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import os |
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import re |
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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{limkonchotiwat-etal-2021-handling, |
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title = "Handling Cross- and Out-of-Domain Samples in {T}hai Word Segmentation", |
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author = "Limkonchotiwat, Peerat and |
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Phatthiyaphaibun, Wannaphong and |
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Sarwar, Raheem and |
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Chuangsuwanich, Ekapol and |
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Nutanong, Sarana", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.findings-acl.86", |
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doi = "10.18653/v1/2021.findings-acl.86", |
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pages = "1003--1016", |
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} |
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""" |
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_DATASETNAME = "vistec_tp_th_21" |
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_DESCRIPTION = """\ |
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The largest social media domain datasets for Thai text processing (word segmentation, |
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misspell correction and detection, and named-entity boundary) called "VISTEC-TP-TH-2021" or VISTEC-2021. |
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VISTEC corpus contains 49,997 sentences with 3.39M words where the collection was manually annotated by |
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linguists on four tasks, namely word segmentation, misspelling detection and correction, |
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and named entity recognition. |
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""" |
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_HOMEPAGE = "https://github.com/mrpeerat/OSKut/tree/main/VISTEC-TP-TH-2021" |
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_LANGUAGES = ["tha"] |
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_LICENSE = Licenses.CC_BY_SA_3_0.value |
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_LOCAL = False |
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_URLS = { |
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"train": "https://raw.githubusercontent.com/mrpeerat/OSKut/main/VISTEC-TP-TH-2021/train/VISTEC-TP-TH-2021_train_proprocessed.txt", |
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"test": "https://raw.githubusercontent.com/mrpeerat/OSKut/main/VISTEC-TP-TH-2021/test/VISTEC-TP-TH-2021_test_proprocessed.txt", |
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} |
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_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class VISTEC21Dataset(datasets.GeneratorBasedBuilder): |
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""" |
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The largest social media domain datasets for Thai text processing (word segmentation, |
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misspell correction and detection, and named-entity boundary) called "VISTEC-TP-TH-2021" or VISTEC-2021. |
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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 = "seq_label" |
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LABEL_CLASSES = ["0", "1"] |
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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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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"ner_tags": datasets.Sequence(datasets.features.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.seq_label_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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data_files = { |
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"train": Path(dl_manager.download_and_extract(_URLS["train"])), |
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"test": Path(dl_manager.download_and_extract(_URLS["test"])), |
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} |
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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": data_files["train"], "split": "train"}, |
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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": data_files["test"], "split": "test"}, |
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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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label_key = "ner_tags" if self.config.schema == "source" else "labels" |
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with open(filepath, "r", encoding="utf-8") as f: |
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lines = f.readlines() |
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id = 0 |
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for line in lines: |
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tokens = line.split("|") |
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token_list = [] |
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ner_tag = [] |
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for token in tokens: |
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if "<ne>" in token: |
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token = token.replace("<ne>", "") |
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token = token.replace("</ne>", "") |
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token_list.append(token) |
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ner_tag.append(1) |
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continue |
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if "</msp>" in token and "<msp value=" in token: |
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token_list.append(re.findall(r"<msp value=([^>]*)>", token)[0]) |
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ner_tag.append(0) |
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continue |
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if "<compound>" in token or "</compound>" in token: |
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token = token.replace("<compound>", "") |
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token = token.replace("</compound>", "") |
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token_list.append(token) |
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ner_tag.append(0) |
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continue |
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token_list.append(token) |
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ner_tag.append(0) |
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id += 1 |
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yield id, { |
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"id": str(id), |
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"tokens": token_list, |
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label_key: ner_tag, |
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} |
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