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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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+ *.7z filter=lfs diff=lfs merge=lfs -text
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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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+ - 1k<n<10k
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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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+ - sentiment-classification
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+ ---
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+
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+ # Dataset Card for MetRec
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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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+ - [Discussion of Social Impact and Biases](#discussion-of-social-impact-and-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:** [AJGT](https://github.com/komari6/Arabic-twitter-corpus-AJGT)
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+ - **Repository:** [AJGT](https://github.com/komari6/Arabic-twitter-corpus-AJGT)
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+ - **Paper:** [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66)
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+ - **Point of Contact:** [Khaled Alomari](khaled.alomari@adu.ac.ae)
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+
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+ ### Dataset Summary
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+
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+ Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66).
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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 binary datset with with negative and positive sentiments.
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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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+ | | Tain |
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+ |---------- | ------ |
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+ |no split | 1,800 |
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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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+ [More Information Needed]
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+
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+ #### Initial Data Collection and Normalization
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+
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+ Contains 1,800 tweets collected from twitter.
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+
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+ #### Who are the source language producers?
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+
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+ From tweeter.
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+
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+ ### Annotations
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+
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+ The dataset does not contain any additional 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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+ ### Discussion of Social Impact and 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
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+
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+ [More Information Needed]
ajgt_twitter_ar.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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+ """Arabic Jordanian General Tweets."""
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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 openpyxl # noqa: requires this pandas optional dependency for reading xlsx files
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+ import pandas as pd
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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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+ Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets \
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+ annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.
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+ """
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+
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+ _CITATION = """\
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+ @inproceedings{alomari2017arabic,
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+ title={Arabic tweets sentimental analysis using machine learning},
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+ author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
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+ booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
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+ pages={602--610},
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+ year={2017},
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+ organization={Springer}
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+ }
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+ """
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+
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+ _URL = "https://raw.githubusercontent.com/komari6/Arabic-twitter-corpus-AJGT/master/"
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+
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+
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+ class AjgtConfig(datasets.BuilderConfig):
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+ """BuilderConfig for Ajgt."""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for Ajgt.
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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(AjgtConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
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+
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+
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+ class AjgtTwitterAr(datasets.GeneratorBasedBuilder):
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+ """Ajgt dataset."""
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+
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+ BUILDER_CONFIGS = [
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+ AjgtConfig(
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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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+ "Negative",
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+ "Positive",
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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/komari6/Arabic-twitter-corpus-AJGT",
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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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+ urls_to_download = {
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+ "train": os.path.join(_URL, "AJGT.xlsx"),
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+ }
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+ downloaded_files = dl_manager.download(urls_to_download)
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """Generate examples."""
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+ with open(filepath, "rb") as f:
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+ df = pd.read_excel(f, engine="openpyxl")
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+ for id_, record in df.iterrows():
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+ tweet, sentiment = record["Feed"], record["Sentiment"]
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+ yield str(id_), {"text": tweet, "label": sentiment}
dataset_infos.json ADDED
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+ {"plain_text": {"description": "Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.\n", "citation": "@inproceedings{alomari2017arabic,\n title={Arabic tweets sentimental analysis using machine learning},\n author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},\n booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},\n pages={602--610},\n year={2017},\n organization={Springer}\n}\n", "homepage": "https://github.com/komari6/Arabic-twitter-corpus-AJGT", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["Negative", "Positive"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ajgt_twitter_ar", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 175424, "num_examples": 1800, "dataset_name": "ajgt_twitter_ar"}}, "download_checksums": {"https://raw.githubusercontent.com/komari6/Arabic-twitter-corpus-AJGT/master/AJGT.xlsx": {"num_bytes": 107395, "checksum": "966c52213872b6b8a3ced5fb7c60aee2abf47ca673c7d2c2eeb064a60bc9ed51"}}, "download_size": 107395, "post_processing_size": null, "dataset_size": 175424, "size_in_bytes": 282819}}
dummy/plain_text/1.0.0/dummy_data.zip ADDED
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