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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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import pandas as pd |
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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{nguyen-etal-2018-introducing, |
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title = "Introducing Two {V}ietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness", |
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author = "Nguyen, Kim Anh and |
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Schulte im Walde, Sabine and |
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Vu, Ngoc Thang", |
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editor = "Walker, Marilyn and |
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Ji, Heng and |
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Stent, Amanda", |
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booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)", |
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month = jun, |
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year = "2018", |
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address = "New Orleans, Louisiana", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/N18-2032", |
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doi = "10.18653/v1/N18-2032", |
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pages = "199--205" |
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} |
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""" |
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_DATASETNAME = "visim400" |
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_DESCRIPTION = """\ |
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ViSim-400 is a Vietnamese dataset of semantic relation \ |
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pairs for evaluation of models that reflect the \ |
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continuum between similarity and relatedness. |
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We choose 'Sim2' instead of 'Sim1' for the label output of \ |
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our SEACrowd dataloader schema because it's been normalized to [1, 10]. |
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""" |
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_HOMEPAGE = "https://www.ims.uni-stuttgart.de/forschung/ressourcen/experiment-daten/vnese-sem-datasets/" |
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_LANGUAGES = ["vie"] |
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_LICENSE = Licenses.CC_BY_NC_SA_4_0.value |
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_LOCAL = False |
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_URLS = {_DATASETNAME: "https://www.ims.uni-stuttgart.de/documents/ressourcen/experiment-daten/ViData.zip"} |
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_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class ViSim400Dataset(datasets.GeneratorBasedBuilder): |
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""" |
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ViSim-400 is a Vietnamese dataset of semantic relation \ |
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pairs for evaluation of models that reflect the \ |
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continuum between similarity and relatedness. |
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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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SEACROWD_SCHEMA_NAME = "pairs_score" |
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BUILDER_CONFIGS = [ |
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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", |
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schema="source", |
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subset_id=f"{_DATASETNAME}", |
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), |
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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", |
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schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", |
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subset_id=f"{_DATASETNAME}", |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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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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"Word1": datasets.Value("string"), |
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"Word2": datasets.Value("string"), |
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"POS": datasets.Value("string"), |
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"Sim1": datasets.Value("string"), |
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"Sim2": datasets.Value("string"), |
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"STD": 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.pairs_features_score() |
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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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data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "ViData/ViSim-400/Visim-400.txt"), |
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"split": "test", |
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}, |
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) |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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with open(filepath, "r", encoding="utf-8") as file: |
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lines = file.readlines() |
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data = [] |
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for line in lines: |
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columns = line.strip().split("\t") |
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data.append(columns) |
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df = pd.DataFrame(data[1:], columns=data[0]) |
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for index, row in df.iterrows(): |
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if self.config.schema == "source": |
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example = row.to_dict() |
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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(index), |
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"text_1": str(row["Word1"]), |
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"text_2": str(row["Word2"]), |
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"label": str(row["Sim2"]), |
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} |
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yield index, example |
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