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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
"""AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages."""
import csv
import datasets
from datasets.utils.download_manager import DownloadManager
_CITATION = """
@article{DBLP:journals/corr/abs-2104-08726,
author = {Abteen Ebrahimi and
Manuel Mager and
Arturo Oncevay and
Vishrav Chaudhary and
Luis Chiruzzo and
Angela Fan and
John Ortega and
Ricardo Ramos and
Annette Rios and
Ivan Vladimir and
Gustavo A. Gim{\'{e}}nez{-}Lugo and
Elisabeth Mager and
Graham Neubig and
Alexis Palmer and
Rolando A. Coto Solano and
Ngoc Thang Vu and
Katharina Kann},
title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of
Pretrained Multilingual Models in Truly Low-resource Languages},
journal = {CoRR},
volume = {abs/2104.08726},
year = {2021},
url = {https://arxiv.org/abs/2104.08726},
eprinttype = {arXiv},
eprint = {2104.08726},
timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """\
AmericasNLI is an extension of XNLI (Conneau et al., 2018) – a natural language inference (NLI) dataset covering 15 high-resource languages – to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).
"""
VERSION = datasets.Version("1.0.0", "")
_DEV_DATA_URL = "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv"
_TEST_DATA_URL = "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv"
_LANGUAGES = ("aym", "bzd", "cni", "gn", "hch", "nah", "oto", "quy", "shp", "tar")
class AmericasNLIConfig(datasets.BuilderConfig):
"""BuilderConfig for AmericasNLI."""
def __init__(self, language: str, languages=None, **kwargs):
"""BuilderConfig for AmericasNLI.
Args:
language: One of aym, bzd, cni, gn, hch, nah, oto, quy, shp, tar or all_languages
**kwargs: keyword arguments forwarded to super.
"""
super(AmericasNLIConfig, self).__init__(**kwargs)
self.language = language
if language != "all_languages":
self.languages = [language]
else:
self.languages = languages if languages is not None else _LANGUAGES
class AmericasNLI(datasets.GeneratorBasedBuilder):
"""TODO"""
VERSION = VERSION
BUILDER_CONFIG_CLASS = AmericasNLIConfig
BUILDER_CONFIGS = [
AmericasNLIConfig(
name=lang,
language=lang,
version=VERSION,
description=f"Plain text import of AmericasNLI for the {lang} language",
)
for lang in _LANGUAGES
] + [
AmericasNLIConfig(
name="all_languages",
language="all_languages",
version=VERSION,
description="Plain text import of AmericasNLI for all languages",
)
]
def _info(self):
if self.config.language == "all_languages":
features = datasets.Features(
{
"language": datasets.Value("string"),
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
)
else:
features = datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage="https://github.com/nala-cub/AmericasNLI",
citation=_CITATION,
)
def _split_generators(self, dl_manager: DownloadManager):
dl_paths = dl_manager.download(
{
"dev_data": _DEV_DATA_URL,
"test_data": _TEST_DATA_URL,
}
)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": dl_paths["dev_data"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": dl_paths["test_data"],
},
),
]
def _generate_examples(self, filepath: str):
"""This function returns the examples in the raw (text) form."""
idx = 0
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for row in reader:
if row["language"] == self.config.language:
yield idx, {
"premise": row["premise"],
"hypothesis": row["hypothesis"],
"label": row["label"],
}
idx += 1
elif self.config.language == "all_languages":
yield idx, {
"language": row["language"],
"premise": row["premise"],
"hypothesis": row["hypothesis"],
"label": row["label"],
}
idx += 1
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