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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{nguyen-etal-2023-visobert, |
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title = "{V}i{S}o{BERT}: A Pre-Trained Language Model for {V}ietnamese Social Media Text Processing", |
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author = "Nguyen, Nam and |
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Phan, Thang and |
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Nguyen, Duc-Vu and |
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Nguyen, Kiet", |
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editor = "Bouamor, Houda and |
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Pino, Juan and |
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Bali, Kalika", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.315", |
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pages = "5191--5207", |
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abstract = "English and Chinese, known as resource-rich languages, have witnessed the strong |
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development of transformer-based language models for natural language processing tasks. Although |
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Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, |
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ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and |
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named entity recognition. These pre-trained language models are still limited to Vietnamese social |
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media tasks. In this paper, we present the first monolingual pre-trained language model for |
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Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality |
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and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our |
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pre-trained model on five important natural language downstream tasks on Vietnamese social media |
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texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and |
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hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, |
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surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our |
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ViSoBERT model is available only for research purposes. Disclaimer: This paper contains actual |
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comments on social networks that might be construed as abusive, offensive, or obscene.", |
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} |
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""" |
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_DATASETNAME = "visobert" |
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_DESCRIPTION = """\ |
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The ViSoBERT corpus is composed of Vietnamese textual data crawled from Facebook, TikTok, and YouTube. The |
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dataset contains Facebook posts, TikTok comments, and Youtube comments of Vietnamese-verified users, from |
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Jan 2016 (Jan 2020 for TikTok) to Dec 2022. A post-processing mechanism is applied to handles hashtags, |
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emojis, misspellings, hyperlinks, and other noncanonical texts. |
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""" |
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_HOMEPAGE = "https://huggingface.co/uitnlp/visobert" |
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_LANGUAGES = ["vie"] |
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_LICENSE = Licenses.CC_BY_NC_4_0.value |
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_LOCAL = False |
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_URLS = "https://drive.usercontent.google.com/download?id=1BoiR9k2DrjBcd2aHy5BOq4haEp5V2_ug&confirm=xxx" |
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_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class ViSoBERTDataset(datasets.GeneratorBasedBuilder): |
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""" |
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The ViSoBERT corpus is a Vietnamese pretraining dataset from https://huggingface.co/uitnlp/visobert. |
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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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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=datasets.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_ssp", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema="seacrowd_ssp", |
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subset_id=f"{_DATASETNAME}", |
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), |
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] |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source" or self.config.schema == "seacrowd_ssp": |
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features = schemas.self_supervised_pretraining.features |
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else: |
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raise ValueError(f"Invalid 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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""" |
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Returns SplitGenerators. |
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""" |
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path = dl_manager.download(_URLS) |
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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": path, |
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"split": "train", |
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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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""" |
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Yields examples as (key, example) tuples. |
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""" |
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with open(filepath, "r", encoding="utf-8") as f: |
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if self.config.schema == "source" or self.config.schema == "seacrowd_ssp": |
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for idx, row in enumerate(f): |
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if row.strip() != "": |
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yield ( |
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idx, |
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{ |
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"id": str(idx), |
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"text": row.strip(), |
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}, |
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) |
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else: |
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raise ValueError(f"Invalid config: '{self.config.name}'") |