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Datasets:
SEACrowd
/
thai_constitution

Languages:
Thai
ArXiv:
Tags:
self-supervised-pretraining
License:
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thai_constitution.py ADDED
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1
+ from pathlib import Path
2
+ from typing import Dict, List, Tuple
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+
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+ import datasets
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+ import pandas as pd
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+
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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 (DEFAULT_SEACROWD_VIEW_NAME,
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+ DEFAULT_SOURCE_VIEW_NAME, Tasks, Licenses, TASK_TO_SCHEMA)
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+
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+ _DATASETNAME = "thai_constitution"
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+ _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
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+ _UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
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+
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+ _CITATION = """\
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+ @misc{
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+ thaiconstitution,
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+ title={Thai Constitution Corpus},
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+ url={https://github.com/PyThaiNLP/Thai-constitution-corpus},
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+ author={Wannaphong Phatthiyaphaibun}
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+ }
23
+ """
24
+
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+ _LOCAL = False
26
+
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+ _DESCRIPTION = """\
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+ Thailand's constitutional archive since 1932
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+ - Data collected from Office of the Council of State
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+ - This project is part of the development plan PyThaiNLP
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+ - The information collected in this text archive is in the public domain according to the Copyright Act 1994, Section 7 (The following are not considered copyrighted works under this Act: (1) daily news and Various facts which has the nature of being only news and not work in the literature department Science department or art department [...] (3) regulations, announcements, orders, clarifications and correspondence of ministries, bureaus, departments or any other government or local agencies [...])
32
+ """
33
+
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+ _HOMEPAGE = "https://github.com/PyThaiNLP/Thai-constitution-corpus/tree/master"
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+
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+ _LICENSE = Licenses.CC0_1_0.value
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+
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+ _SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
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+ _SEACROWD_SCHEMA_NAME = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()
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+ _LANGUAGES = ['tha']
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+ _SOURCE_VERSION = "1.0.0"
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+ _SEACROWD_VERSION = "2024.06.20"
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+
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+ _URLS = [
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+ "https://raw.githubusercontent.com/PyThaiNLP/Thai-constitution-corpus/master/data/%E0%B8%A3%E0%B8%B1%E0%B8%90%E0%B8%98%E0%B8%A3%E0%B8%A3%E0%B8%A1%E0%B8%99%E0%B8%B9%E0%B8%8D%E0%B9%81%E0%B8%AB%E0%B9%88%E0%B8%87%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%AD%E0%B8%B2%E0%B8%93%E0%B8%B2%E0%B8%88%E0%B8%B1%E0%B8%81%E0%B8%A3%E0%B9%84%E0%B8%97%E0%B8%A2%20%E0%B8%9E%E0%B8%B8%E0%B8%97%E0%B8%98%E0%B8%A8%E0%B8%B1%E0%B8%81%E0%B8%A3%E0%B8%B2%E0%B8%8A%202475%20%E0%B9%81%E0%B8%81%E0%B9%89%E0%B9%84%E0%B8%82%E0%B9%80%E0%B8%9E%E0%B8%B4%E0%B9%88%E0%B8%A1%E0%B9%80%E0%B8%95%E0%B8%B4%E0%B8%A1%20%E0%B8%9E%E0%B8%B8%E0%B8%97%E0%B8%98%E0%B8%A8%E0%B8%B1%E0%B8%81%E0%B8%A3%E0%B8%B2%E0%B8%8A%202495.txt"
65
+ ]
66
+
67
+
68
+ class ThaiConstitutionDataset(datasets.GeneratorBasedBuilder):
69
+ """Thailand's constitutional archive since 1932"""
70
+
71
+ BUILDER_CONFIGS = [
72
+ SEACrowdConfig(
73
+ name="thai_constitution_source",
74
+ version=datasets.Version(_SOURCE_VERSION),
75
+ description="Thai constitution source schema",
76
+ schema="source",
77
+ subset_id="thai_constitution",
78
+ ),
79
+ SEACrowdConfig(
80
+ name=f"thai_constitution_seacrowd_{_SEACROWD_SCHEMA_NAME}",
81
+ version=datasets.Version(_SEACROWD_VERSION),
82
+ description="Thai constitution SEACrowd schema",
83
+ schema=f"seacrowd_{_SEACROWD_SCHEMA_NAME}",
84
+ subset_id="thai_constitution",
85
+ ),
86
+ ]
87
+
88
+ def _info(self) -> datasets.DatasetInfo:
89
+ if self.config.schema == "source":
90
+ features = datasets.Features(
91
+ {
92
+ "id": datasets.Value("string"),
93
+ "text": datasets.Value("string"),
94
+ }
95
+ )
96
+ elif self.config.schema == f"seacrowd_{_SEACROWD_SCHEMA_NAME}":
97
+ features = schemas.self_supervised_pretraining.features
98
+ else:
99
+ raise ValueError(f"Invalid config schema: {self.config.schema}")
100
+
101
+ return datasets.DatasetInfo(
102
+ description=_DESCRIPTION,
103
+ features=features,
104
+ homepage=_HOMEPAGE,
105
+ license=_LICENSE,
106
+ citation=_CITATION,
107
+ )
108
+
109
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
110
+ filepaths = [Path(dl_manager.download(url)) for url in _URLS]
111
+
112
+ return [
113
+ datasets.SplitGenerator(
114
+ name=datasets.Split.TRAIN,
115
+ gen_kwargs={"filepaths": filepaths},
116
+ ),
117
+ ]
118
+
119
+ def _generate_examples(self, filepaths: List[Path]) -> Tuple[int, Dict]:
120
+ counter = 0
121
+ for path in filepaths:
122
+ with open(path, encoding="utf-8") as f:
123
+ for line in f.readlines():
124
+ if line.strip() == "":
125
+ continue
126
+
127
+ if self.config.schema == "source":
128
+ yield (
129
+ counter,
130
+ {
131
+ "id": str(counter),
132
+ "text": line.strip(),
133
+ },
134
+ )
135
+ elif self.config.schema == f"seacrowd_{_SEACROWD_SCHEMA_NAME}":
136
+ yield (
137
+ counter,
138
+ {
139
+ "id": str(counter),
140
+ "text": line.strip(),
141
+ },
142
+ )
143
+
144
+ counter += 1