File size: 6,744 Bytes
b386b9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
# 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.
import os
import re
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{limkonchotiwat-etal-2021-handling,
title = "Handling Cross- and Out-of-Domain Samples in {T}hai Word Segmentation",
author = "Limkonchotiwat, Peerat and
Phatthiyaphaibun, Wannaphong and
Sarwar, Raheem and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.86",
doi = "10.18653/v1/2021.findings-acl.86",
pages = "1003--1016",
}
"""
_DATASETNAME = "vistec_tp_th_21"
_DESCRIPTION = """\
The largest social media domain datasets for Thai text processing (word segmentation,
misspell correction and detection, and named-entity boundary) called "VISTEC-TP-TH-2021" or VISTEC-2021.
VISTEC corpus contains 49,997 sentences with 3.39M words where the collection was manually annotated by
linguists on four tasks, namely word segmentation, misspelling detection and correction,
and named entity recognition.
"""
_HOMEPAGE = "https://github.com/mrpeerat/OSKut/tree/main/VISTEC-TP-TH-2021"
_LANGUAGES = ["tha"]
_LICENSE = Licenses.CC_BY_SA_3_0.value
_LOCAL = False
_URLS = {
"train": "https://raw.githubusercontent.com/mrpeerat/OSKut/main/VISTEC-TP-TH-2021/train/VISTEC-TP-TH-2021_train_proprocessed.txt",
"test": "https://raw.githubusercontent.com/mrpeerat/OSKut/main/VISTEC-TP-TH-2021/test/VISTEC-TP-TH-2021_test_proprocessed.txt",
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class VISTEC21Dataset(datasets.GeneratorBasedBuilder):
"""
The largest social media domain datasets for Thai text processing (word segmentation,
misspell correction and detection, and named-entity boundary) called "VISTEC-TP-TH-2021" or VISTEC-2021.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SEACROWD_SCHEMA_NAME = "seq_label"
LABEL_CLASSES = ["0", "1"]
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=_DATASETNAME,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
subset_id=_DATASETNAME,
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=self.LABEL_CLASSES)),
}
)
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
features = schemas.seq_label_features(self.LABEL_CLASSES)
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."""
data_files = {
"train": Path(dl_manager.download_and_extract(_URLS["train"])),
"test": Path(dl_manager.download_and_extract(_URLS["test"])),
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_files["train"], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_files["test"], "split": "test"},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
label_key = "ner_tags" if self.config.schema == "source" else "labels"
with open(filepath, "r", encoding="utf-8") as f:
lines = f.readlines()
id = 0
for line in lines:
tokens = line.split("|")
token_list = []
ner_tag = []
for token in tokens:
if "<ne>" in token:
token = token.replace("<ne>", "")
token = token.replace("</ne>", "")
token_list.append(token)
ner_tag.append(1)
continue
if "</msp>" in token and "<msp value=" in token:
token_list.append(re.findall(r"<msp value=([^>]*)>", token)[0])
ner_tag.append(0)
continue
if "<compound>" in token or "</compound>" in token:
token = token.replace("<compound>", "")
token = token.replace("</compound>", "")
token_list.append(token)
ner_tag.append(0)
continue
token_list.append(token)
ner_tag.append(0)
id += 1
yield id, {
"id": str(id),
"tokens": token_list,
label_key: ner_tag,
}
|