_LABEL_MAPPING = {-1: 0, 0: 2, 1: 1, 2: 3}


	class PoemSentiment(datasets.GeneratorBasedBuilder):
	"""Poem Sentiment: A sentiment dataset of poem verses"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"verse_text": datasets.Value("string"),
	"label": datasets.ClassLabel(names=["negative", "positive", "no_impact", "mixed"]),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download(_URLS)
	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"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	lines = f.readlines()
	for line in lines:
	fields = line.strip().split("\t")
	idx, verse_text, label = fields
	label = _LABEL_MAPPING[int(label)]
	yield int(idx), {"id": int(idx), "verse_text": verse_text, "label": label}