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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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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - crowdsourced
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+ language_creators:
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+ - crowdsourced
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+ languages:
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+ - en
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+ licenses:
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+ - apache-2-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - n>1M
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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 amazon_polarity
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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:** https://registry.opendata.aws/
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+ - **Repository:** https://github.com/zhangxiangxiao/Crepe
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+ - **Paper:** https://arxiv.org/abs/1509.01626
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+ - **Leaderboard:** [Needs More Information]
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+ - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
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+
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+ ### Dataset Summary
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+
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+ The Amazon reviews dataset consists of reviews from amazon.
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+ The data span a period of 18 years, including ~35 million reviews up to March 2013.
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+ Reviews include product and user information, ratings, and a plaintext review.
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating.
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+
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+ ### Languages
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+
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+ Mainly English.
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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 of a title, a content and the corresponding label.
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+
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+ An example from the AmazonPolarity test set looks as follows:
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+
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+ ```
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+ {
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+ 'title':'Great CD',
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+ 'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""",
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+ 'label':1
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+ }
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+ ```
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+
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+ ### Data Fields
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+
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+ - 'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
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+ - 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
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+ - 'label': either 1 (positive) or 0 (negative) rating.
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+
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+ ### Data Splits
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+
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+ The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples.
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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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+ The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
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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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+ [Needs More Information]
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+
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+ #### Who are the source language producers?
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+
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+ [Needs More Information]
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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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+ [Needs More Information]
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+
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+ #### Who are the annotators?
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+
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+ [Needs More Information]
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+
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+ ### Personal and Sensitive Information
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+
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+ [Needs More Information]
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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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+ [Needs More Information]
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+
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+ ### Discussion of Biases
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+
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+ [Needs More Information]
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+
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+ ### Other Known Limitations
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+
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+ [Needs More Information]
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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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+ [Needs More Information]
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+
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+ ### Licensing Information
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+
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+ Apache License 2.0
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+
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+ ### Citation Information
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+
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+ McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
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+
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+ Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)
amazon_polarity.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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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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+ """The amazon polarity dataset for text classification."""
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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 csv
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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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+ _CITATION = """\
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+ @inproceedings{mcauley2013hidden,
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+ title={Hidden factors and hidden topics: understanding rating dimensions with review text},
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+ author={McAuley, Julian and Leskovec, Jure},
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+ booktitle={Proceedings of the 7th ACM conference on Recommender systems},
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+ pages={165--172},
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+ year={2013}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The Amazon reviews dataset consists of reviews from amazon.
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+ The data span a period of 18 years, including ~35 million reviews up to March 2013.
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+ Reviews include product and user information, ratings, and a plaintext review.
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+ """
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+
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+ _HOMEPAGE = "https://registry.opendata.aws/"
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+
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+ _LICENSE = "Apache License 2.0"
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+
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+ _URLs = {
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+ "amazon_polarity": "https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download",
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+ }
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+
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+
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+ class AmazonPolarityConfig(datasets.BuilderConfig):
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+ """BuilderConfig for AmazonPolarity."""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for AmazonPolarity.
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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(AmazonPolarityConfig, self).__init__(**kwargs)
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+
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+
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+ class AmazonPolarity(datasets.GeneratorBasedBuilder):
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+ """Amazon Polarity Classification Dataset."""
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+
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+ VERSION = datasets.Version("3.0.0")
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+
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+ BUILDER_CONFIGS = [
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+ AmazonPolarityConfig(
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+ name="amazon_polarity", version=VERSION, description="Amazon Polarity Classification Dataset."
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+ ),
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+ ]
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+
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+ def _info(self):
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+ features = datasets.Features(
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+ {
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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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+ "title": datasets.Value("string"),
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+ "content": datasets.Value("string"),
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=features,
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+ supervised_keys=None,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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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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+ """Returns SplitGenerators."""
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+ my_urls = _URLs[self.config.name]
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+ data_dir = dl_manager.download_and_extract(my_urls)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir, "amazon_review_polarity_csv", "train.csv"),
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+ "split": "train",
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir, "amazon_review_polarity_csv", "test.csv"),
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+ "split": "test",
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath, split):
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+ """ Yields examples. """
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+
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+ with open(filepath, encoding="utf-8") as f:
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+ data = csv.reader(f, delimiter=",", quoting=csv.QUOTE_ALL)
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+ for id_, row in enumerate(data):
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+ yield id_, {
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+ "title": row[1],
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+ "content": row[2],
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+ "label": int(row[0]) - 1,
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+ }
dataset_infos.json ADDED
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+ {"amazon_polarity": {"description": "The Amazon reviews dataset consists of reviews from amazon.\nThe data span a period of 18 years, including ~35 million reviews up to March 2013.\nReviews include product and user information, ratings, and a plaintext review.\n", "citation": "@inproceedings{mcauley2013hidden,\n title={Hidden factors and hidden topics: understanding rating dimensions with review text},\n author={McAuley, Julian and Leskovec, Jure},\n booktitle={Proceedings of the 7th ACM conference on Recommender systems},\n pages={165--172},\n year={2013}\n}\n", "homepage": "https://registry.opendata.aws/", "license": "Apache License 2.0", "features": {"label": {"num_classes": 2, "names": ["negative", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "amazon_polarity", "config_name": "amazon_polarity", "version": {"version_str": "3.0.0", "description": null, "major": 3, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1604367312, "num_examples": 3600000, "dataset_name": "amazon_polarity"}, "test": {"name": "test", "num_bytes": 178176513, "num_examples": 400000, "dataset_name": "amazon_polarity"}}, "download_checksums": {"https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download": {"num_bytes": 688340758, "checksum": "119dd30cb8226c1df21b009bd02cda3ef09b625a80864e454371904e6d2eaee4"}}, "download_size": 688340758, "post_processing_size": null, "dataset_size": 1782543825, "size_in_bytes": 2470884583}}
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