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Browse files- americas_nli.py +0 -177
americas_nli.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages."""
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import csv
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import datasets
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_CITATION = """
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@article{DBLP:journals/corr/abs-2104-08726,
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author = {Abteen Ebrahimi and
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Manuel Mager and
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Arturo Oncevay and
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Vishrav Chaudhary and
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Luis Chiruzzo and
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Angela Fan and
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John Ortega and
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Ricardo Ramos and
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Annette Rios and
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Ivan Vladimir and
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Gustavo A. Gim{\'{e}}nez{-}Lugo and
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Elisabeth Mager and
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Graham Neubig and
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Alexis Palmer and
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Rolando A. Coto Solano and
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Ngoc Thang Vu and
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Katharina Kann},
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title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of
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Pretrained Multilingual Models in Truly Low-resource Languages},
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journal = {CoRR},
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volume = {abs/2104.08726},
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year = {2021},
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url = {https://arxiv.org/abs/2104.08726},
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eprinttype = {arXiv},
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eprint = {2104.08726},
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timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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"""
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_DESCRIPTION = """\
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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).
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"""
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VERSION = datasets.Version("1.0.0", "")
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_DEV_DATA_URL = "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv"
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_TEST_DATA_URL = "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv"
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_LANGUAGES = ("aym", "bzd", "cni", "gn", "hch", "nah", "oto", "quy", "shp", "tar")
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class AmericasNLIConfig(datasets.BuilderConfig):
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"""BuilderConfig for AmericasNLI."""
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def __init__(self, language: str, languages=None, **kwargs):
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"""BuilderConfig for AmericasNLI.
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Args:
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language: One of aym, bzd, cni, gn, hch, nah, oto, quy, shp, tar or all_languages
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**kwargs: keyword arguments forwarded to super.
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"""
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super(AmericasNLIConfig, self).__init__(**kwargs)
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self.language = language
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if language != "all_languages":
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self.languages = [language]
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else:
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self.languages = languages if languages is not None else _LANGUAGES
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class AmericasNLI(datasets.GeneratorBasedBuilder):
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"""TODO"""
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VERSION = VERSION
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BUILDER_CONFIG_CLASS = AmericasNLIConfig
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BUILDER_CONFIGS = [
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AmericasNLIConfig(
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name=lang,
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language=lang,
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version=VERSION,
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description=f"Plain text import of AmericasNLI for the {lang} language",
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)
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for lang in _LANGUAGES
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] + [
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AmericasNLIConfig(
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name="all_languages",
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language="all_languages",
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version=VERSION,
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description="Plain text import of AmericasNLI for all languages",
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)
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]
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def _info(self):
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if self.config.language == "all_languages":
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features = datasets.Features(
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{
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"language": datasets.Value("string"),
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"premise": datasets.Value("string"),
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"hypothesis": datasets.Value("string"),
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"label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
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}
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)
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else:
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features = datasets.Features(
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{
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"premise": datasets.Value("string"),
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"hypothesis": datasets.Value("string"),
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"label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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# No default supervised_keys (as we have to pass both premise
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# and hypothesis as input).
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supervised_keys=None,
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homepage="https://github.com/nala-cub/AmericasNLI",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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dl_paths = dl_manager.download(
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{
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"dev_data": _DEV_DATA_URL,
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"test_data": _TEST_DATA_URL,
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}
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)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepath": dl_paths["dev_data"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepath": dl_paths["test_data"],
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},
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),
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]
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def _generate_examples(self, filepath: str):
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"""This function returns the examples in the raw (text) form."""
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idx = 0
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with open(filepath, encoding="utf-8") as f:
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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for row in reader:
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if row["language"] == self.config.language:
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yield idx, {
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"premise": row["premise"],
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"hypothesis": row["hypothesis"],
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"label": row["label"],
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}
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idx += 1
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elif self.config.language == "all_languages":
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yield idx, {
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"language": row["language"],
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"premise": row["premise"],
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"hypothesis": row["hypothesis"],
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"label": row["label"],
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}
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idx += 1
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