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"""CivilComments from Jigsaw Unintended Bias Kaggle Competition.""" |
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from __future__ import absolute_import, division, print_function |
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import csv |
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import os |
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import datasets |
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_CITATION = """ |
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@article{DBLP:journals/corr/abs-1903-04561, |
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author = {Daniel Borkan and |
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Lucas Dixon and |
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Jeffrey Sorensen and |
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Nithum Thain and |
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Lucy Vasserman}, |
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title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text |
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Classification}, |
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journal = {CoRR}, |
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volume = {abs/1903.04561}, |
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year = {2019}, |
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url = {http://arxiv.org/abs/1903.04561}, |
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archivePrefix = {arXiv}, |
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eprint = {1903.04561}, |
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timestamp = {Sun, 31 Mar 2019 19:01:24 +0200}, |
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biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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""" |
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_DESCRIPTION = """ |
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The comments in this dataset come from an archive of the Civil Comments |
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platform, a commenting plugin for independent news sites. These public comments |
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were created from 2015 - 2017 and appeared on approximately 50 English-language |
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news sites across the world. When Civil Comments shut down in 2017, they chose |
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to make the public comments available in a lasting open archive to enable future |
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research. The original data, published on figshare, includes the public comment |
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text, some associated metadata such as article IDs, timestamps and |
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commenter-generated "civility" labels, but does not include user ids. Jigsaw |
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extended this dataset by adding additional labels for toxicity and identity |
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mentions. This data set is an exact replica of the data released for the |
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Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This |
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dataset is released under CC0, as is the underlying comment text. |
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""" |
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_DOWNLOAD_URL = "https://storage.googleapis.com/jigsaw-unintended-bias-in-toxicity-classification/civil_comments.zip" |
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class CivilComments(datasets.GeneratorBasedBuilder): |
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"""Classification and tagging of 2M comments on news sites. |
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This version of the CivilComments Dataset provides access to the primary |
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seven labels that were annotated by crowd workers, the toxicity and other |
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tags are a value between 0 and 1 indicating the fraction of annotators that |
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assigned these attributes to the comment text. |
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The other tags, which are only available for a fraction of the input examples |
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are currently ignored, as are all of the attributes that were part of the |
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original civil comments release. See the Kaggle documentation for more |
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details about the available features. |
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""" |
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VERSION = datasets.Version("0.9.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"toxicity": datasets.Value("float32"), |
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"severe_toxicity": datasets.Value("float32"), |
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"obscene": datasets.Value("float32"), |
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"threat": datasets.Value("float32"), |
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"insult": datasets.Value("float32"), |
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"identity_attack": datasets.Value("float32"), |
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"sexual_explicit": datasets.Value("float32"), |
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} |
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), |
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supervised_keys=("text", "toxicity"), |
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homepage="https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_path = dl_manager.download_and_extract(_DOWNLOAD_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filename": os.path.join(dl_path, "train.csv"), "toxicity_label": "target"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filename": os.path.join(dl_path, "test_public_expanded.csv"), |
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"toxicity_label": "toxicity", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filename": os.path.join(dl_path, "test_private_expanded.csv"), |
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"toxicity_label": "toxicity", |
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}, |
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), |
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] |
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def _generate_examples(self, filename, toxicity_label): |
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"""Yields examples. |
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Each example contains a text input and then seven annotation labels. |
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Args: |
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filename: the path of the file to be read for this split. |
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toxicity_label: indicates 'target' or 'toxicity' to capture the variation |
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in the released labels for this dataset. |
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Yields: |
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A dictionary of features, all floating point except the input text. |
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""" |
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with open(filename, encoding="utf-8") as f: |
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reader = csv.DictReader(f) |
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for row in reader: |
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example = {} |
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example["text"] = row["comment_text"] |
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example["toxicity"] = float(row[toxicity_label]) |
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for label in ["severe_toxicity", "obscene", "threat", "insult", "identity_attack", "sexual_explicit"]: |
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example[label] = float(row[label]) |
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yield row["id"], example |
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