Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
Commit
•
da252d5
1
Parent(s):
498dbe6
Convert dataset to Parquet (#5)
Browse files- Convert dataset to Parquet (c07734174dae8da7962d9e7899d04278a6e0e209)
- Delete loading script (a642fa48bb836bd3a0b39b3a8d344badc966c854)
- README.md +11 -4
- data/test-00000-of-00001.parquet +3 -0
- data/train-00000-of-00001.parquet +3 -0
- tweets_hate_speech_detection.py +0 -83
README.md
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@@ -30,13 +30,20 @@ dataset_info:
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dtype: string
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splits:
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- name: train
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num_bytes:
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num_examples: 31962
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- name: test
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num_bytes:
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num_examples: 17197
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download_size:
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dataset_size:
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train-eval-index:
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- config: default
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task: text-classification
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dtype: string
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splits:
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- name: train
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num_bytes: 3191760
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num_examples: 31962
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- name: test
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num_bytes: 1711534
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num_examples: 17197
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download_size: 3180269
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dataset_size: 4903294
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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train-eval-index:
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- config: default
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task: text-classification
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data/test-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e18ee8c6b8c4c721233f7c8a03a5e75534cca295f23add2f2a2a7487a8f1bcd
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size 1112032
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data/train-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:dfd5df659f97f24d66059ca9f161ab177d1c544853d7f106e8e22858cdd801e5
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size 2068237
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tweets_hate_speech_detection.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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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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# Lint as: python3
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"""Detecing which tweets showcase hate or racist remarks."""
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import csv
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import datasets
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from datasets.tasks import TextClassification
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_DESCRIPTION = """\
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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.
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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.
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"""
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_HOMEPAGE = "https://github.com/sharmaroshan/Twitter-Sentiment-Analysis"
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_CITATION = """\
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@InProceedings{Z
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Roshan Sharma:dataset,
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title = {Sentimental Analysis of Tweets for Detecting Hate/Racist Speeches},
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authors={Roshan Sharma},
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year={2018}
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}
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"""
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_URL = {
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"train": "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/train_tweet.csv",
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"test": "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/test_tweets.csv",
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}
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class TweetsHateSpeechDetection(datasets.GeneratorBasedBuilder):
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"""Detecting which tweets showcase hate or racist remarks."""
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"label": datasets.ClassLabel(names=["no-hate-speech", "hate-speech"]),
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"tweet": datasets.Value("string"),
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}
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),
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homepage=_HOMEPAGE,
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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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def _split_generators(self, dl_manager):
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path = dl_manager.download(_URL)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path["train"]}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path["test"]}),
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]
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def _generate_examples(self, filepath):
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"""Generate Tweet examples."""
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with open(filepath, encoding="utf-8") as csv_file:
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csv_reader = csv.DictReader(
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csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
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)
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for id_, row in enumerate(csv_reader):
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yield id_, {
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"label": int(row.setdefault("label", -1)),
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"tweet": row["tweet"],
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}
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