Convert dataset to Parquet

README.md

@@ -30,13 +30,20 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3191888
+    num_bytes: 3191760
     num_examples: 31962
   - name: test
-    num_bytes: 1711606
+    num_bytes: 1711534
     num_examples: 17197
-  download_size: 4738708
-  dataset_size: 4903494
+  download_size: 3180269
+  dataset_size: 4903294
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 train-eval-index:
 - config: default
   task: text-classification

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e18ee8c6b8c4c721233f7c8a03a5e75534cca295f23add2f2a2a7487a8f1bcd
+size 1112032

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfd5df659f97f24d66059ca9f161ab177d1c544853d7f106e8e22858cdd801e5
+size 2068237

tweets_hate_speech_detection.py

@@ -1,83 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""Detecing which tweets showcase hate or racist remarks."""
-
-
-import csv
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.
-
-Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.
-"""
-
-_HOMEPAGE = "https://github.com/sharmaroshan/Twitter-Sentiment-Analysis"
-
-_CITATION = """\
-@InProceedings{Z
-Roshan Sharma:dataset,
-title = {Sentimental Analysis of Tweets for Detecting Hate/Racist Speeches},
-authors={Roshan Sharma},
-year={2018}
-}
-"""
-
-_URL = {
-    "train": "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/train_tweet.csv",
-    "test": "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/test_tweets.csv",
-}
-
-
-class TweetsHateSpeechDetection(datasets.GeneratorBasedBuilder):
-    """Detecting which tweets showcase hate or racist remarks."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "label": datasets.ClassLabel(names=["no-hate-speech", "hate-speech"]),
-                    "tweet": datasets.Value("string"),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="tweet", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        path = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate Tweet examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.DictReader(
-                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
-            )
-            for id_, row in enumerate(csv_reader):
-                yield id_, {
-                    "label": int(row.setdefault("label", -1)),
-                    "tweet": row["tweet"],
-                }