Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-label-classification
Languages:
English
Size:
10K<n<100K
License:
| # 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. | |
| """Class for loading datafrom rtGender""" | |
| from __future__ import absolute_import, division, print_function | |
| import csv | |
| from enum import Enum | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{voigt-etal-2018-rtgender, | |
| title = "{R}t{G}ender: A Corpus for Studying Differential Responses to Gender", | |
| author = "Voigt, Rob and | |
| Jurgens, David and | |
| Prabhakaran, Vinodkumar and | |
| Jurafsky, Dan and | |
| Tsvetkov, Yulia", | |
| booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", | |
| month = may, | |
| year = "2018", | |
| address = "Miyazaki, Japan", | |
| publisher = "European Language Resources Association (ELRA)", | |
| url = "https://www.aclweb.org/anthology/L18-1445", | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| RtGender is a corpus for studying responses to gender online, including posts and responses from Facebook, TED, Fitocracy, and Reddit where the gender of the source poster/speaker is known. | |
| """ | |
| _HOMEPAGE = "https://nlp.stanford.edu/robvoigt/rtgender/#contact" | |
| _LICENSE = "Research Only" | |
| _URL = "https://nlp.stanford.edu/robvoigt/rtgender/rtgender.tar.gz" | |
| class Config(Enum): | |
| ANNOTATIONS = "annotations" | |
| POSTS = "posts" | |
| RESPONSES = "responses" | |
| FB_POLI = "fb_politicians" | |
| FB_PUB = "fb_public" | |
| TED = "ted" | |
| FITOCRACY = "fitocracy" | |
| REDDIT = "reddit" | |
| class rtGender(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| VERSION = datasets.Version("1.1.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name=Config.ANNOTATIONS.value, | |
| version=VERSION, | |
| description="Retrieves only the annotations.", | |
| ), | |
| datasets.BuilderConfig( | |
| name=Config.POSTS.value, | |
| version=VERSION, | |
| description="Retrieves all posts.", | |
| ), | |
| datasets.BuilderConfig( | |
| name=Config.RESPONSES.value, | |
| version=VERSION, | |
| description="Retrieves all responses.", | |
| ) | |
| ] | |
| DEFAULT_CONFIG_NAME = Config.ANNOTATIONS.value # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| POSTS_FEATURES = { | |
| "source": datasets.Value("string"), | |
| "op_id": datasets.Value("string"), | |
| "op_gender": datasets.Value("string"), | |
| "post_id": datasets.Value("string"), | |
| "post_text": datasets.Value("string"), | |
| "post_type": datasets.Value("string"), # only for fb | |
| "subreddit": datasets.Value("string"), # only for reddit | |
| "op_gender_visible": datasets.Value("string"), # only for reddit | |
| } | |
| RESPONSES_FEATURES = { | |
| "source": datasets.Value("string"), | |
| "op_id": datasets.Value("string"), | |
| "op_gender": datasets.Value("string"), | |
| "post_id": datasets.Value("string"), | |
| "responder_id": datasets.Value("string"), | |
| "response_text": datasets.Value("string"), | |
| "op_name": datasets.Value("string"), # only for fb | |
| "op_category": datasets.Value("string"), # only for fb | |
| "responder_gender": datasets.Value("string"), # only for fitocracy and reddit | |
| "responder_gender_visible": datasets.Value("string"), # only for reddit | |
| "subreddit": datasets.Value("string"), | |
| } | |
| ANNOTATION_FEATURES = { | |
| "source": datasets.Value("string"), | |
| "op_gender": datasets.Value("string"), | |
| "post_text": datasets.Value("string"), | |
| "response_text": datasets.Value("string"), | |
| "sentiment": datasets.Value("string"), | |
| "relevance": datasets.Value("string"), | |
| } | |
| def _info(self): | |
| if ( | |
| self.config.name == Config.ANNOTATIONS.value | |
| ): # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features = datasets.Features(self.ANNOTATION_FEATURES) | |
| elif self.config.name == Config.POSTS.value: | |
| features = datasets.Features(self.POSTS_FEATURES) | |
| else: | |
| features = datasets.Features(self.RESPONSES_FEATURES) | |
| 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 | |
| # If there's a common (input, target) tuple from the features, | |
| # specify them here. They'll be used if as_supervised=True in | |
| # builder.as_dataset. | |
| supervised_keys=None, | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| data_dir = dl_manager.download_and_extract(_URL) | |
| if self.config.name == Config.ANNOTATIONS.value: | |
| files = ["annotations.csv"] | |
| elif self.config.name == Config.POSTS.value: | |
| files = [ | |
| "facebook_congress_posts.csv", | |
| "facebook_wiki_posts.csv", | |
| "fitocracy_posts.csv", | |
| "reddit_posts.csv", | |
| ] | |
| else: | |
| files = [ | |
| "facebook_congress_responses.csv", | |
| "facebook_wiki_responses.csv", | |
| "fitocracy_responses.csv", | |
| "reddit_responses.csv", | |
| "ted_responses.csv", | |
| ] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepaths": list(map(lambda x: os.path.join(data_dir, x), files)), | |
| "split": "train", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples( | |
| self, | |
| filepaths, | |
| split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| ): | |
| """ Yields examples as (key, example) tuples. """ | |
| # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is here for legacy reason (tfds) and is not important in itself. | |
| files = [] | |
| readers = {} | |
| for fp in filepaths: | |
| f = open(fp, encoding="utf-8") | |
| reader = csv.reader(f) | |
| next(reader) | |
| readers[fp.replace(".csv", "")] = reader | |
| files.append(f) | |
| id_ = 0 | |
| for reader_name, reader in readers.items(): | |
| for row in reader: | |
| if self.config.name == Config.ANNOTATIONS.value: | |
| yield id_, { | |
| "source": row[0], | |
| "op_gender": row[1], | |
| "post_text": row[2], | |
| "response_text": row[3], | |
| "sentiment": row[4], | |
| "relevance": row[5], | |
| } | |
| elif self.config.name == Config.POSTS.value: | |
| r = { | |
| "source": reader_name, | |
| "op_id": row[0], | |
| "op_gender": row[1], | |
| "post_id": row[2], | |
| "post_text": row[3], | |
| "post_type": None, | |
| "subreddit": None, | |
| "op_gender_visible": None, | |
| } | |
| if "facebook" in reader_name: | |
| r["post_type"] = row[4] | |
| elif "reddit" in reader_name: | |
| r["subreddit"] = row[4] | |
| r["op_gender_visible"] = row[5] | |
| yield id_, r | |
| else: | |
| r = { | |
| "source": reader_name, | |
| "op_id": row[0], | |
| "op_gender": row[1], | |
| "post_id": row[2], | |
| "responder_id": row[3], | |
| "response_text": row[4], | |
| "op_name": None, | |
| "op_category": None, | |
| "responder_gender": None, | |
| "responder_gender_visible": None, | |
| "subreddit": None | |
| } | |
| if "facebook" in reader_name: | |
| r["op_name"] = row[5] | |
| r["op_category"] = row[6] | |
| elif "fitocracy" in reader_name: | |
| r["responder_gender"] = row[5] | |
| elif "reddit" in reader_name: | |
| r["subreddit"] = row[5] | |
| r["responder_gender"] = row[6] | |
| r["responder_gender_visible"] = row[7] | |
| yield id_, r | |
| id_ += 1 | |
| for fd in files: | |
| fd.close() | |