Datasets:
multi_nli_mismatch

	# 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
	"""The Multi-Genre NLI Corpus."""


	import os

	import datasets


	_CITATION = """\
	@InProceedings{N18-1101,
	author = {Williams, Adina
	and Nangia, Nikita
	and Bowman, Samuel},
	title = {A Broad-Coverage Challenge Corpus for
	Sentence Understanding through Inference},
	booktitle = {Proceedings of the 2018 Conference of
	the North American Chapter of the
	Association for Computational Linguistics:
	Human Language Technologies, Volume 1 (Long
	Papers)},
	year = {2018},
	publisher = {Association for Computational Linguistics},
	pages = {1112--1122},
	location = {New Orleans, Louisiana},
	url = {http://aclweb.org/anthology/N18-1101}
	}
	"""

	_DESCRIPTION = """\
	The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
	crowd-sourced collection of 433k sentence pairs annotated with textual
	entailment information. The corpus is modeled on the SNLI corpus, but differs in
	that covers a range of genres of spoken and written text, and supports a
	distinctive cross-genre generalization evaluation. The corpus served as the
	basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
	"""

	ROOT_URL = "http://storage.googleapis.com/tfds-data/downloads/multi_nli/multinli_1.0.zip"


	class MultiNLIMismatchConfig(datasets.BuilderConfig):
	"""BuilderConfig for MultiNLI Mismatch."""

	def __init__(self, **kwargs):
	"""BuilderConfig for MultiNLI Mismatch.

	Args:

	**kwargs: keyword arguments forwarded to super.
	"""
	super(MultiNLIMismatchConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class MultiNliMismatch(datasets.GeneratorBasedBuilder):
	"""MultiNLI: The Stanford Question Answering Dataset. Version 1.1."""

	BUILDER_CONFIGS = [
	MultiNLIMismatchConfig(
	name="plain_text",
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both premise
	# and hypothesis as input).
	supervised_keys=None,
	homepage="https://www.nyu.edu/projects/bowman/multinli/",
	citation=_CITATION,
	)

	def _vocab_text_gen(self, filepath):
	for _, ex in self._generate_examples(filepath):
	yield " ".join([ex["premise"], ex["hypothesis"], ex["label"]])

	def _split_generators(self, dl_manager):

	downloaded_dir = dl_manager.download_and_extract(ROOT_URL)
	mnli_path = os.path.join(downloaded_dir, "multinli_1.0")
	train_path = os.path.join(mnli_path, "multinli_1.0_train.txt")

	validation_path = os.path.join(mnli_path, "multinli_1.0_dev_mismatched.txt")

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
	]

	def _generate_examples(self, filepath):
	"""Generate mnli mismatch examples.

	Args:
	filepath: a string

	Yields:
	dictionaries containing "premise", "hypothesis" and "label" strings
	"""
	for idx, line in enumerate(open(filepath, "rb")):
	if idx == 0:
	continue
	line = line.strip().decode("utf-8")
	split_line = line.split("\t")
	yield idx, {"premise": split_line[5], "hypothesis": split_line[6], "label": split_line[0]}