Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-label-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
other
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
File size: 5,767 Bytes
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# 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.
"""Comments from Jigsaw Toxic Comment Classification Kaggle Competition """
import os
import pandas as pd
import datasets
_DESCRIPTION = """\
This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.
"""
_HOMEPAGE = "https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data"
_LICENSE = 'The "Toxic Comment Classification" dataset is released under CC0, with the underlying comment text being governed by Wikipedia\'s CC-SA-3.0.'
class JigsawToxicityPred(datasets.GeneratorBasedBuilder):
"""This is a dataset of comments from Wikipedia’s talk page edits which have been labeled by human raters for toxic behavior."""
VERSION = datasets.Version("1.1.0")
@property
def manual_download_instructions(self):
return """\
To use jigsaw_toxicity_pred you have to download it manually from Kaggle: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data
You can manually download the data from it's homepage or use the Kaggle CLI tool (follow the instructions here: https://www.kaggle.com/docs/api)
Please extract all files in one folder and then load the dataset with:
`datasets.load_dataset('jigsaw_toxicity_pred', data_dir='/path/to/extracted/data/')`"""
def _info(self):
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=datasets.Features(
{
"comment_text": datasets.Value("string"),
"toxic": datasets.ClassLabel(names=["false", "true"]),
"severe_toxic": datasets.ClassLabel(names=["false", "true"]),
"obscene": datasets.ClassLabel(names=["false", "true"]),
"threat": datasets.ClassLabel(names=["false", "true"]),
"insult": datasets.ClassLabel(names=["false", "true"]),
"identity_hate": datasets.ClassLabel(names=["false", "true"]),
}
),
# 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,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(data_dir):
raise FileNotFoundError(
f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('jigsaw_toxicity_pred', data_dir=...)`. Manual download instructions: {self.manual_download_instructions}"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"train_path": os.path.join(data_dir, "train.csv"), "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"test_text_path": os.path.join(data_dir, "test.csv"),
"test_labels_path": os.path.join(data_dir, "test_labels.csv"),
"split": "test",
},
),
]
def _generate_examples(self, split="train", train_path=None, test_text_path=None, test_labels_path=None):
"""Yields examples."""
# This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
# The key is not important, it's more here for legacy reason (legacy from tfds)
if split == "test":
df1 = pd.read_csv(test_text_path)
df2 = pd.read_csv(test_labels_path)
df3 = df1.merge(df2)
df4 = df3[df3["toxic"] != -1]
elif split == "train":
df4 = pd.read_csv(train_path)
for _, row in df4.iterrows():
example = {}
example["comment_text"] = row["comment_text"]
for label in ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]:
if row[label] != -1:
example[label] = int(row[label])
yield (row["id"], example)
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