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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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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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_CITATION = """\ |
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@inproceedings{PhoMT, |
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title = {{PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation}}, |
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author = {Long Doan and Linh The Nguyen and Nguyen Luong Tran and Thai Hoang and Dat Quoc Nguyen}, |
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booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, |
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year = {2021}, |
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pages = {4495--4503} |
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} |
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""" |
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_DATASETNAME = "phomt" |
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_DESCRIPTION = """\ |
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PhoMT is a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M |
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pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. This is the first large-scale |
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Vietnamese-English machine translation study. |
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""" |
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_LANGUAGES = ["vie", "eng"] |
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_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 = "2024.06.20" |
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MAP_LANG = {"eng": "en", "vie": "vi"} |
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def seacrowd_config_constructor(src_lang, tgt_lang, schema, version): |
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if src_lang == "" or tgt_lang == "": |
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raise ValueError(f"Invalid src_lang {src_lang} or tgt_lang {tgt_lang}") |
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if schema not in ["source", "seacrowd_t2t"]: |
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raise ValueError(f"Invalid schema: {schema}") |
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return SEACrowdConfig( |
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name="phomt_{src}_{tgt}_{schema}".format(src=src_lang, tgt=tgt_lang, schema=schema), |
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version=datasets.Version(version), |
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description="phomt schema for {schema} from {src} to {tgt}".format(schema=schema, src=src_lang, tgt=tgt_lang), |
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schema=schema, |
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subset_id="phomt_{src}_{tgt}".format(src=src_lang, tgt=tgt_lang), |
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) |
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class PhoMT(datasets.GeneratorBasedBuilder): |
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""" |
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PhoMT is a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is |
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2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. |
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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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BUILDER_CONFIGS = [ |
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seacrowd_config_constructor("eng", "vie", "source", _SOURCE_VERSION), |
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seacrowd_config_constructor("eng", "vie", "seacrowd_t2t", _SEACROWD_VERSION), |
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] |
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DEFAULT_CONFIG_NAME = "phomt_eng_vie_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema in ("source", "seacrowd_t2t"): |
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features = schemas.text2text_features |
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else: |
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raise ValueError(f"Invalid config schema: {self.config.schema}") |
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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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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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if self.config.data_dir is None: |
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raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.") |
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else: |
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data_dir = self.config.data_dir |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "train", "train.{lang}")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "dev", "dev.{lang}")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "test", "test.{lang}")}, |
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), |
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] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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config_names_split = self.config.name.split("_") |
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src_lang = config_names_split[1] |
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tgt_lang = config_names_split[2] |
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src_path = filepath.format(lang=MAP_LANG[src_lang]) |
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tgt_path = filepath.format(lang=MAP_LANG[tgt_lang]) |
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with open(src_path, "r", encoding="utf8") as f: |
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src_lines = f.readlines() |
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with open(tgt_path, "r", encoding="utf8") as f: |
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tgt_lines = f.readlines() |
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if self.config.schema in ("source", "seacrowd_t2t"): |
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for idx, (src_line, tgt_line) in enumerate(zip(src_lines, tgt_lines)): |
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ex = { |
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"id": str(idx), |
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"text_1": src_line.strip(), |
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"text_2": tgt_line.strip(), |
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"text_1_name": src_lang, |
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"text_2_name": tgt_lang, |
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} |
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yield idx, ex |
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else: |
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raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") |
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