Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
ArXiv:
Tags:
License:
# 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 Stanford Natural Language Inference (SNLI) Corpus.""" | |
from __future__ import absolute_import, division, print_function | |
import csv | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{snli:emnlp2015, | |
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.}, | |
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, | |
Publisher = {Association for Computational Linguistics}, | |
Title = {A large annotated corpus for learning natural language inference}, | |
Year = {2015} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The SNLI corpus (version 1.0) is a collection of 570k human-written English | |
sentence pairs manually labeled for balanced classification with the labels | |
entailment, contradiction, and neutral, supporting the task of natural language | |
inference (NLI), also known as recognizing textual entailment (RTE). | |
""" | |
_DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip" | |
class Snli(datasets.GeneratorBasedBuilder): | |
"""The Stanford Natural Language Inference (SNLI) Corpus.""" | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="plain_text", | |
version=datasets.Version("1.0.0", ""), | |
description="Plain text import of 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"]), | |
} | |
), | |
# No default supervised_keys (as we have to pass both premise | |
# and hypothesis as input). | |
supervised_keys=None, | |
homepage="https://nlp.stanford.edu/projects/snli/", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
dl_dir = dl_manager.download_and_extract(_DATA_URL) | |
data_dir = os.path.join(dl_dir, "snli_1.0") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")} | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")} | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")} | |
), | |
] | |
def _generate_examples(self, filepath): | |
"""This function returns the examples in the raw (text) form.""" | |
with open(filepath, encoding="utf-8") as f: | |
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
for idx, row in enumerate(reader): | |
label = -1 if row["gold_label"] == "-" else row["gold_label"] | |
yield idx, { | |
"premise": row["sentence1"], | |
"hypothesis": row["sentence2"], | |
"label": label, | |
} | |