Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
Arabic
Size:
100K - 1M
License:
Commit
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bb0cfb2
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Parent(s):
bacc694
Delete loading script
Browse files
hard.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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"""Hotel Reviews in Arabic language"""
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import os
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import datasets
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from datasets.tasks import TextClassification
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_DESCRIPTION = """\
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This dataset contains 93700 hotel reviews in Arabic language.\
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The hotel reviews were collected from Booking.com website during June/July 2016.\
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The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.\
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The following table summarize some tatistics on the HARD Dataset.
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"""
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_CITATION = """\
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@incollection{elnagar2018hotel,
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title={Hotel Arabic-reviews dataset construction for sentiment analysis applications},
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author={Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},
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booktitle={Intelligent Natural Language Processing: Trends and Applications},
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pages={35--52},
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year={2018},
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publisher={Springer}
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}
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"""
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_DOWNLOAD_URL = "https://raw.githubusercontent.com/elnagara/HARD-Arabic-Dataset/master/data/balanced-reviews.zip"
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class HardConfig(datasets.BuilderConfig):
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"""BuilderConfig for Hard."""
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def __init__(self, **kwargs):
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"""BuilderConfig for Hard.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(HardConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
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class Hard(datasets.GeneratorBasedBuilder):
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"""Hard dataset."""
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BUILDER_CONFIGS = [
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HardConfig(
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name="plain_text",
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description="Plain text",
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)
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]
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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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"text": datasets.Value("string"),
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"label": datasets.features.ClassLabel(
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names=[
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"1",
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"2",
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"3",
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"4",
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"5",
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]
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),
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}
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),
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supervised_keys=None,
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homepage="https://github.com/elnagara/HARD-Arabic-Dataset",
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citation=_CITATION,
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task_templates=[TextClassification(text_column="text", label_column="label")],
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)
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def _split_generators(self, dl_manager):
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data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "balanced-reviews.txt")}
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),
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]
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def _generate_examples(self, directory):
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"""Generate examples."""
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with open(directory, mode="r", encoding="utf-16") as file:
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for id_, line in enumerate(file.read().splitlines()[1:]):
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_, _, rating, _, _, _, review_text = line.split("\t")
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yield str(id_), {"text": review_text, "label": rating}
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