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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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_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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_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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_HOMEPAGE = "https://www.yelp.com/dataset/download" |
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_LICENSE = "" |
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import json |
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import datasets |
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class YelpDataset(datasets.GeneratorBasedBuilder): |
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"""Yelp Dataset focusing on restaurant reviews.""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="restaurants", version=VERSION, description="This part of my dataset covers a wide range of restaurants"), |
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] |
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DEFAULT_CONFIG_NAME = "restaurants" |
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_URL = "https://yelpdata.s3.us-west-2.amazonaws.com/" |
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_URLS = { |
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"business": _URL + "yelp_academic_dataset_business.json", |
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"review": _URL + "yelp_academic_dataset_review.json", |
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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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"business_id": datasets.Value("string"), |
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"name": datasets.Value("string"), |
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"categories": 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": datasets.Value("float"), |
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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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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): |
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"""Returns SplitGenerators.""" |
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downloaded_files = dl_manager.download_and_extract(self._URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"business_path": downloaded_files["business"], "review_path": downloaded_files["review"], "split": "train"}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"business_path": downloaded_files["business"], "review_path": downloaded_files["review"], "split": "test"}), |
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] |
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def _generate_examples(self, business_path, review_path, split): |
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"""Yields examples as (key, example) tuples.""" |
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with open(business_path, encoding="utf-8") as f: |
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businesses = {} |
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for line in f: |
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business = json.loads(line) |
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if business.get("categories") and "Restaurants" in business["categories"]: |
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businesses[business['business_id']] = business |
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with open(review_path, encoding="utf-8") as f: |
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for line in f: |
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review = json.loads(line) |
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business_id = review['business_id'] |
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if business_id in businesses: |
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yield review['review_id'], { |
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"business_id": business_id, |
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"name": businesses[business_id]['name'], |
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"categories": businesses[business_id]['categories'], |
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"review_id": review['review_id'], |
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"user_id": review['user_id'], |
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"stars": review['stars'], |
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"text": review['text'], |
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"date": review['date'], |
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} |
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