Datasets:
Tasks:
Translation
Size Categories:
1K<n<10K
Annotations Creators:
no-annotation
Source Datasets:
original
Tags:
License:
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# 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. | |
""" Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English | |
counterparts, used to examine whether neural machine translation models can perform coreference resolution that | |
requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across | |
multiple languages. """ | |
import csv | |
import json | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{Emelin2021WinoXMW, | |
title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution}, | |
author={Denis Emelin and Rico Sennrich}, | |
booktitle={EMNLP}, | |
year={2021} | |
} | |
""" | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English | |
counterparts, used to examine whether neural machine translation models can perform coreference resolution that | |
requires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across | |
multiple languages. | |
""" | |
_HOMEPAGE = "https://github.com/demelin/Wino-X" | |
_LICENSE = "MIT" | |
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
_URLS = { | |
"mt_en_de": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_de_test.jsonl", | |
"mt_en_fr": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_fr_test.jsonl", | |
"mt_en_ru": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_ru_test.jsonl", | |
"lm_en_de": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_de_test.jsonl", | |
"lm_en_fr": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_fr_test.jsonl", | |
"lm_en_ru": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_ru_test.jsonl" | |
} | |
class WinoX(datasets.GeneratorBasedBuilder): | |
""" Wino-X is a dataset of German, French, and Russian Winograd schemas, aligned with their English counterparts """ | |
VERSION = datasets.Version("1.1.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name="mt_en_de", version=VERSION, | |
description="This is the EN-DE part of the Wino-X translation data."), | |
datasets.BuilderConfig(name="mt_en_fr", version=VERSION, | |
description="This is the EN-FR part of the Wino-X translation data."), | |
datasets.BuilderConfig(name="mt_en_ru", version=VERSION, | |
description="This is the EN-RU part of the Wino-X translation data."), | |
datasets.BuilderConfig(name="lm_en_de", version=VERSION, | |
description="This is the EN-DE part of the Wino-X language modeling data."), | |
datasets.BuilderConfig(name="lm_en_fr", version=VERSION, | |
description="This is the EN-FR part of the Wino-X language modeling data."), | |
datasets.BuilderConfig(name="lm_en_ru", version=VERSION, | |
description="This is the EN-RU part of the Wino-X language modeling data."), | |
] | |
def _info(self): | |
# MT example: | |
# {"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1", | |
# "sentence": "The woman looked for a different vase for the bouquet because it was too small.", | |
# "translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.", | |
# "translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.", | |
# "answer": 1, | |
# "pronoun1": "sie", | |
# "pronoun2": "er", | |
# "referent1_en": "vase", | |
# "referent2_en": "bouquet", | |
# "true_translation_referent_of_pronoun1_de": "Vase", | |
# "true_translation_referent_of_pronoun2_de": "Blumenstrauß", | |
# "false_translation_referent_of_pronoun1_de": "Vase", | |
# "false_translation_referent_of_pronoun2_de": "Blumenstrauß"} | |
tgt_lang = self.config.name.split('_')[-1] | |
if self.config.name.startswith('mt_'): | |
features = datasets.Features( | |
{ | |
"qID": datasets.Value("string"), | |
"sentence": datasets.Value("string"), | |
"translation1": datasets.Value("string"), | |
"translation2": datasets.Value("string"), | |
"answer": datasets.Value("int64"), | |
"pronoun1": datasets.Value("string"), | |
"pronoun2": datasets.Value("string"), | |
"referent1_en": datasets.Value("string"), | |
"referent2_en": datasets.Value("string"), | |
"true_translation_referent_of_pronoun1_{}".format(tgt_lang): datasets.Value("string"), | |
"true_translation_referent_of_pronoun2_{}".format(tgt_lang): datasets.Value("string"), | |
"false_translation_referent_of_pronoun1_{}".format(tgt_lang): datasets.Value("string"), | |
"false_translation_referent_of_pronoun2_{}".format(tgt_lang): datasets.Value("string") | |
} | |
) | |
# LM example: | |
# {"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1", | |
# "sentence": "The woman looked for a different vase for the bouquet because it was too small.", | |
# "context_en": "The woman looked for a different vase for the bouquet because _ was too small.", | |
# "context_de": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil _ zu klein war.", | |
# "option1_en": "the vase", | |
# "option2_en": "the bouquet", | |
# "option1_de": "die Vase", | |
# "option2_de": "der Blumenstrauß", | |
# "answer": 1, | |
# "context_referent_of_option1_de": "Vase", | |
# "context_referent_of_option2_de": "Blumenstrauß"} | |
else: | |
features = datasets.Features( | |
{ | |
"qID": datasets.Value("string"), | |
"sentence": datasets.Value("string"), | |
"context_en": datasets.Value("string"), | |
"context_{}".format(tgt_lang): datasets.Value("string"), | |
"answer": datasets.Value("int64"), | |
"option1_en": datasets.Value("string"), | |
"option2_en": datasets.Value("string"), | |
"option1_{}".format(tgt_lang): datasets.Value("string"), | |
"option2_{}".format(tgt_lang): datasets.Value("string"), | |
"context_referent_of_option1_{}".format(tgt_lang): datasets.Value("string"), | |
"context_referent_of_option2_{}".format(tgt_lang): datasets.Value("string") | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
downloaded_files = dl_manager.download_and_extract(_URLS[self.config.name]) | |
return [datasets.SplitGenerator(name=datasets.Split.TEST, | |
gen_kwargs={'filepath': downloaded_files, 'split': 'test'})] | |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
def _generate_examples(self, filepath, split): | |
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
with open(filepath, encoding="utf-8") as f: | |
for key, row in enumerate(f): | |
data = json.loads(row) | |
yield key, data | |