Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
File size: 5,145 Bytes
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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
"""The Multi-Genre NLI Corpus."""
import json
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.
"""
class MultiNli(datasets.GeneratorBasedBuilder):
"""MultiNLI: The Stanford Question Answering Dataset. Version 1.1."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"promptID": datasets.Value("int32"),
"pairID": datasets.Value("string"),
"premise": datasets.Value("string"),
"premise_binary_parse": datasets.Value("string"), # parses in unlabeled binary-branching format
"premise_parse": datasets.Value("string"), # sentence as parsed by the Stanford PCFG Parser 3.5.2
"hypothesis": datasets.Value("string"),
"hypothesis_binary_parse": datasets.Value("string"), # parses in unlabeled binary-branching format
"hypothesis_parse": datasets.Value(
"string"
), # sentence as parsed by the Stanford PCFG Parser 3.5.2
"genre": 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://www.nyu.edu/projects/bowman/multinli/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_dir = dl_manager.download_and_extract("https://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip")
mnli_path = os.path.join(downloaded_dir, "multinli_1.0")
train_path = os.path.join(mnli_path, "multinli_1.0_train.jsonl")
matched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_matched.jsonl")
mismatched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_mismatched.jsonl")
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name="validation_matched", gen_kwargs={"filepath": matched_validation_path}),
datasets.SplitGenerator(name="validation_mismatched", gen_kwargs={"filepath": mismatched_validation_path}),
]
def _generate_examples(self, filepath):
"""Generate mnli examples"""
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
if data["gold_label"] == "-":
continue
yield id_, {
"promptID": data["promptID"],
"pairID": data["pairID"],
"premise": data["sentence1"],
"premise_binary_parse": data["sentence1_binary_parse"],
"premise_parse": data["sentence1_parse"],
"hypothesis": data["sentence2"],
"hypothesis_binary_parse": data["sentence2_binary_parse"],
"hypothesis_parse": data["sentence2_parse"],
"genre": data["genre"],
"label": data["gold_label"],
}
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