# 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. """roman_urdu_hate_speech dataset""" import csv import datasets from datasets.tasks import TextClassification # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @inproceedings{rizwan2020hate, title={Hate-speech and offensive language detection in roman Urdu}, author={Rizwan, Hammad and Shakeel, Muhammad Haroon and Karim, Asim}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, pages={2512--2522}, year={2020} } """ # You can copy an official description _DESCRIPTION = """\ The Roman Urdu Hate-Speech and Offensive Language Detection (RUHSOLD) dataset is a \ Roman Urdu dataset of tweets annotated by experts in the relevant language. \ The authors develop the gold-standard for two sub-tasks. \ First sub-task is based on binary labels of Hate-Offensive content and Normal content (i.e., inoffensive language). \ These labels are self-explanatory. \ The authors refer to this sub-task as coarse-grained classification. \ Second sub-task defines Hate-Offensive content with \ four labels at a granular level. \ These labels are the most relevant for the demographic of users who converse in RU and \ are defined in related literature. The authors refer to this sub-task as fine-grained classification. \ The objective behind creating two gold-standards is to enable the researchers to evaluate the hate speech detection \ approaches on both easier (coarse-grained) and challenging (fine-grained) scenarios. \ """ _HOMEPAGE = "https://github.com/haroonshakeel/roman_urdu_hate_speech" _LICENSE = "MIT License" _Download_URL = "https://raw.githubusercontent.com/haroonshakeel/roman_urdu_hate_speech/main/" # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "Coarse_Grained_train": _Download_URL + "task_1_train.tsv", "Coarse_Grained_validation": _Download_URL + "task_1_validation.tsv", "Coarse_Grained_test": _Download_URL + "task_1_test.tsv", "Fine_Grained_train": _Download_URL + "task_2_train.tsv", "Fine_Grained_validation": _Download_URL + "task_2_validation.tsv", "Fine_Grained_test": _Download_URL + "task_2_test.tsv", } class RomanUrduHateSpeechConfig(datasets.BuilderConfig): """BuilderConfig for RomanUrduHateSpeech Config""" def __init__(self, **kwargs): """BuilderConfig for RomanUrduHateSpeech Config. Args: **kwargs: keyword arguments forwarded to super. """ super(RomanUrduHateSpeechConfig, self).__init__(**kwargs) class RomanUrduHateSpeech(datasets.GeneratorBasedBuilder): """Roman Urdu Hate Speech 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 = [ RomanUrduHateSpeechConfig( name="Coarse_Grained", version=VERSION, description="This part of my dataset covers the Coarse Grained dataset", ), RomanUrduHateSpeechConfig( name="Fine_Grained", version=VERSION, description="This part of my dataset covers the Fine Grained dataset" ), ] DEFAULT_CONFIG_NAME = "Coarse_Grained" # It's not mandatory to have a default configuration. Just use one if it makes sense. def _info(self): if self.config.name == "Coarse_Grained": features = datasets.Features( { "tweet": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["Abusive/Offensive", "Normal"]), # These are the features of your dataset like images, labels ... } ) if self.config.name == "Fine_Grained": features = datasets.Features( { "tweet": datasets.Value("string"), "label": datasets.features.ClassLabel( names=["Abusive/Offensive", "Normal", "Religious Hate", "Sexism", "Profane/Untargeted"] ), # These are the features of your dataset like images, labels ... } ) 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, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, task_templates=[TextClassification(text_column="tweet", label_column="label")], ) def _split_generators(self, dl_manager): urls_train = _URLS[self.config.name + "_train"] urls_validate = _URLS[self.config.name + "_validation"] urls_test = _URLS[self.config.name + "_test"] data_dir_train = dl_manager.download_and_extract(urls_train) data_dir_validate = dl_manager.download_and_extract(urls_validate) data_dir_test = dl_manager.download_and_extract(urls_test) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir_train, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir_test, "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir_validate, "split": "dev", }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as tsv_file: tsv_reader = csv.reader(tsv_file, quotechar="|", delimiter="\t", quoting=csv.QUOTE_ALL) for key, row in enumerate(tsv_reader): if key == 0: continue if self.config.name == "Coarse_Grained": tweet, label = row label = int(label) yield key, { "tweet": tweet, "label": None if split == "test" else label, } if self.config.name == "Fine_Grained": tweet, label = row label = int(label) yield key, { "tweet": tweet, "label": None if split == "test" else label, } # Yields examples as (key, example) tuples