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ldkp3k / ldkp3k.py

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Update ldkp3k.py

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	import csv
	import json
	import os

	import datasets
	from typing import List, Any

	# _SPLIT = ['train', 'test', 'valid']
	_CITATION = """\
	TBA
	"""


	_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": ["data/test.jsonl"],
	"train": ["train.jsonl"],
	"valid": ["data/valid.jsonl"],

	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class LDKP3k(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="small", version=VERSION, description="This part of my dataset covers long document"),
	datasets.BuilderConfig(name="medium", version=VERSION, description="This part of my dataset covers abstract only"),
	datasets.BuilderConfig(name="large", version=VERSION, description="This part of my dataset covers abstract only")


	]

	DEFAULT_CONFIG_NAME = "small"

	def _info(self):
	#print(os.listdir())
	#_URLS['train']=[os.path.join('data/'+self.config.name,filename) for filename in os.listdir('data/'+self.config.name+"/") if filename.startswith('train') and filename.endswith('.jsonl')]
	_URLS['train']=["data/"+self.config.name+"/train.jsonl"]
	if self.config.name =='large':
	_URLS['train']+= ["data/"+self.config.name+"/train_"+str(x)+".jsonl" for x in range(1,5)]

	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"sections": datasets.features.Sequence(datasets.Value("string")),
	"sec_text": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
	"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
	"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
	"sec_bio_tags": datasets.features.Sequence(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):
	print(os.listdir())
	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={
	"filepaths": data_dir['train'],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepaths": data_dir['test'],
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepaths": data_dir['valid'],
	"split": "valid",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepaths, split):
	for filepath in filepaths:
	with open(filepath, encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	yield key, {
	"id": data['paper_id'],
	"sections": data["sections"],
	"sec_text": data["sec_text"],
	"extractive_keyphrases": data["extractive_keyphrases"],
	"abstractive_keyphrases": data["abstractive_keyphrases"],
	"sec_bio_tags": data["sec_bio_tags"]
	}