Datasets:
Upload yelp_dataset.py
Browse files- yelp_dataset.py +219 -0
yelp_dataset.py
ADDED
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# -*- coding: utf-8 -*-
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"""yelp_dataset.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/14UtK4YCjMSx4cVbUb9NBRHviWZg07dtY
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"""
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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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# TODO: Address all TODOs and remove all explanatory comments
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"""TODO: Add a description here."""
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!pip install datasets
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import csv
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import json
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import os
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from typing import List
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import datasets
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import logging
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {A great new dataset},
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author={huggingface, Inc.
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},
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year={2020}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = "https://www.yelp.com/dataset/download"
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URL = "https://yelpdata.s3.us-west-2.amazonaws.com/"
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_URLS = {
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"train": _URL + "yelp_train.csv",
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"test": _URL + "yelp_test.csv",
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}
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class YelpDataset(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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_URLS = _URLS
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VERSION = datasets.Version("1.1.0")
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def _info(self):
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raise ValueError('woops!')
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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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"business_id": datasets.Value("string"),
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"name": datasets.Value("string"),
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"address": datasets.Value("string"),
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"city": datasets.Value("string"),
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"state": datasets.Value("string"),
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"postal_code": datasets.Value("string"),
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"latitude": datasets.Value("float64"),
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"longitude": datasets.Value("float64"),
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"stars_x": datasets.Value("float64"),
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"review_count": datasets.Value("int64"),
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"is_open": datasets.Value("int64"),
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"attributes": datasets.features.Sequence(
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{ "RestaurantsDelivery":datasets.Value("boolean"),
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"OutdoorSeating":datasets.Value("boolean"),
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"BusinessAcceptsCreditCards":datasets.Value("boolean"),
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"BusinessParking": datasets.features.Sequence(
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{'garage':datasets.Value("boolean"),
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'street':datasets.Value("boolean"),
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'validated':datasets.Value("boolean"),
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'lot':datasets.Value("boolean"),
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'valet':datasets.Value("boolean")}),
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"BikeParking":datasets.Value("boolean"),
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"RestaurantsPriceRange2":datasets.Value("int64"),
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"RestaurantsTakeOut":datasets.Value("boolean"),
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"ByAppointmentOnly":datasets.Value("boolean"),
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"WiFi":datasets.Value("string"),
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"Alcohol":datasets.Value("string"),
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"Caters":datasets.Value("boolean"),
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'Corkage':datasets.Value("boolean"),
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'WheelchairAccessible':datasets.Value("boolean"),
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'HasTV':datasets.Value("boolean"),
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'Open24Hours':datasets.Value("boolean"),
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'BikeParking':datasets.Value("boolean"),
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'Ambience': datasets.features.Sequence(
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{'touristy': datasets.Value("boolean"),
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'hipster': datasets.Value("boolean"),
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'romantic': datasets.Value("boolean"),
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'divey': datasets.Value("boolean"),
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'intimate': datasets.Value("boolean"),
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'trendy': datasets.Value("boolean"),
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'upscale': datasets.Value("boolean"),
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'classy': datasets.Value("boolean"),
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'casual': datasets.Value("boolean")}),
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'RestaurantsAttire': datasets.Value("string"),
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'DriveThru':datasets.Value("boolean"),
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'BusinessAcceptsBitcoin':datasets.Value("boolean"),
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'NoiseLevel': datasets.Value("string"),
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'Smoking': datasets.Value("string"),
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'BestNights':datasets.features.Sequence(
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{u'monday': datasets.Value("boolean"),
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u'tuesday': datasets.Value("boolean"),
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u'wednesday': datasets.Value("boolean"),
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u'thursday': datasets.Value("boolean"),
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u'friday': datasets.Value("boolean"),
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u'saturday': datasets.Value("boolean"),
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u'sunday': datasets.Value("boolean")}),
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'GoodForMeal':datasets.features.Sequence(
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{'dessert': datasets.Value("boolean"),
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'latenight': datasets.Value("boolean"),
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'lunch': datasets.Value("boolean"),
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'dinner': datasets.Value("boolean"),
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'brunch': datasets.Value("boolean"),
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'breakfast': datasets.Value("boolean")}),
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'RestaurantsGoodForGroups':datasets.Value("boolean"),
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'GoodForDancing':datasets.Value("boolean"),
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'Music':datasets.features.Sequence(
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{'dj': datasets.Value("boolean"),
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'background_music': datasets.Value("boolean"),
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'no_music': datasets.Value("boolean"),
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'jukebox': datasets.Value("boolean"),
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'live': datasets.Value("boolean"),
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'video': datasets.Value("boolean"),
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'karaoke': datasets.Value("boolean")}),
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'DietaryRestrictions':datasets.features.Sequence(
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{'dairy-free': datasets.Value("boolean"),
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'gluten-free': datasets.Value("boolean"),
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'vegan': datasets.Value("boolean"),
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'kosher': datasets.Value("boolean"),
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'halal': datasets.Value("boolean"),
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'soy-free': datasets.Value("boolean"),
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'vegetarian': datasets.Value("boolean")}),
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'RestaurantsReservations':datasets.Value("boolean"),
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'HairSpecializesIn':datasets.features.Sequence(
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{'straightperms': datasets.Value("boolean"),
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'coloring': datasets.Value("boolean"),
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'extensions': datasets.Value("boolean"),
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'africanamerican': datasets.Value("boolean"),
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'curly': datasets.Value("boolean"),
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'kids': datasets.Value("boolean"),
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'perms': datasets.Value("boolean"),
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'asian': datasets.Value("boolean")}),
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'BYOBCorkage': datasets.Value("string"),
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'BYOB':datasets.Value("boolean"),
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'DogsAllowed':datasets.Value("boolean"),
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'RestaurantsCounterService':datasets.Value("boolean"),
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'RestaurantsTableService':datasets.Value("boolean"),
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'CoatCheck':datasets.Value("boolean"),
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'AgesAllowed': datasets.Value("string"),
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'AcceptsInsurance':datasets.Value("boolean"),
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'HappyHour':datasets.Value("boolean"),
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'GoodForKids':datasets.Value("boolean"),
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}
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),
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"categories": datasets.Value("string"),
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"hours": datasets.Value("string"),
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"review_id": datasets.Value("string"),
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"user_id": datasets.Value("string"),
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"stars_y": datasets.Value("float64"),
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"useful": datasets.Value("int64"),
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"funny": datasets.Value("int64"),
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"cool": datasets.Value("int64"),
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"text": datasets.Value("string"),
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"date": datasets.Value("string"),
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}
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),
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# No default supervised_keys (as we have to pass both question
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# and context as input).
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supervised_keys=None,
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homepage="https://www.yelp.com/dataset/download",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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urls_to_download = self._URLS
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downloaded_files = dl_manager.download_and_extract(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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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
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]
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def _generate_examples(self, filepath):
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"""This function returns the examples in the raw (text) form."""
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logging.info("generating examples from = %s", filepath)
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with open(filepath, encoding="utf-8") as csv_file:
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reader = csv.DictReader(csv_file)
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for i, row in enumerate(reader):
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# Convert the row to a dictionary, removing any null values
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example = {key: value for key, value in row.items() if value is not None}
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yield i, example
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