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Browse files- wiki_lingua.py +0 -168
wiki_lingua.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# 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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"""WikiLingua."""
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import json
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import datasets
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@inproceedings{ladhak-etal-2020-wikilingua,
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title = "{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization",
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author = "Ladhak, Faisal and
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Durmus, Esin and
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Cardie, Claire and
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McKeown, Kathleen",
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
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month = nov,
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year = "2020",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2020.findings-emnlp.360",
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doi = "10.18653/v1/2020.findings-emnlp.360",
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pages = "4034--4048",
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}
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"""
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_DESCRIPTION = """\
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WikiLingua is a large-scale multilingual dataset for the evaluation of
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cross-lingual abstractive summarization systems. The dataset includes ~770k
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article and summary pairs in 18 languages from WikiHow. The gold-standard
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article-summary alignments across languages was done by aligning the images
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that are used to describe each how-to step in an article.
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"""
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_HOMEPAGE = "https://github.com/esdurmus/Wikilingua"
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_LICENSE = "CC BY-NC-SA 3.0"
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# Download link
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_URL = "data/{language}.jsonl.gz"
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_LANGUAGES = [
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"arabic",
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"chinese",
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"czech",
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"dutch",
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"english",
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"french",
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"german",
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"hindi",
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"indonesian",
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"italian",
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"japanese",
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"korean",
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"portuguese",
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"russian",
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"spanish",
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"thai",
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"turkish",
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"vietnamese",
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]
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class WikiLingua(datasets.GeneratorBasedBuilder):
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"""WikiLingua dataset."""
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VERSION = datasets.Version("1.1.1")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name=lang,
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version=datasets.Version("1.1.1"),
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description=f"A subset of article-summary in {lang.capitalize()}",
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)
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for lang in _LANGUAGES
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]
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DEFAULT_CONFIG_NAME = "english"
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def _info(self):
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if self.config.name == "english":
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features = datasets.Features(
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{
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"url": datasets.Value("string"),
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"article": datasets.Sequence(
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{
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"section_name": datasets.Value("string"),
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"document": datasets.Value("string"),
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"summary": datasets.Value("string"),
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}
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),
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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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"url": datasets.Value("string"),
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"article": datasets.Sequence(
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{
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"section_name": datasets.Value("string"),
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"document": datasets.Value("string"),
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"summary": datasets.Value("string"),
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"english_url": datasets.Value("string"),
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"english_section_name": datasets.Value("string"),
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}
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),
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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filepath = dl_manager.download_and_extract(_URL.format(language=self.config.name))
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": filepath,
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},
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),
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]
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def _process_article(self, article):
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"""Parse the article and convert into list of dict"""
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processed_article = []
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for key, value in article.items():
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row = {"section_name": key, "document": value["document"], "summary": value["summary"]}
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if self.config.name != "english":
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row["english_url"] = value["english_url"]
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row["english_section_name"] = value["english_section_name"]
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processed_article.append(row)
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return processed_article
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def _generate_examples(self, filepath):
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"""Yields examples."""
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with open(filepath, "rb") as f:
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for id_, line in enumerate(f):
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row = json.loads(line)
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yield id_, {"url": row["url"], "article": self._process_article(row["article"])}
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