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* finishing aya_collection_translated dataloader * change id and template_id datatype to int64
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seacrowd/sea_datasets/aya_collection_translated/aya_collection_translated.py
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from pathlib import Path | ||
from typing import Dict, List, Tuple | ||
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import datasets | ||
import pandas as pd | ||
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from seacrowd.utils import schemas | ||
from seacrowd.utils.configs import SEACrowdConfig | ||
from seacrowd.utils.constants import Licenses, Tasks | ||
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_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} | ||
} | ||
""" | ||
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_DATASETNAME = "aya_collection_translated" | ||
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_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. | ||
""" | ||
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_HOMEPAGE = "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split" | ||
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_LANGUAGES = ["ceb", "tha", "mya", "zsm", "jav", "ind", "vie", "sun", "ace", "bjn", "khm", "lao", "min"] | ||
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_LICENSE = Licenses.APACHE_2_0.value | ||
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_LOCAL = False | ||
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_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", | ||
} | ||
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_SUPPORTED_TASKS = [Tasks.INSTRUCTION_TUNING] | ||
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_SOURCE_VERSION = "1.0.0" | ||
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_SEACROWD_VERSION = "1.0.0" | ||
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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. | ||
""" | ||
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | ||
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | ||
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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 | ||
] | ||
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_ind_source" | ||
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def _info(self) -> datasets.DatasetInfo: | ||
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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"), | ||
} | ||
) | ||
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elif self.config.schema == "seacrowd_t2t": | ||
features = schemas.text2text_features | ||
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return datasets.DatasetInfo( | ||
description=_DESCRIPTION, | ||
features=features, | ||
homepage=_HOMEPAGE, | ||
license=_LICENSE, | ||
citation=_CITATION, | ||
) | ||
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | ||
"""Returns SplitGenerators.""" | ||
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language = self.config.name.split("_")[3] | ||
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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 | ||
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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")) | ||
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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", | ||
}, | ||
), | ||
] | ||
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def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: | ||
"""Yields examples as (key, example) tuples.""" | ||
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if isinstance(filepath, Path): | ||
dfs = [pd.read_parquet(filepath)] | ||
else: | ||
dfs = [pd.read_parquet(path) for path in filepath] | ||
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df = pd.concat(dfs, ignore_index=True) | ||
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for index, row in df.iterrows(): | ||
if self.config.schema == "source": | ||
example = row.to_dict() | ||
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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", | ||
} | ||
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yield index, example |