Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Multilinguality:
translation
Size Categories:
unknown
Annotations Creators:
expert-generated
Source Datasets:
extended|esnli
License:
File size: 6,165 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
"""Dutch translation of the e-SNLI corpus with added quality estimation scores"""
import csv
csv.register_dialect("tsv", delimiter="\t")
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. This version includes an automatic translation to Dutch and two quality estimation annotations
for each translated field.
"""
_HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"
_URLS = {
"train": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/train.tsv.gz",
"validation": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/validation.tsv.gz",
"test": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/test.tsv.gz",
}
class IkNlp22ExpNLIConfig(datasets.GeneratorBasedBuilder):
"""e-SNLI corpus with added translation and quality estimation scores"""
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_en": datasets.Value("string"),
"premise_nl": datasets.Value("string"),
"hypothesis_en": datasets.Value("string"),
"hypothesis_nl": datasets.Value("string"),
"label": datasets.Value("int32"),
"explanation_1_en": datasets.Value("string"),
"explanation_1_nl": datasets.Value("string"),
"explanation_2_en": datasets.Value("string"),
"explanation_2_nl": datasets.Value("string"),
"explanation_3_en": datasets.Value("string"),
"explanation_3_nl": datasets.Value("string"),
"da_premise": datasets.Value("string"),
"mqm_premise": datasets.Value("string"),
"da_hypothesis": datasets.Value("string"),
"mqm_hypothesis": datasets.Value("string"),
"da_explanation_1": datasets.Value("string"),
"mqm_explanation_1": datasets.Value("string"),
"da_explanation_2": datasets.Value("string"),
"mqm_explanation_2": datasets.Value("string"),
"da_explanation_3": datasets.Value("string"),
"mqm_explanation_3": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=name,
gen_kwargs={"filepath": filepath},
)
for name, filepath in files.items()
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, dialect="tsv")
for i, row in enumerate(reader):
yield i, {
"premise_en": row["premise_en"],
"premise_nl": row["premise_nl"],
"hypothesis_en": row["hypothesis_en"],
"hypothesis_nl": row["hypothesis_nl"],
"label": row["label"],
"explanation_1_en": row["explanation_1_en"],
"explanation_1_nl": row["explanation_1_nl"],
"explanation_2_en": row.get("explanation_2_en", ""),
"explanation_2_nl": row.get("explanation_2_nl", ""),
"explanation_3_en": row.get("explanation_3_en", ""),
"explanation_3_nl": row.get("explanation_3_nl", ""),
"da_premise": row["da_premise"],
"mqm_premise": row["mqm_premise"],
"da_hypothesis": row["da_hypothesis"],
"mqm_hypothesis": row["mqm_hypothesis"],
"da_explanation_1": row["da_explanation_1"],
"mqm_explanation_1": row["mqm_explanation_1"],
"da_explanation_2": row.get("da_explanation_2", ""),
"mqm_explanation_2": row.get("mqm_explanation_2", ""),
"da_explanation_3": row.get("da_explanation_3", ""),
"mqm_explanation_3": row.get("mqm_explanation_3", ""),
}
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