Datasets:

GroNLP
/

ik-nlp-22_transqe

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""Dutch translation of the e-SNLI corpus with added quality estimation scores"""


	import csv
	csv.register_dialect("tsv", delimiter="\t")

	import datasets


	_CITATION = """
	@incollection{NIPS2018_8163,
	title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
	author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
	booktitle = {Advances in Neural Information Processing Systems 31},
	editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
	pages = {9539--9549},
	year = {2018},
	publisher = {Curran Associates, Inc.},
	url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
	}
	"""

	_DESCRIPTION = """
	The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
	include human-annotated natural language explanations of the entailment
	relations. This version includes an automatic translation to Dutch and two quality estimation annotations
	for each translated field.
	"""

	_HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"

	_URLS = {
	"train": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/train.tsv.gz",
	"validation": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/validation.tsv.gz",
	"test": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/test.tsv.gz",
	}

	class IkNlp22ExpNLIConfig(datasets.GeneratorBasedBuilder):
	"""e-SNLI corpus with added translation and quality estimation scores"""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text",
	version=datasets.Version("0.0.2"),
	description="Plain text import of e-SNLI",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"premise_en": datasets.Value("string"),
	"premise_nl": datasets.Value("string"),
	"hypothesis_en": datasets.Value("string"),
	"hypothesis_nl": datasets.Value("string"),
	"label": datasets.Value("int32"),
	"explanation_1_en": datasets.Value("string"),
	"explanation_1_nl": datasets.Value("string"),
	"explanation_2_en": datasets.Value("string"),
	"explanation_2_nl": datasets.Value("string"),
	"explanation_3_en": datasets.Value("string"),
	"explanation_3_nl": datasets.Value("string"),
	"da_premise": datasets.Value("string"),
	"mqm_premise": datasets.Value("string"),
	"da_hypothesis": datasets.Value("string"),
	"mqm_hypothesis": datasets.Value("string"),
	"da_explanation_1": datasets.Value("string"),
	"mqm_explanation_1": datasets.Value("string"),
	"da_explanation_2": datasets.Value("string"),
	"mqm_explanation_2": datasets.Value("string"),
	"da_explanation_3": datasets.Value("string"),
	"mqm_explanation_3": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	files = dl_manager.download_and_extract(_URLS)
	return [
	datasets.SplitGenerator(
	name=name,
	gen_kwargs={"filepath": filepath},
	)
	for name, filepath in files.items()
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f, dialect="tsv")
	for i, row in enumerate(reader):
	yield i, {
	"premise_en": row["premise_en"],
	"premise_nl": row["premise_nl"],
	"hypothesis_en": row["hypothesis_en"],
	"hypothesis_nl": row["hypothesis_nl"],
	"label": row["label"],
	"explanation_1_en": row["explanation_1_en"],
	"explanation_1_nl": row["explanation_1_nl"],
	"explanation_2_en": row.get("explanation_2_en", ""),
	"explanation_2_nl": row.get("explanation_2_nl", ""),
	"explanation_3_en": row.get("explanation_3_en", ""),
	"explanation_3_nl": row.get("explanation_3_nl", ""),
	"da_premise": row["da_premise"],
	"mqm_premise": row["mqm_premise"],
	"da_hypothesis": row["da_hypothesis"],
	"mqm_hypothesis": row["mqm_hypothesis"],
	"da_explanation_1": row["da_explanation_1"],
	"mqm_explanation_1": row["mqm_explanation_1"],
	"da_explanation_2": row.get("da_explanation_2", ""),
	"mqm_explanation_2": row.get("mqm_explanation_2", ""),
	"da_explanation_3": row.get("da_explanation_3", ""),
	"mqm_explanation_3": row.get("mqm_explanation_3", ""),
	}