Convert dataset to Parquet

Coarse_Grained/test-00000-of-00001.parquet

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+oid sha256:b5e07f4849ff9ecd0c216844626fefd1c3c4afd1439bd0b16dcb914714f1f641
+size 147029

Coarse_Grained/train-00000-of-00001.parquet

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+oid sha256:974c71ca5261efae4761268b9e86fbd2dc97ab9ac2456efafefffa2a05257ac0
+size 525200

Coarse_Grained/validation-00000-of-00001.parquet

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+oid sha256:850a3d809bb192a6436046a055e5b6b8d62d215b2f4d1c3a38d0c6fda1100d58
+size 58491

Fine_Grained/test-00000-of-00001.parquet

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+oid sha256:202503e8552bfe140c83d8a8c4790cc8b2c95a89c6e7f11497586246e4586ea3
+size 147890

Fine_Grained/train-00000-of-00001.parquet

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+size 525885

Fine_Grained/validation-00000-of-00001.parquet

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+oid sha256:00eadf726850f32bb4f338f2700387d6113e4dae764e0423cd9cc7aaf1d02b15
+size 525885

README.md

@@ -33,16 +33,16 @@ dataset_info:
           '1': Normal
   splits:
   - name: train
-    num_bytes: 725719
+    num_bytes: 725715
     num_examples: 7208
   - name: test
-    num_bytes: 218087
+    num_bytes: 202318
     num_examples: 2002
   - name: validation
-    num_bytes: 79759
+    num_bytes: 79755
     num_examples: 800
-  download_size: 927937
-  dataset_size: 1023565
+  download_size: 730720
+  dataset_size: 1007788
 - config_name: Fine_Grained
   features:
   - name: tweet
@@ -58,16 +58,34 @@ dataset_info:
           '4': Profane/Untargeted
   splits:
   - name: train
-    num_bytes: 723670
+    num_bytes: 723666
     num_examples: 7208
   - name: test
-    num_bytes: 219359
+    num_bytes: 203590
     num_examples: 2002
   - name: validation
-    num_bytes: 723670
+    num_bytes: 723666
     num_examples: 7208
-  download_size: 1519423
-  dataset_size: 1666699
+  download_size: 1199660
+  dataset_size: 1650922
+configs:
+- config_name: Coarse_Grained
+  data_files:
+  - split: train
+    path: Coarse_Grained/train-*
+  - split: test
+    path: Coarse_Grained/test-*
+  - split: validation
+    path: Coarse_Grained/validation-*
+  default: true
+- config_name: Fine_Grained
+  data_files:
+  - split: train
+    path: Fine_Grained/train-*
+  - split: test
+    path: Fine_Grained/test-*
+  - split: validation
+    path: Fine_Grained/validation-*
 ---
 
 # Dataset Card for roman_urdu_hate_speech

roman_urdu_hate_speech.py

@@ -1,210 +0,0 @@
-# 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