Datasets:

midas
/

inspec

	import csv
	import json
	import os

	import datasets
	from typing import List, Any

	# _SPLIT = ['train', 'test', 'valid']
	_CITATION = """\
	author: amardeep
	"""


	_DESCRIPTION = """\
	This new dataset is designed to solve kp NLP task and is crafted with a lot of care.
	"""


	_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",
	"valid": "valid.jsonl"
	}


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

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="extraction", version=VERSION, description="This part of my dataset covers long document"),
	datasets.BuilderConfig(name="generation", version=VERSION, description="This part of my dataset covers abstract only"),
	datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers abstract only"),
	datasets.BuilderConfig(name="ldkp_generation", version=VERSION, description="This part of my dataset covers abstract only"),
	datasets.BuilderConfig(name="ldkp_extraction", version=VERSION, description="This part of my dataset covers abstract only"),

	]

	DEFAULT_CONFIG_NAME = "extraction"

	def _info(self):
	if self.config.name == "extraction" or self.config.name == "ldkp_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" or self.config.name == "ldkp_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"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir['valid'],
	"split": "valid",
	},
	),
	]

	# 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["doc_bio_tags"]
	}
	elif self.config.name == "ldkp_extraction":
	yield key, {
	"id": data['paper_id'],
	"document": data["document"]+data["other_metadata"]['text'],
	"doc_bio_tags": data["document_tags"] + data["other_metadata"]['bio_tags']
	}
	elif self.config.name == "ldkp_generation":
	yield key, {
	"id": data['paper_id'],
	"document": data["document"]+data["other_metadata"]['text'],
	"extractive_keyphrases": data["extractive_keyphrases"],
	"abstractive_keyphrases": data["abstractive_keyphrases"]
	}
	elif self.config.name == "generation":
	yield key, {
	"id": data['paper_id'],
	"document": data["document"],
	"extractive_keyphrases": data["extractive_keyphrases"],
	"abstractive_keyphrases": data["abstractive_keyphrases"]
	}
	else:
	yield key, {
	"id": data['paper_id'],
	"document": data["document"],
	"doc_bio_tags": data["doc_bio_tags"],
	"extractive_keyphrases": data["extractive_keyphrases"],
	"abstractive_keyphrases": data["abstractive_keyphrases"],
	"other_metadata": data["other_metadata"]
	}