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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import io | |
| import json | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple | |
| import datasets | |
| import zstandard as zstd | |
| from huggingface_hub import HfFileSystem | |
| from seacrowd.utils.configs import SEACrowdConfig | |
| from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA, | |
| Licenses, Tasks) | |
| _CITATION = """\ | |
| @misc{nguyen2024culturay, | |
| title={CulturaY: A Large Cleaned Multilingual Dataset of 75 Languages}, | |
| author={Thuat Nguyen, Huu Nguyen and Thien Nguyen}, | |
| year={2024}, | |
| } | |
| """ | |
| _DATASETNAME = "culturay" | |
| _DESCRIPTION = """\ | |
| CulturaY: A Large Cleaned Multilingual Dataset of 75 Languages From the team | |
| that brought you CulutraX, we present CulturaY, another substantial multilingual | |
| dataset of 15TB (uncompressed)/3TB (zstd-compressed) that applies the same | |
| dataset cleaning methodology to the HPLT v1.1 dataset. Please note that HPLT | |
| v1.2 has also been released and is an alternative verison with different | |
| cleaning methodolgies. This data was used in part to train our SOTA Vietnamese | |
| model: Vistral-7B-Chat. | |
| Before using this dataloader, please accept the acknowledgement at | |
| https://huggingface.co/datasets/ontocord/CulturaY and use huggingface-cli login | |
| for authentication. | |
| """ | |
| _HOMEPAGE = "https://huggingface.co/datasets/ontocord/CulturaY" | |
| _LANGUAGES = ["mya", "fil", "zlm", "vie", "ind", "tha"] | |
| _LICENSE = Licenses.CC_BY_4_0.value | |
| _LOCAL = False | |
| _BASE_URL = "https://huggingface.co/datasets/ontocord/CulturaY/resolve/main/{lang}/" | |
| _SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING] | |
| _SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}" # ssp | |
| _SOURCE_VERSION = "1.0.0" | |
| _SEACROWD_VERSION = "2024.06.20" | |
| class CulturaYDataset(datasets.GeneratorBasedBuilder): | |
| """Substantial multilingual dataset by cleaning HPLT v1.1 (Internet Archive) data.""" | |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | |
| SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | |
| BUILDER_CONFIGS = [] | |
| for subset in _LANGUAGES: | |
| BUILDER_CONFIGS += [ | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_{subset}_source", | |
| version=SOURCE_VERSION, | |
| description=f"{_DATASETNAME} {subset} source schema", | |
| schema="source", | |
| subset_id=subset, | |
| ), | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}", | |
| version=SEACROWD_VERSION, | |
| description=f"{_DATASETNAME} {subset} SEACrowd schema", | |
| schema=_SEACROWD_SCHEMA, | |
| subset_id=subset, | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_my_source" # smallest wrt n_doc | |
| def _info(self) -> datasets.DatasetInfo: | |
| if self.config.schema == "source": | |
| features = datasets.Features( | |
| { | |
| "id": datasets.Value("int64"), | |
| "document_lang": datasets.Value("string"), | |
| "scores": datasets.Sequence(datasets.Value("float64")), | |
| "langs": datasets.Sequence(datasets.Value("string")), | |
| "text": datasets.Value("string"), | |
| "url": datasets.Value("string"), | |
| "collection": datasets.Value("string"), | |
| } | |
| ) | |
| elif self.config.schema == _SEACROWD_SCHEMA: | |
| features = SCHEMA_TO_FEATURES[ | |
| TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]] | |
| ] # ssp_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. Data is not yet extracted for efficient generation.""" | |
| lang_dict = {"mya": "my", "fil": "tl", "zlm": "ms", "vie": "vi", "ind": "id", "tha": "th"} | |
| subset = lang_dict[self.config.subset_id] | |
| base_path = _BASE_URL.format(lang=subset) | |
| fs = HfFileSystem(token=dl_manager.download_config.token) | |
| file_list = fs.ls(f"datasets/ontocord/CulturaY/{subset}", detail=False) | |
| data_urls = [ | |
| f"{base_path}{filename.split('/')[-1]}" | |
| for filename in file_list | |
| if filename.endswith(".jsonl.zst") | |
| ] | |
| data_paths = list(map(Path, dl_manager.download(data_urls))) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "data_paths": data_paths, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, data_paths: Path) -> Tuple[int, Dict]: | |
| """Yields examples as (key, example) tuples.""" | |
| key = 0 | |
| for data_path in data_paths: | |
| with open(data_path, "rb") as f: | |
| # Zstandard decompression | |
| dctx = zstd.ZstdDecompressor() | |
| reader = dctx.stream_reader(f) | |
| text_io = io.TextIOWrapper(reader, encoding="utf-8") | |
| # read jsonl file by line and yield | |
| for line in text_io: | |
| data = json.loads(line) | |
| if self.config.schema == "source": | |
| yield key, { | |
| "id": data["id"], | |
| "document_lang": data["document_lang"], | |
| "scores": data["scores"], | |
| "langs": data["langs"], | |
| "text": data["text"], | |
| "url": data["url"], | |
| "collection": data["collection"], | |
| } | |
| elif self.config.schema == _SEACROWD_SCHEMA: | |
| yield key, { | |
| "id": str(data["id"]), | |
| "text": data["text"], | |
| } | |
| key += 1 | |