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"""
Huggingface datasets script for Zenodo dataset.
"""
import json
import os
import datasets
_DESCRIPTION = """\
This dataset is for downloading a Zenodo dataset without extra packages.
"""
class NewDataset(datasets.GeneratorBasedBuilder):
"""This dataset downloads a zenodo dataset and returns its path."""
VERSION = datasets.Version("1.1.0")
# 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="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
# datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
# ]
# DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
# if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above
# features = datasets.Features(
# {
# "sentence": datasets.Value("string"),
# "option1": datasets.Value("string"),
# "answer": datasets.Value("string")
# # These are the features of your dataset like images, labels ...
# }
# )
# else: # This is an example to show how to have different features for "first_domain" and "second_domain"
# features = datasets.Features(
# {
# "sentence": datasets.Value("string"),
# "option2": datasets.Value("string"),
# "second_domain_answer": datasets.Value("string")
# # These are the features of your dataset like images, labels ...
# }
# )
features = datasets.Features(
{
"path": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_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, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# 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):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# 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
zenodo_id = self.config.name.split("/")[0]
filename = self.config.name.split("/")[1]
url = f"https://zenodo.org/record/{zenodo_id}/files/{filename}"
data_dir = dl_manager.download_and_extract([url])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
data_dir=data_dir[0],
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, data_dir):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
yield "path", data_dir
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