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| from pathlib import Path |
| from typing import Dict, List, Tuple |
|
|
| import datasets |
|
|
| from seacrowd.utils import schemas |
| from seacrowd.utils.configs import SEACrowdConfig |
| from seacrowd.utils.constants import Licenses, Tasks |
|
|
| _CITATION = """\ |
| @inproceedings{elkishky_ccaligned_2020, |
| author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp}, |
| booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}, |
| month = {November}, |
| title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs}, |
| year = {2020} |
| address = "Online", |
| publisher = "Association for Computational Linguistics", |
| url = "https://www.aclweb.org/anthology/2020.emnlp-main.480", |
| doi = "10.18653/v1/2020.emnlp-main.480", |
| pages = "5960--5969" |
| } |
| """ |
|
|
| _DATASETNAME = "cc_aligned_doc" |
|
|
| _DESCRIPTION = """\ |
| CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English\ |
| (10 languages are from Southeast Asia; Burmese has two document collection with different scripts).\ |
| These web-document pairs were constructed by performing language identification on raw web-documents, \ |
| and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern \ |
| matching approach yielded more than 100 million aligned documents paired with English. |
| """ |
|
|
| _HOMEPAGE = "https://www2.statmt.org/cc-aligned/" |
|
|
| _LANGUAGES = ["ind", "sun", "tha", "vie", "zlm", "lao", "khm", "mya", "ceb", "war"] |
|
|
| _LICENSE = Licenses.UNKNOWN.value |
|
|
| _LOCAL = False |
| _SUBSETS = {"id_ID": "ind", "su_ID": "sun", "th_TH": "tha", "vi_VN": "vie", "ms_MY": "zlm", "lo_LA": "lao", "km_KH": "khm", "my_MM": "mya", "my_MM_zaw": "mya", "cx_PH": "ceb", "wy_PH": "war"} |
| _URLS = {_DATASETNAME: "https://data.statmt.org/cc-aligned/en_XX-{subset}.tsv.xz"} |
|
|
| _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
|
|
| _SOURCE_VERSION = "1.0.0" |
|
|
| _SEACROWD_VERSION = "2024.06.20" |
|
|
|
|
| class CCAlignedDocDataset(datasets.GeneratorBasedBuilder): |
|
|
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
| SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
| SEACROWD_SCHEMA_NAME = "t2t" |
|
|
| BUILDER_CONFIGS = [SEACrowdConfig(name=f"{_DATASETNAME}_{subset}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}",) for subset in _SUBSETS.keys()] + [ |
| SEACrowdConfig( |
| name=f"{_DATASETNAME}_{subset}_seacrowd_{schema_name}", |
| version=datasets.Version(_SEACROWD_VERSION), |
| description=f"{_DATASETNAME} SEACrowd schema", |
| schema=f"seacrowd_{schema_name}", |
| subset_id=f"{_DATASETNAME}", |
| ) |
| for subset, schema_name in zip(_SUBSETS.keys(), len(_SUBSETS.keys()) * [SEACROWD_SCHEMA_NAME]) |
| ] |
|
|
| DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_id_ID_source" |
|
|
| def _info(self) -> datasets.DatasetInfo: |
|
|
| if self.config.schema == "source": |
| features = datasets.Features( |
| { |
| "Domain": datasets.Value("string"), |
| "Source_URL": datasets.Value("string"), |
| "Source_Content": datasets.Value("string"), |
| "Target_URL": datasets.Value("string"), |
| "Target_Content": datasets.Value("string"), |
| } |
| ) |
| elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
| 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.""" |
| subset = "_".join([self.config.name.split("_")[3], self.config.name.split("_")[4]]) |
| urls = _URLS[_DATASETNAME].format(subset=subset) |
| 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]: |
| """Yields examples as (key, example) tuples.""" |
| subset = "_".join([self.config.name.split("_")[3], self.config.name.split("_")[4]]) |
| lines = open(filepath, "r").readlines() |
| if self.config.schema == "source": |
| idx = 0 |
| for line in lines: |
| content = line.split("\t") |
| example = { |
| "Domain": content[0], |
| "Source_URL": content[1], |
| "Source_Content": content[2], |
| "Target_URL": content[3], |
| "Target_Content": content[4], |
| } |
| yield idx, example |
| idx += 1 |
| elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
| idx = 0 |
| for line in lines: |
| content = line.split("\t") |
| example = { |
| "id": str(idx), |
| "text_1": content[2], |
| "text_2": content[4], |
| "text_1_name": "en", |
| "text_2_name": _SUBSETS[subset], |
| } |
| yield idx, example |
| idx += 1 |
|
|
