Upload roots_vi_ted.py with huggingface_hub

roots_vi_ted.py

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