Convert dataset to Parquet

#4
by albertvillanova HF staff - opened
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README.md CHANGED
@@ -33,16 +33,16 @@ dataset_info:
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  '1': Normal
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  splits:
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  - name: train
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- num_bytes: 725719
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  num_examples: 7208
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  - name: test
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- num_bytes: 218087
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  num_examples: 2002
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  - name: validation
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- num_bytes: 79759
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  num_examples: 800
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- download_size: 927937
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- dataset_size: 1023565
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  - config_name: Fine_Grained
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  features:
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  - name: tweet
@@ -58,16 +58,34 @@ dataset_info:
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  '4': Profane/Untargeted
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  splits:
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  - name: train
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- num_bytes: 723670
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  num_examples: 7208
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  - name: test
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- num_bytes: 219359
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  num_examples: 2002
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  - name: validation
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- num_bytes: 723670
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  num_examples: 7208
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- download_size: 1519423
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- dataset_size: 1666699
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for roman_urdu_hate_speech
 
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  '1': Normal
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  splits:
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  - name: train
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+ num_bytes: 725715
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  num_examples: 7208
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  - name: test
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+ num_bytes: 202318
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  num_examples: 2002
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  - name: validation
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+ num_bytes: 79755
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  num_examples: 800
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+ download_size: 730720
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+ dataset_size: 1007788
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  - config_name: Fine_Grained
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  features:
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  - name: tweet
 
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  '4': Profane/Untargeted
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  splits:
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  - name: train
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+ num_bytes: 723666
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  num_examples: 7208
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  - name: test
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+ num_bytes: 203590
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  num_examples: 2002
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  - name: validation
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+ num_bytes: 723666
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  num_examples: 7208
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+ download_size: 1199660
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+ dataset_size: 1650922
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+ configs:
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+ - config_name: Coarse_Grained
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+ data_files:
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+ - split: train
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+ path: Coarse_Grained/train-*
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+ - split: test
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+ path: Coarse_Grained/test-*
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+ - split: validation
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+ path: Coarse_Grained/validation-*
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+ default: true
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+ - config_name: Fine_Grained
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+ data_files:
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+ - split: train
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+ path: Fine_Grained/train-*
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+ - split: test
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+ path: Fine_Grained/test-*
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+ - split: validation
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+ path: Fine_Grained/validation-*
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  ---
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  # Dataset Card for roman_urdu_hate_speech
roman_urdu_hate_speech.py DELETED
@@ -1,210 +0,0 @@
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- # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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- """roman_urdu_hate_speech dataset"""
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-
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-
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- import csv
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-
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- import datasets
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- from datasets.tasks import TextClassification
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-
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-
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- # Find for instance the citation on arxiv or on the dataset repo/website
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- _CITATION = """\
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- @inproceedings{rizwan2020hate,
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- title={Hate-speech and offensive language detection in roman Urdu},
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- author={Rizwan, Hammad and Shakeel, Muhammad Haroon and Karim, Asim},
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- booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
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- pages={2512--2522},
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- year={2020}
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- }
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- """
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-
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- # You can copy an official description
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- _DESCRIPTION = """\
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- The Roman Urdu Hate-Speech and Offensive Language Detection (RUHSOLD) dataset is a \
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- Roman Urdu dataset of tweets annotated by experts in the relevant language. \
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- The authors develop the gold-standard for two sub-tasks. \
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- First sub-task is based on binary labels of Hate-Offensive content and Normal content (i.e., inoffensive language). \
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- These labels are self-explanatory. \
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- The authors refer to this sub-task as coarse-grained classification. \
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- Second sub-task defines Hate-Offensive content with \
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- four labels at a granular level. \
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- These labels are the most relevant for the demographic of users who converse in RU and \
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- are defined in related literature. The authors refer to this sub-task as fine-grained classification. \
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- The objective behind creating two gold-standards is to enable the researchers to evaluate the hate speech detection \
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- approaches on both easier (coarse-grained) and challenging (fine-grained) scenarios. \
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- """
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-
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- _HOMEPAGE = "https://github.com/haroonshakeel/roman_urdu_hate_speech"
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-
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- _LICENSE = "MIT License"
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-
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- _Download_URL = "https://raw.githubusercontent.com/haroonshakeel/roman_urdu_hate_speech/main/"
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-
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- # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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- # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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- _URLS = {
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- "Coarse_Grained_train": _Download_URL + "task_1_train.tsv",
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- "Coarse_Grained_validation": _Download_URL + "task_1_validation.tsv",
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- "Coarse_Grained_test": _Download_URL + "task_1_test.tsv",
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- "Fine_Grained_train": _Download_URL + "task_2_train.tsv",
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- "Fine_Grained_validation": _Download_URL + "task_2_validation.tsv",
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- "Fine_Grained_test": _Download_URL + "task_2_test.tsv",
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- }
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-
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-
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- class RomanUrduHateSpeechConfig(datasets.BuilderConfig):
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- """BuilderConfig for RomanUrduHateSpeech Config"""
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-
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- def __init__(self, **kwargs):
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- """BuilderConfig for RomanUrduHateSpeech Config.
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- Args:
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- **kwargs: keyword arguments forwarded to super.
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- """
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- super(RomanUrduHateSpeechConfig, self).__init__(**kwargs)
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-
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-
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- class RomanUrduHateSpeech(datasets.GeneratorBasedBuilder):
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- """Roman Urdu Hate Speech dataset"""
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-
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- VERSION = datasets.Version("1.1.0")
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-
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- # This is an example of a dataset with multiple configurations.
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- # If you don't want/need to define several sub-sets in your dataset,
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- # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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-
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- # If you need to make complex sub-parts in the datasets with configurable options
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- # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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- # BUILDER_CONFIG_CLASS = MyBuilderConfig
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-
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- # You will be able to load one or the other configurations in the following list with
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- # data = datasets.load_dataset('my_dataset', 'first_domain')
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- # data = datasets.load_dataset('my_dataset', 'second_domain')
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- BUILDER_CONFIGS = [
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- RomanUrduHateSpeechConfig(
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- name="Coarse_Grained",
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- version=VERSION,
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- description="This part of my dataset covers the Coarse Grained dataset",
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- ),
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- RomanUrduHateSpeechConfig(
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- name="Fine_Grained", version=VERSION, description="This part of my dataset covers the Fine Grained dataset"
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- ),
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- ]
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-
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- DEFAULT_CONFIG_NAME = "Coarse_Grained"
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- # It's not mandatory to have a default configuration. Just use one if it makes sense.
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-
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- def _info(self):
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-
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- if self.config.name == "Coarse_Grained":
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- features = datasets.Features(
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- {
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- "tweet": datasets.Value("string"),
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- "label": datasets.features.ClassLabel(names=["Abusive/Offensive", "Normal"]),
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- # These are the features of your dataset like images, labels ...
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- }
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- )
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- if self.config.name == "Fine_Grained":
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- features = datasets.Features(
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- {
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- "tweet": datasets.Value("string"),
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- "label": datasets.features.ClassLabel(
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- names=["Abusive/Offensive", "Normal", "Religious Hate", "Sexism", "Profane/Untargeted"]
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- ),
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- # These are the features of your dataset like images, labels ...
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- }
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- )
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # This defines the different columns of the dataset and their types
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- features=features, # Here we define them above because they are different between the two configurations
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- # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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- # specify them. They'll be used if as_supervised=True in builder.as_dataset.
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- # supervised_keys=("sentence", "label"),
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- # Homepage of the dataset for documentation
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- homepage=_HOMEPAGE,
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- # License for the dataset if available
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- license=_LICENSE,
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- # Citation for the dataset
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- citation=_CITATION,
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- task_templates=[TextClassification(text_column="tweet", label_column="label")],
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- )
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-
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- def _split_generators(self, dl_manager):
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-
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- urls_train = _URLS[self.config.name + "_train"]
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-
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- urls_validate = _URLS[self.config.name + "_validation"]
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-
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- urls_test = _URLS[self.config.name + "_test"]
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-
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- data_dir_train = dl_manager.download_and_extract(urls_train)
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-
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- data_dir_validate = dl_manager.download_and_extract(urls_validate)
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-
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- data_dir_test = dl_manager.download_and_extract(urls_test)
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-
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={
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- "filepath": data_dir_train,
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- "split": "train",
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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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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={
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- "filepath": data_dir_test,
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- "split": "test",
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- },
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={
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- "filepath": data_dir_validate,
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- "split": "dev",
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- },
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- ),
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- ]
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-
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- # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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- def _generate_examples(self, filepath, split):
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-
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- # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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- with open(filepath, encoding="utf-8") as tsv_file:
192
- tsv_reader = csv.reader(tsv_file, quotechar="|", delimiter="\t", quoting=csv.QUOTE_ALL)
193
- for key, row in enumerate(tsv_reader):
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- if key == 0:
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- continue
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- if self.config.name == "Coarse_Grained":
197
- tweet, label = row
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- label = int(label)
199
- yield key, {
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- "tweet": tweet,
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- "label": None if split == "test" else label,
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- }
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- if self.config.name == "Fine_Grained":
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- tweet, label = row
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- label = int(label)
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- yield key, {
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- "tweet": tweet,
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- "label": None if split == "test" else label,
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- }
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- # Yields examples as (key, example) tuples