Datasets:

tasksource
/

crowdflower

	# 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
	"""Crowdflower datasets"""

	from __future__ import absolute_import, division, print_function

	import csv
	import os
	import textwrap

	import six

	import datasets


	_crowdflower_CITATION = r"""
	@inproceedings{van2012designing,
	title={Designing a scalable crowdsourcing platform},
	author={Van Pelt, Chris and Sorokin, Alex},
	booktitle={Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data},
	pages={765--766},
	year={2012}
	}
	"""

	_crowdflower_DESCRIPTION = """
	Collection of crowdflower classification datasets
	"""

	DATA_URL = "https://www.dropbox.com/s/ldrcdsv8d9qiwg0/crowdflower.zip?dl=1"

	TASK_TO_LABELS = {'airline-sentiment': ['neutral', 'positive', 'negative'],
	'corporate-messaging': ['Information', 'Action', 'Exclude', 'Dialogue'],
	'economic-news': ['not sure', 'yes', 'no'],
	'political-media-audience': ['constituency', 'national'],
	'political-media-bias': ['partisan', 'neutral'],
	'political-media-message': ['information',
	'support',
	'policy',
	'constituency',
	'personal',
	'other',
	'media',
	'mobilization',
	'attack'],
	'sentiment_nuclear_power': ['Neutral / author is just sharing information',
	'Negative',
	'Tweet NOT related to nuclear energy',
	'Positive'],
	'text_emotion': ['sadness',
	'empty',
	'relief',
	'hate',
	'worry',
	'enthusiasm',
	'happiness',
	'neutral',
	'love',
	'fun',
	'anger',
	'surprise',
	'boredom'],
	'tweet_global_warming': ['Yes', 'No']}

	def get_labels(task):
	return TASK_TO_LABELS[task]

	class crowdflowerConfig(datasets.BuilderConfig):
	"""BuilderConfig for crowdflower."""

	def __init__(
	self,
	text_features,
	label_classes=None,
	process_label=lambda x: x,
	**kwargs,
	):
	"""BuilderConfig for crowdflower.
	Args:
	text_features: `dict[string, string]`, map from the name of the feature
	dict for each text field to the name of the column in the tsv file
	label_column: `string`, name of the column in the tsv file corresponding
	to the label
	data_url: `string`, url to download the zip file from
	data_dir: `string`, the path to the folder containing the tsv files in the
	downloaded zip
	citation: `string`, citation for the data set
	url: `string`, url for information about the data set
	label_classes: `list[string]`, the list of classes if the label is
	categorical. If not provided, then the label will be of type
	`datasets.Value('float32')`.
	process_label: `Function[string, any]`, function taking in the raw value
	of the label and processing it to the form required by the label feature
	**kwargs: keyword arguments forwarded to super.
	"""

	super(crowdflowerConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)

	self.text_features = text_features
	self.label_column = "label"
	self.label_classes = get_labels(self.name)
	self.data_url = DATA_URL
	self.data_dir = os.path.join("crowdflower", self.name)
	self.citation = textwrap.dedent(_crowdflower_CITATION)
	def process_label(x):
	x=str(x)
	if x=="Y":
	return "Yes"
	if x=="N":
	return "No"
	return x
	self.process_label = process_label
	self.description = ""
	self.url = ""


	class crowdflower(datasets.GeneratorBasedBuilder):

	"""The General Language Understanding Evaluation (crowdflower) benchmark."""

	BUILDER_CONFIG_CLASS = crowdflowerConfig

	BUILDER_CONFIGS = [
	crowdflowerConfig(name="sentiment_nuclear_power",
	text_features={"text": "text"},),
	crowdflowerConfig(name="tweet_global_warming",
	text_features={"text": "text"},),
	crowdflowerConfig(name="airline-sentiment",
	text_features={"text": "text"},),
	crowdflowerConfig(name="corporate-messaging",
	text_features={"text": "text"},),
	crowdflowerConfig(name="economic-news",
	text_features={"text": "text"},),
	crowdflowerConfig(name="political-media-audience",
	text_features={"text": "text"},),
	crowdflowerConfig(name="political-media-bias",
	text_features={"text": "text"},),
	crowdflowerConfig(name="political-media-message",
	text_features={"text": "text"},),
	crowdflowerConfig(name="text_emotion",
	text_features={"text": "text"},),
	]

	def _info(self):
	features = {text_feature: datasets.Value("string") for text_feature in six.iterkeys(self.config.text_features)}
	if self.config.label_classes:
	features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
	else:
	features["label"] = datasets.Value("float32")
	features["idx"] = datasets.Value("int32")
	return datasets.DatasetInfo(
	description=_crowdflower_DESCRIPTION,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=self.config.citation + "\n" + _crowdflower_CITATION,
	)

	def _split_generators(self, dl_manager):
	dl_dir = dl_manager.download_and_extract(self.config.data_url)
	data_dir = os.path.join(dl_dir, self.config.data_dir)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data_file": os.path.join(data_dir or "", "train.tsv"),
	"split": "train",
	},
	),
	]

	def _generate_examples(self, data_file, split):

	process_label = self.config.process_label
	label_classes = self.config.label_classes

	with open(data_file, encoding="latin-1") as f:
	reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)

	for n, row in enumerate(reader):

	example = {feat: row[col] for feat, col in six.iteritems(self.config.text_features)}
	example["idx"] = n
	#print(row)

	if self.config.label_column in row:
	label = row[self.config.label_column]
	label = process_label(label)
	if label_classes and label not in label_classes:
	continue
	example["label"] = label
	else:
	example["label"] = process_label(-1)
	if not example["label"] or not example["text"]:
	continue
	yield example["idx"], example