Datasets:
Tasks:
Text Classification
Formats:
json
Sub-tasks:
multi-label-classification
Languages:
Finnish
Size:
100K - 1M
Tags:
toxicity, multi-label
License:
File size: 3,968 Bytes
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"""Comments from Jigsaw Toxic Comment Classification Kaggle Competition """
import json
import pandas as pd
import datasets
_DESCRIPTION = """\
This dataset consists of a large number of Wikipedia comments translated to Finnish which have been labeled by human raters for toxic behavior.
"""
_HOMEPAGE = "https://turkunlp.org/"
_URLS = {
"train": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/train_fi_deepl.jsonl.bz2",
"test": "https://huggingface.co/datasets/TurkuNLP/wikipedia-toxicity-data-fi/resolve/main/test_fi_deepl.jsonl.bz2"
}
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")
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(
{
"text": datasets.Value("string"),
"label_toxicity": datasets.ClassLabel(names=["false", "true"]),
"label_severe_toxicity": datasets.ClassLabel(names=["false", "true"]),
"label_obscene": datasets.ClassLabel(names=["false", "true"]),
"label_threat": datasets.ClassLabel(names=["false", "true"]),
"label_insult": datasets.ClassLabel(names=["false", "true"]),
"label_identity_attack": 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
)
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
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": downloaded_files["train"]}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
},
),
]
def _generate_examples(self, filepath):
"""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)
# read the json into dictionaries
with open(filepath, 'r') as json_file:
json_list = list(json_file)
lines = [json.loads(jline) for jline in json_list]
for data in lines:
example = {}
example["text"] = data["text"]
for label in ["label_toxicity", "label_severe_toxicity", "label_obscene", "label_threat", "label_insult", "label_identity_attack"]:
example[label] = int(data[label])
yield (data["id"], example)
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