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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", ""),
}
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