Convert dataset to Parquet

README.md

@@ -31,13 +31,13 @@ dataset_info:
           '1': '1'
   splits:
   - name: train
-    num_bytes: 1104322
+    num_bytes: 1104314
     num_examples: 10041
   - name: test
-    num_bytes: 109681
+    num_bytes: 109677
     num_examples: 1000
-  download_size: 410001
-  dataset_size: 1214003
+  download_size: 645430
+  dataset_size: 1213991
 - config_name: task02
   features:
   - name: text
@@ -51,13 +51,26 @@ dataset_info:
           '2': '2'
   splits:
   - name: train
-    num_bytes: 1104322
+    num_bytes: 1104314
     num_examples: 10041
   - name: test
-    num_bytes: 109681
+    num_bytes: 109677
     num_examples: 1000
-  download_size: 410147
-  dataset_size: 1214003
+  download_size: 646033
+  dataset_size: 1213991
+configs:
+- config_name: task01
+  data_files:
+  - split: train
+    path: task01/train-*
+  - split: test
+    path: task01/test-*
+- config_name: task02
+  data_files:
+  - split: train
+    path: task02/train-*
+  - split: test
+    path: task02/test-*
 ---
 
 # Dataset Card for Poleval 2019 cyberbullying

poleval2019_cyberbullying.py

@@ -1,153 +0,0 @@
-# coding=utf-8
-# 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.
-"""Cyberbullying Classification Dataset in Polish"""
-
-
-import os
-
-import datasets
-
-
-_DESCRIPTION = """\
-    In Task 6-1, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets
-    that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and
-    related phenomena.
-
-    In Task 6-2, the participants shall distinguish between three classes of tweets: 0 (non-harmful),
-    1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech,
-    some of them even putting those two phenomena in the same group. The specific conditions on which we based
-    our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research
-    will be summarized in an introductory paper for the task, however, the main and definitive condition to 1
-    distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying),
-    or a public person/entity/large group (hate-speech).
-"""
-
-_HOMEPAGE = "http://2019.poleval.pl/index.php/tasks/task6"
-
-_URL_TRAIN_TASK1 = "http://2019.poleval.pl/task6/task_6-1.zip"
-_URL_TRAIN_TASK2 = "http://2019.poleval.pl/task6/task_6-2.zip"
-_URL_TEST = "http://2019.poleval.pl/task6/task6_test.zip"
-
-_CITATION = """\
-@proceedings{ogr:kob:19:poleval,
-  editor    = {Maciej Ogrodniczuk and Łukasz Kobyliński},
-  title     = {{Proceedings of the PolEval 2019 Workshop}},
-  year      = {2019},
-  address   = {Warsaw, Poland},
-  publisher = {Institute of Computer Science, Polish Academy of Sciences},
-  url       = {http://2019.poleval.pl/files/poleval2019.pdf},
-  isbn      = "978-83-63159-28-3"}
-}
-"""
-
-
-class Poleval2019CyberBullyingConfig(datasets.BuilderConfig):
-    """BuilderConfig for Poleval2019CyberBullying."""
-
-    def __init__(
-        self,
-        text_features,
-        label_classes,
-        **kwargs,
-    ):
-        super(Poleval2019CyberBullyingConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.text_features = text_features
-        self.label_classes = label_classes
-
-
-class Poleval2019CyberBullying(datasets.GeneratorBasedBuilder):
-    """Cyberbullying Classification Dataset in Polish"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        Poleval2019CyberBullyingConfig(
-            name="task01",
-            text_features=["text"],
-            label_classes=["0", "1"],
-        ),
-        Poleval2019CyberBullyingConfig(
-            name="task02",
-            text_features=["text"],
-            label_classes=["0", "1", "2"],
-        ),
-    ]
-
-    def _info(self):
-
-        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features}
-        features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(features),
-            supervised_keys=("text", "label"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        if self.config.name == "task01":
-            train_path = dl_manager.download_and_extract(_URL_TRAIN_TASK1)
-
-        if self.config.name == "task02":
-            train_path = dl_manager.download_and_extract(_URL_TRAIN_TASK2)
-
-        data_dir_test = dl_manager.download_and_extract(_URL_TEST)
-
-        if self.config.name == "task01":
-            test_path = os.path.join(data_dir_test, "Task6", "task 01")
-
-        if self.config.name == "task02":
-            test_path = os.path.join(data_dir_test, "Task6", "task 02")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": train_path,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": test_path,
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        if split == "train":
-            text_path = os.path.join(filepath, "training_set_clean_only_text.txt")
-            label_path = os.path.join(filepath, "training_set_clean_only_tags.txt")
-
-        if split == "test":
-            if self.config.name == "task01":
-                text_path = os.path.join(filepath, "test_set_clean_only_text.txt")
-                label_path = os.path.join(filepath, "test_set_clean_only_tags.txt")
-            if self.config.name == "task02":
-                text_path = os.path.join(filepath, "test_set_only_text.txt")
-                label_path = os.path.join(filepath, "test_set_only_tags.txt")
-
-        with open(text_path, encoding="utf-8") as text_file:
-            with open(label_path, encoding="utf-8") as label_file:
-                for id_, (text, label) in enumerate(zip(text_file, label_file)):
-                    yield id_, {"text": text.strip(), "label": int(label.strip())}

task01/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 58740

task01/train-00000-of-00001.parquet

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+size 586690

task02/test-00000-of-00001.parquet

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+size 58821

task02/train-00000-of-00001.parquet

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