Datasets:

taln-ls2n
/

kpbiomed

	"""KPBiomed benchmark dataset for keyphrase extraction an generation."""


	import csv
	import json
	import os

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\

	"""

	# You can copy an official description
	_DESCRIPTION = """\
	KPBiomed benchmark dataset for keyphrase extraction an generation.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = ""

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "Apache 2.0 License"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"train_large": "train_large.jsonl",
	"train_medium" : "train_medium.jsonl",
	"train_small" : "train_small.jsonl",
	"val" : "val.jsonl",
	"test" : "test.jsonl"

	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class KPBiomed(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("0.0.1")

	# 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="large", version=VERSION, description="This part of my dataset covers the large training data."),
	datasets.BuilderConfig(name="medium", version=VERSION, description="This part of my dataset covers the medium training data."),
	datasets.BuilderConfig(name="small", version=VERSION, description="This part of my dataset covers the small training data."),

	]

	DEFAULT_CONFIG_NAME = "small" # 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 == "small": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"abstract": datasets.Value("string"),
	"authors": datasets.Value("string"),
	"mesh_terms": datasets.features.Sequence(datasets.Value("string")),
	"year": datasets.Value("string"),
	"keyphrases": datasets.features.Sequence(datasets.Value("string")),
	"prmu": datasets.features.Sequence(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
	urls = _URLS
	data_dir = dl_manager.download_and_extract(urls)
	if self.config.name == "large":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["train_large"]),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["test"]),
	"split": "test",
	},
	),

	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["val"]),
	"split": "test",
	},
	),

	]
	elif self.config.name == "medium":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["train_medium"]),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["test"]),
	"split": "test",
	},
	),

	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["val"]),
	"split": "test",
	},
	),

	]

	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["train_small"]),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["test"]),
	"split": "test",
	},
	),

	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["val"]),
	"split": "test",
	},
	),

	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# 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.
	with open(filepath, encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	# Yields examples as (key, example) tuples
	yield key, {
	"id": data["id"],
	"title": data["title"],
	"abstract": data["abstract"],
	"authors" : data["authors"],
	"mesh_terms" : data["mesh_terms"],
	"year" : data["year"],
	"keyphrases": data["keyphrases"],
	"prmu": data["prmu"],
	}