| 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 | |