Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
crowdsourced
Annotations Creators:
lexyr
Source Datasets:
original
License:
cc-by-4.0
| # coding=utf-8 | |
| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """The SocialGrep dataset loader base.""" | |
| import csv | |
| import os | |
| import datasets | |
| DATASET_NAME = "the-reddit-nft-dataset" | |
| DATASET_TITLE = "the-reddit-nft-dataset" | |
| DATASET_DESCRIPTION = """\ | |
| A comprehensive dataset of Reddit's NFT discussion. | |
| """ | |
| _HOMEPAGE = f"https://socialgrep.com/datasets/{DATASET_NAME}" | |
| _LICENSE = "CC-BY v4.0" | |
| URL_TEMPLATE = "https://exports.socialgrep.com/download/public/{dataset_file}.zip" | |
| DATASET_FILE_TEMPLATE = "{dataset}-{type}.csv" | |
| _DATASET_FILES = { | |
| 'posts': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="posts"), | |
| 'comments': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="comments"), | |
| } | |
| _CITATION = f"""\ | |
| @misc{{socialgrep:{DATASET_NAME}, | |
| title = {{{DATASET_TITLE}}}, | |
| author={{Lexyr Inc. | |
| }}, | |
| year={{2022}} | |
| }} | |
| """ | |
| class theredditnftdataset(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name="posts", version=VERSION, description="The dataset posts."), | |
| datasets.BuilderConfig(name="comments", version=VERSION, description="The dataset comments."), | |
| ] | |
| def _info(self): | |
| if self.config.name == "posts": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features = datasets.Features( | |
| { | |
| "type": datasets.Value("string"), | |
| "id": datasets.Value("string"), | |
| "subreddit.id": datasets.Value("string"), | |
| "subreddit.name": datasets.Value("string"), | |
| "subreddit.nsfw": datasets.Value("bool"), | |
| "created_utc": datasets.Value("timestamp[s,tz=utc]"), | |
| "permalink": datasets.Value("string"), | |
| "domain": datasets.Value("string"), | |
| "url": datasets.Value("string"), | |
| "selftext": datasets.Value("large_string"), | |
| "title": datasets.Value("string"), | |
| "score": datasets.Value("int32"), | |
| } | |
| ) | |
| else: # This is an example to show how to have different features for "first_domain" and "second_domain" | |
| features = datasets.Features( | |
| { | |
| "type": datasets.ClassLabel(num_classes=2, names=['post', 'comment']), | |
| "id": datasets.Value("string"), | |
| "subreddit.id": datasets.Value("string"), | |
| "subreddit.name": datasets.Value("string"), | |
| "subreddit.nsfw": datasets.Value("bool"), | |
| "created_utc": datasets.Value("timestamp[s,tz=utc]"), | |
| "permalink": datasets.Value("string"), | |
| "body": datasets.Value("large_string"), | |
| "sentiment": datasets.Value("float32"), | |
| "score": datasets.Value("int32"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=DATASET_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, | |
| # specify them here. They'll be used if as_supervised=True in | |
| # builder.as_dataset. | |
| supervised_keys=None, | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
| # 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. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| my_urls = [URL_TEMPLATE.format(dataset_file=_DATASET_FILES[self.config.name])] | |
| data_dir = dl_manager.download_and_extract(my_urls)[0] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, _DATASET_FILES[self.config.name]), | |
| "split": "train", | |
| }, | |
| ) | |
| ] | |
| def _generate_examples( | |
| self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| ): | |
| """ Yields examples as (key, example) tuples. """ | |
| # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| bool_cols = ["subreddit.nsfw"] | |
| int_cols = ["score", "created_utc"] | |
| float_cols = ["sentiment"] | |
| with open(filepath, encoding="utf-8") as f: | |
| reader = csv.DictReader(f) | |
| for row in reader: | |
| for col in bool_cols: | |
| if col in row: | |
| if row[col]: | |
| row[col] = (row[col] == "true") | |
| else: | |
| row[col] = None | |
| for col in int_cols: | |
| if col in row: | |
| if row[col]: | |
| row[col] = int(row[col]) | |
| else: | |
| row[col] = None | |
| for col in float_cols: | |
| if col in row: | |
| if row[col]: | |
| row[col] = float(row[col]) | |
| else: | |
| row[col] = None | |
| if row["type"] == "post": | |
| key = f"t3_{row['id']}" | |
| if row["type"] == "comment": | |
| key = f"t1_{row['id']}" | |
| yield key, row | |
| if __name__ == "__main__": | |
| print("Please use the HuggingFace dataset library, or") | |
| print("download from https://socialgrep.com/datasets.") |