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visobert.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{nguyen-etal-2023-visobert,
+    title = "{V}i{S}o{BERT}: A Pre-Trained Language Model for {V}ietnamese Social Media Text Processing",
+    author = "Nguyen, Nam  and
+      Phan, Thang  and
+      Nguyen, Duc-Vu  and
+      Nguyen, Kiet",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.315",
+    pages = "5191--5207",
+    abstract = "English and Chinese, known as resource-rich languages, have witnessed the strong
+    development of transformer-based language models for natural language processing tasks. Although
+    Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT,
+    ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and
+    named entity recognition. These pre-trained language models are still limited to Vietnamese social
+    media tasks. In this paper, we present the first monolingual pre-trained language model for
+    Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality
+    and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our
+    pre-trained model on five important natural language downstream tasks on Vietnamese social media
+    texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and
+    hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters,
+    surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our
+    ViSoBERT model is available only for research purposes. Disclaimer: This paper contains actual
+    comments on social networks that might be construed as abusive, offensive, or obscene.",
+}
+"""
+
+_DATASETNAME = "visobert"
+
+_DESCRIPTION = """\
+The ViSoBERT corpus is composed of Vietnamese textual data crawled from Facebook, TikTok, and YouTube. The
+dataset contains Facebook posts, TikTok comments, and Youtube comments of Vietnamese-verified users, from
+Jan 2016 (Jan 2020 for TikTok) to Dec 2022. A post-processing mechanism is applied to handles hashtags,
+emojis, misspellings, hyperlinks, and other noncanonical texts.
+"""
+
+_HOMEPAGE = "https://huggingface.co/uitnlp/visobert"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.CC_BY_NC_4_0.value
+
+_LOCAL = False
+
+_URLS = "https://drive.usercontent.google.com/download?id=1BoiR9k2DrjBcd2aHy5BOq4haEp5V2_ug&confirm=xxx"
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ViSoBERTDataset(datasets.GeneratorBasedBuilder):
+    """
+    The ViSoBERT corpus is a Vietnamese pretraining dataset from https://huggingface.co/uitnlp/visobert.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_ssp",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_ssp",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source" or self.config.schema == "seacrowd_ssp":
+            features = schemas.self_supervised_pretraining.features
+        else:
+            raise ValueError(f"Invalid schema: '{self.config.schema}'")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """
+        Returns SplitGenerators.
+        """
+
+        path = dl_manager.download(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+
+        with open(filepath, "r", encoding="utf-8") as f:
+            if self.config.schema == "source" or self.config.schema == "seacrowd_ssp":
+                for idx, row in enumerate(f):
+                    if row.strip() != "":
+                        yield (
+                            idx,
+                            {
+                                "id": str(idx),
+                                "text": row.strip(),
+                            },
+                        )
+            else:
+                raise ValueError(f"Invalid config: '{self.config.name}'")