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…wd#489) * add dataloader for PhoMT dataset * Update seacrowd/sea_datasets/phomt/phomt.py Co-authored-by: Elyanah Aco <[email protected]> * Update seacrowd/sea_datasets/phomt/phomt.py Co-authored-by: Elyanah Aco <[email protected]> * Update seacrowd/sea_datasets/phomt/phomt.py Co-authored-by: Elyanah Aco <[email protected]> * Update seacrowd/sea_datasets/phomt/phomt.py Co-authored-by: Elyanah Aco <[email protected]> * Update seacrowd/sea_datasets/phomt/phomt.py Co-authored-by: Elyanah Aco <[email protected]> * update text1/2 name for PhoMT dataset * Update phomt.py to replace en&vi to eng&vie --------- Co-authored-by: Elyanah Aco <[email protected]>
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import os | ||
from pathlib import Path | ||
from typing import Dict, List, Tuple | ||
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import datasets | ||
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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 = """\ | ||
@inproceedings{PhoMT, | ||
title = {{PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation}}, | ||
author = {Long Doan and Linh The Nguyen and Nguyen Luong Tran and Thai Hoang and Dat Quoc Nguyen}, | ||
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, | ||
year = {2021}, | ||
pages = {4495--4503} | ||
} | ||
""" | ||
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_DATASETNAME = "phomt" | ||
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_DESCRIPTION = """\ | ||
PhoMT is a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M | ||
pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. This is the first large-scale | ||
Vietnamese-English machine translation study. | ||
""" | ||
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_LANGUAGES = ["vie", "eng"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) | ||
_LOCAL = True | ||
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_HOMEPAGE = "https://github.com/VinAIResearch/PhoMT" | ||
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_LICENSE = Licenses.MIT.value | ||
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] | ||
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_SOURCE_VERSION = "1.0.0" | ||
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_SEACROWD_VERSION = "1.0.0" | ||
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MAP_LANG = {"eng": "en", "vie": "vi"} | ||
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def seacrowd_config_constructor(src_lang, tgt_lang, schema, version): | ||
if src_lang == "" or tgt_lang == "": | ||
raise ValueError(f"Invalid src_lang {src_lang} or tgt_lang {tgt_lang}") | ||
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if schema not in ["source", "seacrowd_t2t"]: | ||
raise ValueError(f"Invalid schema: {schema}") | ||
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return SEACrowdConfig( | ||
name="phomt_{src}_{tgt}_{schema}".format(src=src_lang, tgt=tgt_lang, schema=schema), | ||
version=datasets.Version(version), | ||
description="phomt schema for {schema} from {src} to {tgt}".format(schema=schema, src=src_lang, tgt=tgt_lang), | ||
schema=schema, | ||
subset_id="phomt_{src}_{tgt}".format(src=src_lang, tgt=tgt_lang), | ||
) | ||
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class PhoMT(datasets.GeneratorBasedBuilder): | ||
""" | ||
PhoMT is a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is | ||
2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. | ||
""" | ||
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | ||
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | ||
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BUILDER_CONFIGS = [ | ||
seacrowd_config_constructor("eng", "vie", "source", _SOURCE_VERSION), | ||
seacrowd_config_constructor("eng", "vie", "seacrowd_t2t", _SEACROWD_VERSION), | ||
] | ||
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DEFAULT_CONFIG_NAME = "phomt_eng_vie_source" | ||
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def _info(self) -> datasets.DatasetInfo: | ||
if self.config.schema in ("source", "seacrowd_t2t"): | ||
features = schemas.text2text_features | ||
else: | ||
raise ValueError(f"Invalid config schema: {self.config.schema}") | ||
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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.""" | ||
if self.config.data_dir is None: | ||
raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.") | ||
else: | ||
data_dir = self.config.data_dir | ||
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return [ | ||
datasets.SplitGenerator( | ||
name=datasets.Split.TRAIN, | ||
gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "train", "train.{lang}")}, | ||
), | ||
datasets.SplitGenerator( | ||
name=datasets.Split.VALIDATION, | ||
gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "dev", "dev.{lang}")}, | ||
), | ||
datasets.SplitGenerator( | ||
name=datasets.Split.TEST, | ||
gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "test", "test.{lang}")}, | ||
), | ||
] | ||
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: | ||
config_names_split = self.config.name.split("_") | ||
src_lang = config_names_split[1] | ||
tgt_lang = config_names_split[2] | ||
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src_path = filepath.format(lang=MAP_LANG[src_lang]) | ||
tgt_path = filepath.format(lang=MAP_LANG[tgt_lang]) | ||
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with open(src_path, "r", encoding="utf8") as f: | ||
src_lines = f.readlines() | ||
with open(tgt_path, "r", encoding="utf8") as f: | ||
tgt_lines = f.readlines() | ||
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if self.config.schema in ("source", "seacrowd_t2t"): | ||
for idx, (src_line, tgt_line) in enumerate(zip(src_lines, tgt_lines)): | ||
ex = { | ||
"id": str(idx), | ||
"text_1": src_line.strip(), | ||
"text_2": tgt_line.strip(), | ||
"text_1_name": src_lang, | ||
"text_2_name": tgt_lang, | ||
} | ||
yield idx, ex | ||
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else: | ||
raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") |