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gatitos.py
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# coding=utf-8
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# Copyright 2022 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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"""
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The GATITOS (Google's Additional Translations Into Tail-languages: Often Short) dataset is a high-quality, multi-way parallel dataset of tokens and short phrases.
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This dataset consists in 4,000 English segments (4,500 tokens) that have been translated into each of 173 languages, 170 of which are low-resource, 23 are spoken in Southeast Asia.
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This dataset contains primarily short segments: 93% single tokens, and only 23 sentences (0.6%) have over 5 tokens.
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As such it is best thought of as a multilingual lexicon, rather than a parallel training corpus.
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The source text is frequent words in the English Language, along with some common phrases and short sentences.
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Care has been taken to ensure that they include good coverage of numbers, months, days of the week, swadesh words, and names of the languages themselves (including the endonym).
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"""
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from pathlib import Path
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from typing import Dict, List, Tuple
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import datasets
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from seacrowd.utils import schemas
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import Licenses, Tasks
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_CITATION = """\
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@misc{jones2023bilex,
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title={Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation},
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author={Alex Jones and Isaac Caswell and Ishank Saxena and Orhan Firat},
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year={2023},
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eprint={2303.15265},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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}
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"""
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_DATASETNAME = "gatitos"
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_DESCRIPTION = """\
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The GATITOS (Google's Additional Translations Into Tail-languages: Often Short) dataset is a high-quality, multi-way parallel dataset of tokens and short phrases.
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This dataset consists in 4,000 English segments (4,500 tokens) that have been translated into each of 173 languages, 170 of which are low-resource, 23 are spoken in Southeast Asia.
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This dataset contains primarily short segments: 93% single tokens, and only 23 sentences (0.6%) have over 5 tokens.
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As such it is best thought of as a multilingual lexicon, rather than a parallel training corpus.
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The source text is frequent words in the English Language, along with some common phrases and short sentences.
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Care has been taken to ensure that they include good coverage of numbers, months, days of the week, swadesh words, and names of the languages themselves (including the endonym).
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"""
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_HOMEPAGE = "https://github.com/google-research/url-nlp/blob/main/gatitos/README.md"
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_LANGUAGES = ["ace", "ban", "bbc", "bew", "bjn", "bts", "btx", "bug", "cnh", "hil", "iba", "ilo", "kac", "lus", "mad", "mak", "meo", "min", "pag", "pam", "shn", "tet", "war"]
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_LICENSE = Licenses.CC_BY_4_0.value
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_LOCAL = False
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_URLs = "https://raw.githubusercontent.com/google-research/url-nlp/main/gatitos/{src}_{tgt}.tsv"
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class GATITOSDataset(datasets.GeneratorBasedBuilder):
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"""The GATITOS (Google's Additional Translations Into Tail-languages: Often Short) dataset is a high-quality, multi-way parallel dataset of tokens and short phrases."""
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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BUILDER_CONFIGS = [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{src_lang}_{tgt_lang}_source",
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version=datasets.Version(_SOURCE_VERSION),
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description=f"{_DATASETNAME} source schema",
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schema="source",
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subset_id=f"{_DATASETNAME}_{src_lang}_{tgt_lang}",
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)
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for (src_lang, tgt_lang) in [("eng", lang) for lang in _LANGUAGES] + [(lang, "eng") for lang in _LANGUAGES]
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] + [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{src_lang}_{tgt_lang}_seacrowd_t2t",
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version=datasets.Version(_SEACROWD_VERSION),
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description=f"{_DATASETNAME} SEACrowd schema",
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schema="seacrowd_t2t",
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subset_id=f"{_DATASETNAME}_{src_lang}_{tgt_lang}",
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)
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for (src_lang, tgt_lang) in [("eng", lang) for lang in _LANGUAGES] + [(lang, "eng") for lang in _LANGUAGES]
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]
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features({"id": datasets.Value("string"), "src_text": datasets.Value("string"), "tgt_text": datasets.Value("string")})
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elif self.config.schema == "seacrowd_t2t":
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features = schemas.text2text_features
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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_, src_lang, tgt_lang = self.config.subset_id.split("_")
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filepath = dl_manager.download_and_extract(_URLs.format(src=src_lang.replace("eng", "en"), tgt=tgt_lang.replace("eng", "en")))
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# Whatever you put in gen_kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": filepath, "src_lang": src_lang, "tgt_lang": tgt_lang},
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)
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]
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def _generate_examples(self, src_lang: str, tgt_lang: str, filepath: Path) -> Tuple[int, Dict]:
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if self.config.schema == "source":
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for row_id, row in enumerate(open(filepath)):
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src_text, tgt_text = row.strip().split("\t")
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yield row_id, {"id": row_id, "src_text": src_text, "tgt_text": tgt_text}
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elif self.config.schema == "seacrowd_t2t":
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for row_id, row in enumerate(open(filepath)):
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src_text, tgt_text = row.strip().split("\t")
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yield row_id, {"id": row_id, "text_1": src_text, "text_2": tgt_text, "text_1_name": src_lang, "text_2_name": tgt_lang}
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