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Upload orchid_pos.py with huggingface_hub
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orchid_pos.py
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+
# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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+
#
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# http://www.apache.org/licenses/LICENSE-2.0
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+
#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+
# See the License for the specific language governing permissions and
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+
# limitations under the License.
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15 |
+
import os
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16 |
+
import re
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17 |
+
from pathlib import Path
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+
from typing import Dict, List, Tuple
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19 |
+
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+
import datasets
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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 Licenses, Tasks
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+
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_CITATION = """\
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@article{sornlertlamvanich1999building,
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title={Building a Thai part-of-speech tagged corpus (ORCHID)},
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author={Sornlertlamvanich, Virach and Takahashi, Naoto and Isahara, Hitoshi},
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+
journal={Journal of the Acoustical Society of Japan (E)},
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+
volume={20},
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+
number={3},
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pages={189--198},
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year={1999},
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publisher={Acoustical Society of Japan}
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}
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+
"""
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+
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_DATASETNAME = "orchid_pos"
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+
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+
_DESCRIPTION = """\
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The ORCHID corpus is a Thai part-of-speech (POS) tagged dataset, resulting from a collaboration between\
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Japan's Communications Research Laboratory (CRL) and Thailand's National Electronics and Computer Technology\
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+
Center (NECTEC). It is structured at three levels: paragraph, sentence, and word. The dataset incorporates a\
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+
unique tagset designed for use in multi-lingual machine translation projects, and is tailored to address the\
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+
challenges of Thai text, which lacks explicit word and sentence boundaries, punctuation, and inflection.\
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+
This dataset includes text information along with numbering for retrieval, and employs a probabilistic trigram\
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model for word segmentation and POS tagging. The ORCHID corpus is specifically structured to reduce ambiguity in\
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POS assignments, making it a valuable resource for Thai language processing and computational linguistics research.
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+
"""
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+
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_HOMEPAGE = "https://github.com/wannaphong/corpus_mirror/releases/tag/orchid-v1.0"
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53 |
+
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+
_LANGUAGES = ["tha"]
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+
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_LICENSE = Licenses.CC_BY_NC_SA_3_0.value
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+
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_LOCAL = False
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+
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_URLS = {
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_DATASETNAME: "https://github.com/wannaphong/corpus_mirror/releases/download/orchid-v1.0/orchid97.crp.utf",
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+
}
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+
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_SUPPORTED_TASKS = [Tasks.POS_TAGGING]
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+
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_SOURCE_VERSION = "1.0.0"
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+
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_SEACROWD_VERSION = "2024.06.20"
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+
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class OrchidPOSDataset(datasets.GeneratorBasedBuilder):
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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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+
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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=f"{_DATASETNAME}",
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+
),
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+
SEACrowdConfig(
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name=f"{_DATASETNAME}_seacrowd_seq_label",
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+
version=SEACROWD_VERSION,
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+
description=f"{_DATASETNAME} SEACrowd schema",
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+
schema="seacrowd_seq_label",
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89 |
+
subset_id=f"{_DATASETNAME}",
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+
),
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91 |
+
]
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+
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+
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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+
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def _info(self) -> datasets.DatasetInfo:
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label_names = [
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"NPRP",
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"NCNM",
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"NONM",
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"NLBL",
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"NCMN",
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"NTTL",
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"PPRS",
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"PDMN",
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"PNTR",
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"PREL",
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"VACT",
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"VSTA",
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"VATT",
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"XVBM",
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"XVAM",
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"XVMM",
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"XVBB",
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+
"XVAE",
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+
"DDAN",
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116 |
+
"DDAC",
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117 |
+
"DDBQ",
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+
"DDAQ",
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+
"DIAC",
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120 |
+
"DIBQ",
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+
"DIAQ",
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+
"DCNM",
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+
"DONM",
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+
"ADVN",
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+
"ADVI",
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126 |
+
"ADVP",
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+
"ADVS",
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+
"CNIT",
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+
"CLTV",
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130 |
+
"CMTR",
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131 |
+
"CFQC",
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+
"CVBL",
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+
"JCRG",
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+
"JCMP",
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+
"JSBR",
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+
"RPRE",
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+
"INT",
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+
"FIXN",
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"FIXV",
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+
"EAFF",
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"EITT",
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"NEG",
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"PUNC",
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"CMTR@PUNC",
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]
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+
if self.config.schema == "source":
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+
features = datasets.Features(
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+
{
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+
"ttitle": datasets.Value("string"),
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+
"etitle": datasets.Value("string"),
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151 |
+
"tauthor": datasets.Value("string"),
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+
"eauthor": datasets.Value("string"),
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+
"tinbook": datasets.Value("string"),
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154 |
+
"einbook": datasets.Value("string"),
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+
"tpublisher": datasets.Value("string"),
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+
"epublisher": datasets.Value("string"),
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+
"year": datasets.Value("string"),
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+
"file": datasets.Value("string"),
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+
"tokens": datasets.Sequence(datasets.Value("string")),
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"labels": datasets.Sequence(datasets.ClassLabel(names=label_names)),
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+
}
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+
)
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+
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+
elif self.config.schema == "seacrowd_seq_label":
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features = schemas.seq_label_features(label_names)
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+
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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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172 |
+
citation=_CITATION,
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+
)
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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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179 |
+
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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, ""),
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"split": "train",
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},
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)
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]
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+
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def _get_tokens_labels(self, paragraphs):
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tokens = []
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+
labels = []
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token_mapping = {
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"<space>": " ",
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195 |
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"<exclamation>": "!",
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"<quotation>": '"',
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"<number>": "#",
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"<dollar>": "$",
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"<percent>": "%",
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"<ampersand>": "&",
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"<apostrophe>": "'",
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+
"<slash>": "/",
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203 |
+
"<colon>": ":",
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204 |
+
"<semi_colon>": ";",
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205 |
+
"<less_than>": "<",
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+
"<equal>": "=",
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+
"<greater than>": ">",
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+
"<question_mark>": "?",
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+
"<at_mark>": "@",
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210 |
+
"<left_parenthesis>": "(",
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+
"<left_square_bracket>": "[",
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+
"<right_parenthesis>": ")",
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213 |
+
"<right_square_bracket>": "]",
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+
"<asterisk>": "*",
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+
"<circumflex_accent>": "^",
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216 |
+
"<plus>": "+",
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217 |
+
"<low_line>": "_",
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218 |
+
"<comma>": ",",
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219 |
+
"left_curly_bracket": "{",
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220 |
+
"<minus>": "-",
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221 |
+
"<right_curly_bracket>": "}",
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222 |
+
"<full_stop>": ".",
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223 |
+
"<tilde>": "~",
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}
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225 |
+
for paragraph in paragraphs:
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sentences = re.split(r"#\d+\n", paragraph)
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for sentence in sentences[1:]:
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token_pos_pairs = sentence.split("//")[1]
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229 |
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for token_pos_pair in token_pos_pairs.split("\n")[1:-1]:
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if "/" in token_pos_pair:
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token = token_pos_pair.split("/")[0]
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232 |
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tokens.append(token_mapping[token] if token in token_mapping.keys() else token)
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labels.append(token_pos_pair.split("/")[1])
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+
else:
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token = token_pos_pair.split("@")[0]
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236 |
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tokens.append(token_mapping[token] if token in token_mapping.keys() else token)
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237 |
+
labels.append(token_pos_pair.split("@")[1])
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238 |
+
return tokens, labels
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239 |
+
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240 |
+
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
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241 |
+
"""Yields examples as (key, example) tuples."""
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242 |
+
file_content = open(filepath, "r").read()
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243 |
+
texts = file_content.split("%TTitle:")
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244 |
+
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245 |
+
idx = 0
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246 |
+
for text in texts[1:]:
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247 |
+
file_part = text.split("%File")[-1]
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248 |
+
tokens, labels = self._get_tokens_labels(re.split(r"#P\d+\n", file_part)[1:])
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249 |
+
if self.config.schema == "source":
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250 |
+
parts = text.split("%")
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251 |
+
example = {
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252 |
+
"ttitle": parts[0],
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253 |
+
"etitle": ":".join(parts[1].split(":")[1:]).strip(),
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254 |
+
"tauthor": ":".join(parts[2].split(":")[1:]).strip(),
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255 |
+
"eauthor": ":".join(parts[3].split(":")[1:]).strip(),
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256 |
+
"tinbook": ":".join(parts[4].split(":")[1:]).strip(),
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257 |
+
"einbook": ":".join(parts[5].split(":")[1:]).strip(),
|
258 |
+
"tpublisher": ":".join(parts[6].split(":")[1:]).strip(),
|
259 |
+
"epublisher": ":".join(parts[7].split(":")[1:]).strip(),
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260 |
+
"year": ":".join(parts[9].split(":")[1:]).strip(),
|
261 |
+
"file": file_part.strip(),
|
262 |
+
"tokens": tokens,
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263 |
+
"labels": labels,
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264 |
+
}
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265 |
+
elif self.config.schema == "seacrowd_seq_label":
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266 |
+
example = {
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267 |
+
"id": idx,
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268 |
+
"tokens": tokens,
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269 |
+
"labels": labels,
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270 |
+
}
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+
yield idx, example
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+
idx += 1
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