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  1. the-reddit-covid-dataset.py +182 -0
the-reddit-covid-dataset.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 SocialGrep dataset loader base."""
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+
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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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+ DATASET_NAME = "the-reddit-covid-dataset"
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+ DATASET_TITLE = "the-reddit-covid-dataset"
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+
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+ DATASET_DESCRIPTION = """\
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+ This dataset attempts to capture the full extent of COVID-19 discussion across the entire site of Reddit. All posts and comments found to mention the term 'COVID' as of 2021-10-25 have been gathered from the site.
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+ """
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+
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+ _HOMEPAGE = f"https://socialgrep.com/datasets/{DATASET_NAME}"
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+
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+ _LICENSE = "CC-BY v4.0"
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+
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+ URL_TEMPLATE = "https://exports.socialgrep.com/download/public/{dataset_file}.zip"
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+ DATASET_FILE_TEMPLATE = "{dataset}-{type}.csv"
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+
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+ _DATASET_FILES = {
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+ 'posts': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="posts"),
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+ 'comments': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="comments"),
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+ }
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+
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+ _CITATION = f"""\
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+ @misc{{socialgrep:{DATASET_NAME},
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+ title = {{{DATASET_TITLE}}},
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+ author={{Lexyr Inc.
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+ }},
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+ year={{2022}}
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+ }}
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+ """
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+
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+
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+ class theredditcoviddataset(datasets.GeneratorBasedBuilder):
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+ VERSION = datasets.Version("1.0.0")
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+
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+ # This is an example of a dataset with multiple configurations.
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+ # If you don't want/need to define several sub-sets in your dataset,
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+ # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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+
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+ # If you need to make complex sub-parts in the datasets with configurable options
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+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
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+
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+ # You will be able to load one or the other configurations in the following list with
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+ # data = datasets.load_dataset('my_dataset', 'first_domain')
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+ # data = datasets.load_dataset('my_dataset', 'second_domain')
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(name="posts", version=VERSION, description="The dataset posts."),
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+ datasets.BuilderConfig(name="comments", version=VERSION, description="The dataset comments."),
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+ ]
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+
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+ def _info(self):
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+ if self.config.name == "posts": # This is the name of the configuration selected in BUILDER_CONFIGS above
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+ features = datasets.Features(
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+ {
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+ "type": datasets.Value("string"),
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+ "id": datasets.Value("string"),
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+ "subreddit.id": datasets.Value("string"),
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+ "subreddit.name": datasets.Value("string"),
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+ "subreddit.nsfw": datasets.Value("bool"),
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+ "created_utc": datasets.Value("timestamp[s,tz=utc]"),
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+ "permalink": datasets.Value("string"),
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+ "domain": datasets.Value("string"),
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+ "url": datasets.Value("string"),
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+ "selftext": datasets.Value("large_string"),
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+ "title": datasets.Value("string"),
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+ "score": datasets.Value("int32"),
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+ }
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+ )
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+ else: # This is an example to show how to have different features for "first_domain" and "second_domain"
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+ features = datasets.Features(
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+ {
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+ "type": datasets.ClassLabel(num_classes=2, names=['post', 'comment']),
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+ "id": datasets.Value("string"),
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+ "subreddit.id": datasets.Value("string"),
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+ "subreddit.name": datasets.Value("string"),
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+ "subreddit.nsfw": datasets.Value("bool"),
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+ "created_utc": datasets.Value("timestamp[s,tz=utc]"),
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+ "permalink": datasets.Value("string"),
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+ "body": datasets.Value("large_string"),
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+ "sentiment": datasets.Value("float32"),
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+ "score": datasets.Value("int32"),
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=DATASET_DESCRIPTION,
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+ # This defines the different columns of the dataset and their types
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+ features=features, # Here we define them above because they are different between the two configurations
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+ # If there's a common (input, target) tuple from the features,
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+ # specify them here. They'll be used if as_supervised=True in
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+ # builder.as_dataset.
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage=_HOMEPAGE,
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+ # License for the dataset if available
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+ license=_LICENSE,
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+ # Citation for the dataset
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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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+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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+
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+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
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+ # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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+ my_urls = [URL_TEMPLATE.format(dataset_file=_DATASET_FILES[self.config.name])]
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+ data_dir = dl_manager.download_and_extract(my_urls)[0]
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir, _DATASET_FILES[self.config.name]),
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+ "split": "train",
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+ },
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+ )
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+ ]
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+
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+ def _generate_examples(
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+ self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ ):
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+ """ Yields examples as (key, example) tuples. """
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+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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+ bool_cols = ["subreddit.nsfw"]
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+ int_cols = ["score", "created_utc"]
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+ float_cols = ["sentiment"]
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+
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+ with open(filepath, encoding="utf-8") as f:
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+ reader = csv.DictReader(f)
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+ for row in reader:
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+ for col in bool_cols:
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+ if col in row:
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+ if row[col]:
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+ row[col] = (row[col] == "true")
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+ else:
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+ row[col] = None
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+ for col in int_cols:
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+ if col in row:
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+ if row[col]:
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+ row[col] = int(row[col])
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+ else:
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+ row[col] = None
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+ for col in float_cols:
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+ if col in row:
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+ if row[col]:
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+ row[col] = float(row[col])
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+ else:
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+ row[col] = None
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+
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+ if row["type"] == "post":
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+ key = f"t3_{row['id']}"
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+ if row["type"] == "comment":
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+ key = f"t1_{row['id']}"
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+ yield key, row
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+
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+
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+ if __name__ == "__main__":
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+ print("Please use the HuggingFace dataset library, or")
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+ print("download from https://socialgrep.com/datasets.")