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	import json
	import datasets

	# _SPLIT = ['train', 'test', 'valid']
	_CITATION = """\
	@inproceedings{10.1145/313238.313437,
	author = {Witten, Ian H. and Paynter, Gordon W. and Frank, Eibe and Gutwin, Carl and Nevill-Manning, Craig G.},
	title = {KEA: Practical Automatic Keyphrase Extraction},
	year = {1999},
	isbn = {1581131453},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/313238.313437},
	doi = {10.1145/313238.313437},
	booktitle = {Proceedings of the Fourth ACM Conference on Digital Libraries},
	pages = {254–255},
	numpages = {2},
	location = {Berkeley, California, USA},
	series = {DL '99}
	}
	"""

	_DESCRIPTION = """\

	"""

	_HOMEPAGE = ""

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

	# TODO: Add link to the official dataset URLs here

	_URLS = {
	"test": "test.jsonl",
	"train": "train.jsonl"
	}


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

	VERSION = datasets.Version("0.0.1")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="extraction", version=VERSION,
	description="This part of my dataset covers extraction"),
	datasets.BuilderConfig(name="generation", version=VERSION,
	description="This part of my dataset covers generation"),
	datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
	]

	DEFAULT_CONFIG_NAME = "extraction"

	def _info(self):
	if self.config.name == "extraction": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"id": datasets.Value("int64"),
	"document": datasets.features.Sequence(datasets.Value("string")),
	"doc_bio_tags": datasets.features.Sequence(datasets.Value("string"))

	}
	)
	elif self.config.name == "generation":
	features = datasets.Features(
	{
	"id": datasets.Value("int64"),
	"document": datasets.features.Sequence(datasets.Value("string")),
	"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
	"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string"))

	}
	)
	else:
	features = datasets.Features(
	{
	"id": datasets.Value("int64"),
	"document": datasets.features.Sequence(datasets.Value("string")),
	"doc_bio_tags": datasets.features.Sequence(datasets.Value("string")),
	"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
	"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
	"other_metadata": datasets.features.Sequence(
	{
	"text": datasets.features.Sequence(datasets.Value("string")),
	"bio_tags": 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,
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):

	data_dir = dl_manager.download_and_extract(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir['train'],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir['test'],
	"split": "test"
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	if self.config.name == "extraction":
	# Yields examples as (key, example) tuples
	yield key, {
	"id": data['paper_id'],
	"document": data["document"],
	"doc_bio_tags": data.get("doc_bio_tags")
	}
	elif self.config.name == "generation":
	yield key, {
	"id": data['paper_id'],
	"document": data["document"],
	"extractive_keyphrases": data.get("extractive_keyphrases"),
	"abstractive_keyphrases": data.get("abstractive_keyphrases")
	}
	else:
	yield key, {
	"id": data['paper_id'],
	"document": data["document"],
	"doc_bio_tags": data.get("doc_bio_tags"),
	"extractive_keyphrases": data.get("extractive_keyphrases"),
	"abstractive_keyphrases": data.get("abstractive_keyphrases"),
	"other_metadata": data["other_metadata"]
	}