Datasets:
bprec

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Brand-Product Relation Extraction Corpora"""


	import json

	import datasets


	# DONE: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{inproceedings,
	author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
	year = {2020},
	month = {05},
	pages = {},
	title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
	}
	"""

	# DONE: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	Dataset consisting of Polish language texts annotated to recognize brand-product relations.
	"""

	# DONE: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/736"

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

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLs = {
	"tele": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_tele_export.json",
	"electro": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_electro_export.json",
	"cosmetics": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_cosmetics_export.json",
	"banking": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_banking_export.json",
	}

	_CATEGORIES = {
	"tele": "telecommunications",
	"electro": "electronics",
	"cosmetics": "cosmetics",
	"banking": "banking",
	}
	_ALL_CATEGORIES = "all"
	_VERSION = "1.1.0"


	class BprecConfig(datasets.BuilderConfig):
	"""BuilderConfig for BprecConfig."""

	def __init__(self, categories=None, **kwargs):
	super(BprecConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
	self.categories = categories


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class Bprec(datasets.GeneratorBasedBuilder):
	"""Brand-Product Relation Extraction Corpora in Polish"""

	BUILDER_CONFIGS = [
	BprecConfig(
	name=_ALL_CATEGORIES,
	categories=_CATEGORIES,
	description="A collection of Polish language texts annotated to recognize brand-product relations",
	)
	] + [
	BprecConfig(
	name=cat,
	categories=[cat],
	description=f"{_CATEGORIES[cat]} examples from a collection of Polish language texts annotated to recognize brand-product relations",
	)
	for cat in _CATEGORIES
	]
	BUILDER_CONFIG_CLASS = BprecConfig
	DEFAULT_CONFIG_NAME = _ALL_CATEGORIES

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"category": datasets.Value("string"),
	"text": datasets.Value("string"),
	"ner": datasets.features.Sequence(
	{
	"source": {
	"from": datasets.Value("int32"),
	"text": datasets.Value("string"),
	"to": datasets.Value("int32"),
	"type": datasets.features.ClassLabel(
	names=[
	"PRODUCT_NAME",
	"PRODUCT_NAME_IMP",
	"PRODUCT_NO_BRAND",
	"BRAND_NAME",
	"BRAND_NAME_IMP",
	"VERSION",
	"PRODUCT_ADJ",
	"BRAND_ADJ",
	"LOCATION",
	"LOCATION_IMP",
	]
	),
	},
	"target": {
	"from": datasets.Value("int32"),
	"text": datasets.Value("string"),
	"to": datasets.Value("int32"),
	"type": datasets.features.ClassLabel(
	names=[
	"PRODUCT_NAME",
	"PRODUCT_NAME_IMP",
	"PRODUCT_NO_BRAND",
	"BRAND_NAME",
	"BRAND_NAME_IMP",
	"VERSION",
	"PRODUCT_ADJ",
	"BRAND_ADJ",
	"LOCATION",
	"LOCATION_IMP",
	]
	),
	},
	}
	),
	}
	)
	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,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# 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):
	"""Returns SplitGenerators."""
	# 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
	_my_urls = [_URLs[cat] for cat in self.config.categories]

	downloaded_files = dl_manager.download_and_extract(_my_urls)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filedirs": downloaded_files}),
	]

	def _generate_examples(self, filedirs, split="tele"):
	"""Yields examples."""
	# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
	# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
	# The key is not important, it's more here for legacy reason (legacy from tfds)
	cats = [cat for cat in self.config.categories]
	for cat, filepath in zip(cats, filedirs):
	with open(filepath, "r", encoding="utf-8") as f:
	data = json.load(f)
	for key in data.keys():
	example = data[key]
	id_ = example.get("id")
	text = example.get("text")
	ner = example.get("ner")
	yield id_, {
	"id": id_,
	"category": cat,
	"text": text,
	"ner": ner,
	}