Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
found
Tags:
License:
system HF staff commited on
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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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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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+ - en
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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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+ - n>1M
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+ source_datasets:
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+ - extended|other-yahoo-answers-corpus
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+ task_categories:
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+ - text-classification
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+ task_ids:
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+ - topic-classification
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+ ---
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+
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+ # Dataset Card Creation Guide
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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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
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+ - **Repository:** https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
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+ - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
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+ - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
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+ - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
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+
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+ ### Dataset Summary
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+
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+ [More Information Needed]
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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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+ [More Information Needed]
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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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+ [More Information Needed]
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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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+ [More Information Needed]
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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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+ [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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+ [More Information Needed]
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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
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+
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+ [More Information Needed]
dataset_infos.json ADDED
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+ {"yahoo_answers_topics": {"description": "\nYahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content and the main category information.\n", "citation": "", "homepage": "https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "topic": {"num_classes": 10, "names": ["Society & Culture", "Science & Mathematics", "Health", "Education & Reference", "Computers & Internet", "Sports", "Business & Finance", "Entertainment & Music", "Family & Relationships", "Politics & Government"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_title": {"dtype": "string", "id": null, "_type": "Value"}, "question_content": {"dtype": "string", "id": null, "_type": "Value"}, "best_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yahoo_answers_topics", "config_name": "yahoo_answers_topics", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 760287375, "num_examples": 1400000, "dataset_name": "yahoo_answers_topics"}, "test": {"name": "test", "num_bytes": 32653934, "num_examples": 60000, "dataset_name": "yahoo_answers_topics"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU": {"num_bytes": 319476309, "checksum": "db51df05ca9f6652ef722e7168291a073c859b1ade3493b0ec74174ca0d777a0"}}, "download_size": 319476309, "post_processing_size": null, "dataset_size": 792941309, "size_in_bytes": 1112417618}}
dummy/yahoo_answers_topics/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:7be3aa2e8f6603c76da91c675e4e80de5008483c7b4d4458febdf395c3df1bcc
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+ size 4437
yahoo_answers_topics.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 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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+ """Yahoo! Answers Topic Classification Dataset"""
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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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+ _DESCRIPTION = """
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+ Yahoo! Answers Topic Classification is text classification dataset. \
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+ The dataset is the Yahoo! Answers corpus as of 10/25/2007. \
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+ The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. \
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+ From all the answers and other meta-information, this dataset only used the best answer content and the main category information.
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+ """
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+
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+ _URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU"
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+
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+ _TOPICS = [
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+ "Society & Culture",
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+ "Science & Mathematics",
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+ "Health",
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+ "Education & Reference",
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+ "Computers & Internet",
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+ "Sports",
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+ "Business & Finance",
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+ "Entertainment & Music",
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+ "Family & Relationships",
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+ "Politics & Government",
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+ ]
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+
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+
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+ class YahooAnswersTopics(datasets.GeneratorBasedBuilder):
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+ "Yahoo! Answers Topic Classification Dataset"
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+
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+ VERSION = datasets.Version("1.0.0")
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(
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+ name="yahoo_answers_topics",
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+ version=datasets.Version("1.0.0", ""),
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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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+ "id": datasets.Value("int32"),
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+ "topic": datasets.features.ClassLabel(names=_TOPICS),
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+ "question_title": datasets.Value("string"),
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+ "question_content": datasets.Value("string"),
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+ "best_answer": datasets.Value("string"),
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+ },
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+ ),
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+ supervised_keys=None,
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+ homepage="https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset",
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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(_URL)
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+
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+ # Extracting (un-taring) the training data
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+ data_dir = os.path.join(data_dir, "yahoo_answers_csv")
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")}
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")}
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ with open(filepath, encoding="utf-8") as f:
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+ rows = csv.reader(f)
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+ for i, row in enumerate(rows):
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+ yield i, {
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+ "id": i,
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+ "topic": int(row[0]) - 1,
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+ "question_title": row[1],
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+ "question_content": row[2],
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+ "best_answer": row[3],
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+ }