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 = """ @misc{singh2024aya, title={Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning}, author={Shivalika Singh and Freddie Vargus and Daniel Dsouza and Börje F. Karlsson and Abinaya Mahendiran and Wei-Yin Ko and Herumb Shandilya and Jay Patel and Deividas Mataciunas and Laura OMahony and Mike Zhang and Ramith Hettiarachchi and Joseph Wilson and Marina Machado and Luisa Souza Moura and Dominik Krzemiński and Hakimeh Fadaei and Irem Ergün and Ifeoma Okoh and Aisha Alaagib and Oshan Mudannayake and Zaid Alyafeai and Vu Minh Chien and Sebastian Ruder and Surya Guthikonda and Emad A. Alghamdi and Sebastian Gehrmann and Niklas Muennighoff and Max Bartolo and Julia Kreutzer and Ahmet Üstün and Marzieh Fadaee and Sara Hooker}, year={2024}, eprint={2402.06619}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DATASETNAME = "aya_collection_translated" _DESCRIPTION = """ The Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and completions covering a wide range of tasks. This dataset covers the translated prompts of the Aya Collection. """ _HOMEPAGE = "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split" _LANGUAGES = ["ceb", "tha", "mya", "zsm", "jav", "ind", "vie", "sun", "ace", "bjn", "khm", "lao", "min"] _LICENSE = Licenses.APACHE_2_0.value _LOCAL = False _URLS = { "ceb": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/cebuano", "tha": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/thai", "mya": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/burmese", "zsm": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/malayalam", "jav": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/javanese", "ind": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/indonesian", "vie": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/vietnamese", "sun": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/sundanese", "ace": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/achinese", "bjn": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/banjar", "khm": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/central_khmer", "lao": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/lao", "min": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/minangkabau", } _SUPPORTED_TASKS = [Tasks.INSTRUCTION_TUNING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class AyaCollectionTranslatedDataset(datasets.GeneratorBasedBuilder): """ The Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and completions covering a wide range of tasks. This dataset covers the translated prompts of the Aya Collection. """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_{LANG}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} {LANG} source schema", schema="source", subset_id=f"{_DATASETNAME}_{LANG}", ) for LANG in _LANGUAGES ] + [ SEACrowdConfig( name=f"{_DATASETNAME}_{LANG}_seacrowd_t2t", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} {LANG} SEACrowd schema", schema="seacrowd_t2t", subset_id=f"{_DATASETNAME}_{LANG}", ) for LANG in _LANGUAGES ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_ind_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("int64"), "inputs": datasets.Value("string"), "targets": datasets.Value("string"), "dataset_name": datasets.Value("string"), "sub_dataset_name": datasets.Value("string"), "task_type": datasets.Value("string"), "template_id": datasets.Value("int64"), "language": datasets.Value("string"), "script": datasets.Value("string"), "split": datasets.Value("string"), } ) elif self.config.schema == "seacrowd_t2t": features = schemas.text2text_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.""" language = self.config.name.split("_")[3] if language in _LANGUAGES: data_train_paths = [] for version in [0, 1, 2]: for all in [1, 2, 3]: if version >= all: continue else: try: data_train_path = Path(dl_manager.download_and_extract(f"{_URLS[language]}/train-0000{version}-of-0000{all}.parquet?download=true")) data_train_paths.append(data_train_path) except Exception: continue data_validation_path = Path(dl_manager.download_and_extract(f"{_URLS[language]}/validation-00000-of-00001.parquet?download=true")) data_test_path = Path(dl_manager.download_and_extract(f"{_URLS[language]}/test-00000-of-00001.parquet?download=true")) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_train_paths, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_test_path, "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_validation_path, "split": "dev", }, ), ] def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if isinstance(filepath, Path): dfs = [pd.read_parquet(filepath)] else: dfs = [pd.read_parquet(path) for path in filepath] df = pd.concat(dfs, ignore_index=True) for index, row in df.iterrows(): if self.config.schema == "source": example = row.to_dict() elif self.config.schema == "seacrowd_t2t": example = { "id": str(index), "text_1": row["inputs"], "text_2": row["targets"], "text_1_name": "inputs", "text_2_name": "targets", } yield index, example