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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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import pandas as pd |
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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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@inproceedings{DBLP:conf/nips/LaurenconSWAMSW22, |
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author={Hugo Laurençon and Lucile Saulnier and Thomas Wang and Christopher Akiki and Albert Villanova del Moral and |
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Teven Le Scao and Leandro von Werra and Chenghao Mou and Eduardo González Ponferrada and Huu Nguyen and Jörg Frohberg |
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and Mario Sasko and Quentin Lhoest and Angelina McMillan-Major and Gérard Dupont and Stella Biderman and Anna Rogers |
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and Loubna Ben Allal and Francesco De Toni and Giada Pistilli and Olivier Nguyen and Somaieh Nikpoor and Maraim Masoud |
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and Pierre Colombo and Javier de la Rosa and Paulo Villegas and Tristan Thrush and Shayne Longpre and Sebastian Nagel |
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and Leon Weber and Manuel Muñoz and Jian Zhu and Daniel van Strien and Zaid Alyafeai and Khalid Almubarak and Minh |
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Chien Vu and Itziar Gonzalez-Dios and Aitor Soroa and Kyle Lo and Manan Dey and Pedro Ortiz Suarez and Aaron Gokaslan |
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and Shamik Bose and David Ifeoluwa Adelani and Long Phan and Hieu Tran and Ian Yu and Suhas Pai and Jenny Chim and |
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Violette Lepercq and Suzana Ilic and Margaret Mitchell and Alexandra Sasha Luccioni and Yacine Jernite}, |
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title={The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset}, |
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year={2022}, |
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cdate={1640995200000}, |
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url={http://papers.nips.cc/paper_files/paper/2022/hash/ce9e92e3de2372a4b93353eb7f3dc0bd-Abstract-Datasets_and_Benchmarks.html}, |
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booktitle={NeurIPS}, |
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} |
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""" |
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_DATASETNAME = "roots_vi_ted" |
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_DESCRIPTION = """ |
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ROOTS_vi_ted is a subset of Vietnamese in ted_talks_iwslt datasets. ted_talks_iwslt is a collection of the original Ted |
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talks and their translated version. The translations are available in more than 109+ languages, though the distribution |
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is not uniform. Before using this dataloader, please accept the acknowledgement at |
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https://huggingface.co/datasets/bigscience-data/roots_vi_ted_talks_iwslt and use huggingface-cli login for authentication. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/bigscience-data/roots_vi_ted_talks_iwslt" |
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_LANGUAGES = ["vie"] |
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_LICENSE = Licenses.CC_BY_NC_ND_4_0.value |
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_LOCAL = False |
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_URLS = {_DATASETNAME: {"train": "https://huggingface.co/datasets/bigscience-data/roots_vi_ted_talks_iwslt/resolve/main/data/train-00000-of-00001.parquet?download=true"}} |
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_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class RootsViTedDataset(datasets.GeneratorBasedBuilder): |
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"""RootsViTed is a subset of Vietnamese in ted_talks_iwslt datasets.""" |
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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="roots_vi_ted_source", |
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version=SOURCE_VERSION, |
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description="roots_vi_ted source schema", |
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schema="source", |
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subset_id="roots_vi_ted", |
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), |
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SEACrowdConfig( |
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name="roots_vi_ted_seacrowd_ssp", |
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version=SEACROWD_VERSION, |
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description="roots_vi_ted SEACrowd schema", |
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schema="seacrowd_ssp", |
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subset_id="roots_vi_ted", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "roots_vi_ted_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( |
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{ |
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"text": datasets.Value("string"), |
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"meta": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_ssp": |
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features = schemas.self_supervised_pretraining.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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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": data_dir, "split": "train"}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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if self.config.schema == "source": |
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df = pd.read_parquet(filepath[split]) |
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for i, row in df.iterrows(): |
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yield i, { |
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"text": row["text"], |
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"meta": row["meta"], |
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} |
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elif self.config.schema == "seacrowd_ssp": |
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df = pd.read_parquet(filepath[split]) |
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for i, row in df.iterrows(): |
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yield i, { |
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"id": str(i), |
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"text": row["text"], |
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} |
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