Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
natural-language-inference
Size:
100K - 1M
Tags:
quality-estimation
License:
File size: 6,165 Bytes
416f750 5ff16e6 f343170 5ff16e6 416f750 ca05673 416f750 14c2139 416f750 8a6366b 416f750 8a6366b c360804 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
# 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", ""),
}
|