# 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