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Update files from the datasets library (from 1.2.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - found
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+ language_creators:
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+ - found
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+ languages:
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+ - ar
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+ licenses:
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+ - unknown
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 10k<n<100k
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - text-classification
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+ task_ids:
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+ - multi-class-classification
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+ ---
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+
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+ # Dataset Card for Hard
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-instances)
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+ - [Data Splits](#data-instances)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
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+ - **Repository:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
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+ - **Paper:** [Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3)
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+ - **Point of Contact:** [Ashraf Elnagar](ashraf@sharjah.ac.ae)
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+
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+ ### Dataset Summary
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+
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+ This dataset contains 93,700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset.
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ [More Information Needed]
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+
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+ ### Languages
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+
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+ The dataset is based on Arabic.
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ A typical data point comprises a rating from 1 to 5 for hotels.
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+
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+ ### Data Fields
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+
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+ [More Information Needed]
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+
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+ ### Data Splits
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+
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+ The dataset is not split.
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ [More Information Needed]
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
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+
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+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
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+ [More Information Needed]
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+
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+ ### Discussion of Biases
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+
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+ [More Information Needed]
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+
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+ ### Other Known Limitations
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+
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+ [More Information Needed]
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ [More Information Needed]
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+
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+ ### Licensing Information
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+
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+ [More Information Needed]
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+
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+ ### Citation Information
dataset_infos.json ADDED
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+ {"plain_text": {"description": "This dataset contains 93700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset.\n", "citation": "@incollection{elnagar2018hotel,\n title={Hotel Arabic-reviews dataset construction for sentiment analysis applications},\n author={Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},\n booktitle={Intelligent Natural Language Processing: Trends and Applications},\n pages={35--52},\n year={2018},\n publisher={Springer}\n}\n", "homepage": "https://github.com/elnagara/HARD-Arabic-Dataset", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 5, "names": ["1", "2", "3", "4", "5"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "hard", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 27507085, "num_examples": 105698, "dataset_name": "hard"}}, "download_checksums": {"https://raw.githubusercontent.com/elnagara/HARD-Arabic-Dataset/master/data/balanced-reviews.zip": {"num_bytes": 8508677, "checksum": "1939c1ca59ff50bd3887223b153c7fb5c9fd232b405320244c55791bc8b5d448"}}, "download_size": 8508677, "post_processing_size": null, "dataset_size": 27507085, "size_in_bytes": 36015762}}
dummy/plain_text/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f3db1abfa2e9cc8a6bfb3c5598a2a8138c6c6dca8a6c4c2ea406b23bf3b3194a
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+ size 556
hard.py ADDED
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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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+
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+ # Lint as: python3
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+ """Hotel Reviews in Arabic language"""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import os
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+
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+ import datasets
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+
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+
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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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+
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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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+
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+ _DOWNLOAD_URL = "https://raw.githubusercontent.com/elnagara/HARD-Arabic-Dataset/master/data/balanced-reviews.zip"
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+
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+
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+ class HardConfig(datasets.BuilderConfig):
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+ """BuilderConfig for Hard."""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for Hard.
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+
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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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+
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+
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+ class Hard(datasets.GeneratorBasedBuilder):
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+ """Hard dataset."""
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+
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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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+
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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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+ )
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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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+
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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}