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Upload tatoeba.py with huggingface_hub

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+ from pathlib import Path
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+ from typing import Dict, List, Tuple
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+
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+ import datasets
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+ from datasets.download.download_manager import DownloadManager
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+
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+ from seacrowd.utils import schemas
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+ from seacrowd.utils.configs import SEACrowdConfig
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+ from seacrowd.utils.constants import Licenses, Tasks
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+
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+ _CITATION = """\
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+ @article{tatoeba,
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+ title = {Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond},
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+ author = {Mikel, Artetxe and Holger, Schwenk,},
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+ journal = {arXiv:1812.10464v2},
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+ year = {2018}
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+ }
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+ """
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+
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+ _LOCAL = False
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+ _LANGUAGES = ["ind", "vie", "tgl", "jav", "tha", "eng"]
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+ _DATASETNAME = "tatoeba"
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+ _DESCRIPTION = """\
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+ This dataset is a subset of the Tatoeba corpus containing language pairs for Indonesian, Vietnamese, Tagalog, Javanese, and Thai.
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+ The original dataset description can be found below:
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+
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+ This data is extracted from the Tatoeba corpus, dated Saturday 2018/11/17.
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+ For each languages, we have selected 1000 English sentences and their translations, if available. Please check
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+ this paper for a description of the languages, their families and scripts as well as baseline results.
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+ Please note that the English sentences are not identical for all language pairs. This means that the results are
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+ not directly comparable across languages. In particular, the sentences tend to have less variety for several
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+ low-resource languages, e.g. "Tom needed water", "Tom needs water", "Tom is getting water", ...
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+ """
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+
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+ _HOMEPAGE = "https://github.com/facebookresearch/LASER/blob/main/data/tatoeba/v1/README.md"
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+ _LICENSE = Licenses.APACHE_2_0.value
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+ _URL = "https://github.com/facebookresearch/LASER/raw/main/data/tatoeba/v1/"
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+
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+ _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
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+ _SOURCE_VERSION = "1.0.0"
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+ _SEACROWD_VERSION = "2024.06.20"
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+
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+
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+ class TatoebaDataset(datasets.GeneratorBasedBuilder):
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+ """Tatoeba subset for Indonesian, Vietnamese, Tagalog, Javanese, and Thai."""
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+
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+ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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+ SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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+
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+ SEACROWD_SCHEMA_NAME = "t2t"
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+
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+ # Add configurations for loading a dataset per language.
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+ dataset_names = sorted([f"tatoeba_{lang}_eng" for lang in _LANGUAGES[:-1]]) + sorted([f"tatoeba_eng_{lang}" for lang in _LANGUAGES[:-1]])
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+ BUILDER_CONFIGS = []
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+ for name in dataset_names:
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+ source_config = SEACrowdConfig(
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+ name=f"{name}_source",
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+ version=SOURCE_VERSION,
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+ description=f"{_DATASETNAME} source schema",
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+ schema="source",
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+ subset_id=name,
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+ )
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+ BUILDER_CONFIGS.append(source_config)
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+ seacrowd_config = SEACrowdConfig(
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+ name=f"{name}_seacrowd_{SEACROWD_SCHEMA_NAME}",
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+ version=SEACROWD_VERSION,
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+ description=f"{_DATASETNAME} SEACrowd schema",
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+ schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
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+ subset_id=name,
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+ )
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+ BUILDER_CONFIGS.append(seacrowd_config)
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+
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+ # Add configuration that allows loading all datasets at once.
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+ BUILDER_CONFIGS.extend(
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+ [
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+ # tatoeba_source
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+ SEACrowdConfig(
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+ name=f"{_DATASETNAME}_source",
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+ version=SOURCE_VERSION,
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+ description=f"{_DATASETNAME} source schema (all)",
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+ schema="source",
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+ subset_id=_DATASETNAME,
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+ ),
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+ # tatoeba_seacrowd_t2t
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+ SEACrowdConfig(
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+ name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
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+ version=SEACROWD_VERSION,
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+ description=f"{_DATASETNAME} SEACrowd schema (all)",
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+ schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
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+ subset_id=_DATASETNAME,
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+ ),
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+ ]
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+ )
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+
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+ # Choose first language as default
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+ DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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+
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+ def _info(self) -> datasets.DatasetInfo:
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+ if self.config.schema == "source":
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+ features = datasets.Features(
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+ {
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+ "source_sentence": datasets.Value("string"),
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+ "target_sentence": datasets.Value("string"),
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+ "source_lang": datasets.Value("string"),
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+ "target_lang": datasets.Value("string"),
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+ }
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+ )
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+ elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
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+ features = schemas.text2text_features
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=features,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
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+ """Return SplitGenerators."""
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+ language_pairs = []
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+ tatoeba_source_data = []
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+ tatoeba_eng_data = []
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+
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+ lang_1 = self.config.name.split("_")[1]
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+ lang_2 = self.config.name.split("_")[2]
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+ if lang_1 == "eng":
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+ lang = lang_2
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+ else:
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+ lang = lang_1
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+
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+ if lang in _LANGUAGES:
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+ # Load data per language
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+ tatoeba_source_data.append(dl_manager.download_and_extract(_URL + f"tatoeba.{lang}-eng.{lang_1}"))
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+ tatoeba_eng_data.append(dl_manager.download_and_extract(_URL + f"tatoeba.{lang}-eng.{lang_2}"))
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+ language_pairs.append((lang_1, lang_2))
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+ else:
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+ # Load examples from all languages at once
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+ # We just want to run this part when tatoeba_source / tatoeba_seacrowd_t2t was chosen.
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+ for lang in _LANGUAGES[:-1]:
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+ tatoeba_source_data.append(dl_manager.download_and_extract(_URL + f"tatoeba.{lang}-eng.{lang}"))
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+ tatoeba_eng_data.append(dl_manager.download_and_extract(_URL + f"tatoeba.{lang}-eng.eng"))
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+ language_pairs.append((lang, "eng"))
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={
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+ "filepaths": (tatoeba_source_data, tatoeba_eng_data),
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+ "split": "dev",
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+ "language_pairs": language_pairs,
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+ },
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+ )
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+ ]
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+
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+ def _generate_examples(self, filepaths: Tuple[List[Path], List[Path]], split: str, language_pairs: List[str]) -> Tuple[int, Dict]:
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+ """Yield examples as (key, example) tuples"""
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+ source_files, target_files = filepaths
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+ source_sents = []
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+ target_sents = []
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+ source_langs = []
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+ target_langs = []
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+
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+ for source_file, target_file, (lang_1, lang_2) in zip(source_files, target_files, language_pairs):
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+ with open(source_file, encoding="utf-8") as f1:
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+ for row in f1:
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+ source_sents.append(row.strip())
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+ source_langs.append(lang_1)
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+ with open(target_file, encoding="utf-8") as f2:
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+ for row in f2:
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+ target_sents.append(row.strip())
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+ target_langs.append(lang_2)
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+
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+ for idx, (source, target, lang_src, lang_tgt) in enumerate(zip(source_sents, target_sents, source_langs, target_langs)):
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+ if self.config.schema == "source":
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+ example = {
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+ "source_sentence": source,
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+ "target_sentence": target,
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+ # The source_lang in the HuggingFace source seems incorrect
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+ # I am overriding it with the actual language code.
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+ "source_lang": lang_src,
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+ "target_lang": lang_tgt,
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+ }
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+ elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
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+ example = {
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+ "id": str(idx),
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+ "text_1": source,
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+ "text_2": target,
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+ # The source_lang in the HuggingFace source seems incorrect
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+ # I am overriding it with the actual language code.
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+ "text_1_name": lang_src,
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+ "text_2_name": lang_tgt,
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+ }
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+ yield idx, example