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"""Ethos dataset""" |
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import pandas as pd |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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@misc{mollas2020ethos, |
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title={ETHOS: an Online Hate Speech Detection Dataset}, |
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author={Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and Grigorios Tsoumakas}, |
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year={2020}, |
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eprint={2006.08328}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """ |
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ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech |
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detection on social media platforms, called Ethos. There are two variations of the dataset: |
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Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label |
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about hate speech presence or absence. 565 of them do not contain hate speech, |
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while the rest of them, 433, contain. |
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Ethos_Dataset_Multi_Label: which contains 8 labels for the 433 comments with hate speech content. |
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These labels are violence (if it incites (1) or not (0) violence), directed_vs_general (if it is |
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directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like, |
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gender, race, national_origin, disability, religion and sexual_orientation. |
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""" |
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_URL = "https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset" |
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class EthosConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Ethos.""" |
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def __init__(self, variation="binary", **kwargs): |
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"""Constructs an EthosDataset. |
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Args: |
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variation: can be binary or multilabel |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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if variation.lower() == "binary": |
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self.variation = "binary" |
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elif variation.lower() == "multilabel": |
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self.variation = "multilabel" |
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else: |
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logger.warning("Wrong variation. Could be either 'binary' or 'multilabel', using 'binary' instead.") |
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self.variation = "binary" |
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super(EthosConfig, self).__init__(**kwargs) |
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class Ethos(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIG_CLASS = EthosConfig |
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BUILDER_CONFIGS = [ |
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EthosConfig( |
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name="binary", |
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version=datasets.Version("1.0.0", ""), |
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description="Ethos Binary", |
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variation="binary", |
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), |
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EthosConfig( |
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name="multilabel", |
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version=datasets.Version("1.0.0", ""), |
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description="Ethos Multi Label", |
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variation="multilabel", |
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), |
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] |
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def _info(self): |
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if self.config.variation == "binary": |
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f = datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"label": datasets.features.ClassLabel(names=["no_hate_speech", "hate_speech"]), |
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} |
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) |
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else: |
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f = datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"violence": datasets.ClassLabel(names=["not_violent", "violent"]), |
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"directed_vs_generalized": datasets.ClassLabel(names=["generalied", "directed"]), |
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"gender": datasets.ClassLabel(names=["false", "true"]), |
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"race": datasets.ClassLabel(names=["false", "true"]), |
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"national_origin": datasets.ClassLabel(names=["false", "true"]), |
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"disability": datasets.ClassLabel(names=["false", "true"]), |
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"religion": datasets.ClassLabel(names=["false", "true"]), |
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"sexual_orientation": datasets.ClassLabel(names=["false", "true"]), |
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} |
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) |
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return datasets.DatasetInfo( |
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features=f, |
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supervised_keys=None, |
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homepage="https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset/tree/masterethos/ethos_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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if self.config.variation == "binary": |
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url = { |
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"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset" |
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"/master/ethos/ethos_data/Ethos_Dataset_Binary.csv" |
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} |
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else: |
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url = { |
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"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset" |
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"/master/ethos/ethos_data/Ethos_Dataset_Multi_Label.csv" |
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} |
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downloaded_files = dl_manager.download_and_extract(url) |
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] |
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def _generate_examples(self, filepath): |
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"""Yields examples.""" |
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data = pd.read_csv(filepath, delimiter=";") |
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if self.config.variation == "binary": |
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x = data["comment"].values |
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y = [1 if i >= 0.5 else 0 for i in data["isHate"].values] |
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class_names = ["no_hate_speech", "hate_speech"] |
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for i in range(len(x)): |
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_id = i |
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yield _id, {"text": x[i], "label": class_names[y[i]]} |
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else: |
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x = data["comment"].values |
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y_temp = data.loc[:, data.columns != "comment"].values |
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y = [] |
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for yt in y_temp: |
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yi = [] |
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for i in yt: |
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if i >= 0.5: |
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yi.append(int(1)) |
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else: |
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yi.append(int(0)) |
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y.append(yi) |
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for i in range(len(x)): |
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_id = i |
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yield _id, { |
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"text": x[i], |
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"violence": y[i][0], |
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"directed_vs_generalized": y[i][1], |
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"gender": y[i][2], |
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"race": y[i][3], |
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"national_origin": y[i][4], |
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"disability": y[i][5], |
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"religion": y[i][6], |
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"sexual_orientation": y[i][7], |
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} |
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