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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{fujita2021empirical, |
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title={An Empirical Investigation of Online News Classification on an Open-Domain, Large-Scale and High-Quality Dataset in Vietnamese}, |
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author={Fujita, H and Perez-Meana, H}, |
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booktitle={New Trends in Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 20th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT_21)}, |
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volume={337}, |
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pages={367}, |
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year={2021}, |
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organization={IOS Press} |
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} |
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""" |
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_DATASETNAME = "uit_vion" |
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_DESCRIPTION = """\ |
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UIT-ViON (Vietnamese Online Newspaper) is a dataset collected from well-known online newspapers in Vietnamese. |
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The UIT-ViON is an open-domain, large-scale, and high-quality dataset consisting of 260,000 textual data |
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points annotated with 13 different categories for evaluating Vietnamese short text classification. |
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The dataset is split into training, validation, and test sets, each containing 208000, 26000, |
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and 26000 pieces of text, respectively. |
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""" |
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_HOMEPAGE = "https://github.com/kh4nh12/UIT-ViON-Dataset" |
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_LANGUAGES = ["vie"] |
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_LICENSE = Licenses.UNKNOWN.value |
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_LOCAL = False |
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_URLS = { |
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_DATASETNAME: "https://github.com/kh4nh12/UIT-ViON-Dataset/archive/refs/heads/master.zip", |
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} |
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_SUPPORTED_TASKS = [Tasks.TOPIC_MODELING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class UitVion(datasets.GeneratorBasedBuilder): |
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"""UIT-ViON (Vietnamese Online Newspaper) is a dataset collected from well-known online newspapers in Vietnamese.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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LABEL_CLASSES = [i for i in range(13)] |
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SEACROWD_SCHEMA_NAME = "text" |
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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=_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=_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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"title": datasets.Value("string"), |
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"link": datasets.Value("string"), |
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"label": datasets.ClassLabel(names=self.LABEL_CLASSES), |
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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.text_features(self.LABEL_CLASSES) |
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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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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(urls) |
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file_dir = os.path.join("UIT-ViON-Dataset-main", "data.zip") |
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data_dir = os.path.join(data_dir, file_dir) |
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data_dir = dl_manager.download_and_extract(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={ |
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"filepath": os.path.join(data_dir, "UIT-ViON_train.csv"), |
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"split": "train", |
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}, |
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), |
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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, "UIT-ViON_test.csv"), |
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"split": "test", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "UIT-ViON_dev.csv"), |
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"split": "dev", |
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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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data = pd.read_csv(filepath) |
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if self.config.schema == "source": |
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for i, row in data.iterrows(): |
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yield i, { |
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"title": str(row["title"]), |
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"link": str(row["link"]), |
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"label": row["label"], |
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} |
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
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for i, row in data.iterrows(): |
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yield i, { |
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"id": str(i), |
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"text": str(row["title"]), |
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"label": int(row["label"]), |
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} |
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