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esnli / esnli.py

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	# 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
	"""e-SNLI: Natural Language Inference with Natural Language Explanations."""


	import csv

	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.
	"""
	_URL = "https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/"


	class Esnli(datasets.GeneratorBasedBuilder):
	"""e-SNLI: Natural Language Inference with Natural Language Explanations corpus."""

	# Version History
	# 0.0.2 Added explanation_2, explanation_3 fields which exist in the dev/test
	# splits only.
	# 0.0.1 Initial version
	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": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
	"explanation_1": datasets.Value("string"),
	"explanation_2": datasets.Value("string"),
	"explanation_3": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/OanaMariaCamburu/e-SNLI",
	citation=_CITATION,
	)

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

	files = dl_manager.download_and_extract(
	{
	"train": [_URL + "esnli_train_1.csv", _URL + "esnli_train_2.csv"],
	"validation": [_URL + "esnli_dev.csv"],
	"test": [_URL + "esnli_test.csv"],
	}
	)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"files": files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"files": files["validation"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"files": files["test"]},
	),
	]

	def _generate_examples(self, files):
	"""Yields examples."""
	for filepath in files:
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f)
	for _, row in enumerate(reader):
	yield row["pairID"], {
	"premise": row["Sentence1"],
	"hypothesis": row["Sentence2"],
	"label": row["gold_label"],
	"explanation_1": row["Explanation_1"],
	"explanation_2": row.get("Explanation_2", ""),
	"explanation_3": row.get("Explanation_3", ""),
	}