Datasets:
ronec

	# 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.

	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)

	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{dumitrescu2019introducing,
	title={Introducing RONEC--the Romanian Named Entity Corpus},
	author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius},
	journal={arXiv preprint arXiv:1909.01247},
	year={2019}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	RONEC - the Romanian Named Entity Corpus, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities. It is used for named entity recognition and represents the largest Romanian NER corpus to date.
	"""

	_HOMEPAGE = "https://github.com/dumitrescustefan/ronec"

	_LICENSE = "MIT License"

	# 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)
	_URL = "https://raw.githubusercontent.com/dumitrescustefan/ronec/master/data/"
	_TRAINING_FILE = "train.json"
	_DEV_FILE = "valid.json"
	_TEST_FILE = "test.json"


	class RONECConfig(datasets.BuilderConfig):
	"""BuilderConfig for RONEC dataset"""

	def __init__(self, **kwargs):
	super(RONECConfig, self).__init__(**kwargs)


	class RONEC(datasets.GeneratorBasedBuilder):
	"""RONEC dataset"""

	VERSION = datasets.Version("2.0.0")
	BUILDER_CONFIGS = [
	RONECConfig(name="ronec", version=VERSION, description="RONEC dataset"),
	]

	def _info(self):
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_ids": datasets.Sequence(datasets.Value("int32")),
	"space_after": datasets.Sequence(datasets.Value("bool")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-PERSON",
	"I-PERSON",
	"B-ORG",
	"I-ORG",
	"B-GPE",
	"I-GPE",
	"B-LOC",
	"I-LOC",
	"B-NAT_REL_POL",
	"I-NAT_REL_POL",
	"B-EVENT",
	"I-EVENT",
	"B-LANGUAGE",
	"I-LANGUAGE",
	"B-WORK_OF_ART",
	"I-WORK_OF_ART",
	"B-DATETIME",
	"I-DATETIME",
	"B-PERIOD",
	"I-PERIOD",
	"B-MONEY",
	"I-MONEY",
	"B-QUANTITY",
	"I-QUANTITY",
	"B-NUMERIC",
	"I-NUMERIC",
	"B-ORDINAL",
	"I-ORDINAL",
	"B-FACILITY",
	"I-FACILITY",
	]
	)
	),
	}
	)

	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."""

	urls_to_download = {"train": _URL + _TRAINING_FILE, "dev": _URL + _DEV_FILE, "test": _URL + _TEST_FILE}

	downloaded_files = dl_manager.download(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": downloaded_files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": downloaded_files["dev"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": downloaded_files["test"]},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""

	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, "r", encoding="utf-8") as f:
	data = json.load(f)
	for instance in data:
	yield instance["id"], instance