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e4b9a7b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | # -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
import re
import warnings
from typing import List, Tuple, Union
from transformers import (
CamembertTokenizer,
pipeline,
)
from pythainlp.tokenize import word_tokenize
_model_name = "wangchanberta-base-att-spm-uncased"
_tokenizer = CamembertTokenizer.from_pretrained(
f"airesearch/{_model_name}", revision="main"
)
if _model_name == "wangchanberta-base-att-spm-uncased":
_tokenizer.additional_special_tokens = ["<s>NOTUSED", "</s>NOTUSED", "<_>"]
class ThaiNameTagger:
def __init__(
self, dataset_name: str = "thainer", grouped_entities: bool = True
):
"""
This function tags named entities in text in IOB format.
Powered by wangchanberta from VISTEC-depa\
AI Research Institute of Thailand
:param str dataset_name:
* *thainer* - ThaiNER dataset
:param bool grouped_entities: grouped entities
"""
self.dataset_name = dataset_name
self.grouped_entities = grouped_entities
self.classify_tokens = pipeline(
task="ner",
tokenizer=_tokenizer,
model=f"airesearch/{_model_name}",
revision=f"finetuned@{self.dataset_name}-ner",
ignore_labels=[],
grouped_entities=self.grouped_entities,
)
def _IOB(self, tag):
if tag != "O":
return "B-" + tag
return "O"
def _clear_tag(self, tag):
return tag.replace("B-", "").replace("I-", "")
def get_ner(
self, text: str, pos: bool = False, tag: bool = False
) -> Union[List[Tuple[str, str]], str]:
"""
This function tags named entities in text in IOB format.
Powered by wangchanberta from VISTEC-depa\
AI Research Institute of Thailand
:param str text: text in Thai to be tagged
:param bool tag: output HTML-like tags.
:return: a list of tuples associated with tokenized word groups,\
NER tags, and output HTML-like tags (if the parameter `tag` is \
specified as `True`). \
Otherwise, return a list of tuples associated with tokenized \
words and NER tags
:rtype: Union[list[tuple[str, str]]], str
"""
if pos:
warnings.warn(
"This model doesn't support output of POS tags and it doesn't output the POS tags."
)
text = re.sub(" ", "<_>", text)
self.json_ner = self.classify_tokens(text)
self.output = ""
if self.grouped_entities and self.dataset_name == "thainer":
self.sent_ner = [
(
i["word"].replace("<_>", " ").replace("▁", ""),
self._IOB(i["entity_group"]),
)
for i in self.json_ner
]
elif self.dataset_name == "thainer":
self.sent_ner = [
(i["word"].replace("<_>", " ").replace("▁", ""), i["entity"])
for i in self.json_ner
if i["word"] != "▁"
]
else:
self.sent_ner = [
(
i["word"].replace("<_>", " ").replace("▁", ""),
i["entity"].replace("_", "-").replace("E-", "I-"),
)
for i in self.json_ner
]
if self.sent_ner[0][0] == "" and len(self.sent_ner) > 1:
self.sent_ner = self.sent_ner[1:]
for idx, (word, ner) in enumerate(self.sent_ner):
if idx > 0 and ner.startswith("B-"):
if self._clear_tag(ner) == self._clear_tag(
self.sent_ner[idx - 1][1]
):
self.sent_ner[idx] = (word, ner.replace("B-", "I-"))
if tag:
temp = ""
sent = ""
for idx, (word, ner) in enumerate(self.sent_ner):
if ner.startswith("B-") and temp != "":
sent += "</" + temp + ">"
temp = ner[2:]
sent += "<" + temp + ">"
elif ner.startswith("B-"):
temp = ner[2:]
sent += "<" + temp + ">"
elif ner == "O" and temp != "":
sent += "</" + temp + ">"
temp = ""
sent += word
if idx == len(self.sent_ner) - 1 and temp != "":
sent += "</" + temp + ">"
return sent
else:
return self.sent_ner
class NamedEntityRecognition:
def __init__(
self, model: str = "pythainlp/thainer-corpus-v2-base-model"
) -> None:
"""
This function tags named entities in text in IOB format.
Powered by wangchanberta from VISTEC-depa\
AI Research Institute of Thailand
:param str model: The model that use wangchanberta pretrained.
"""
from transformers import AutoModelForTokenClassification, AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model)
self.model = AutoModelForTokenClassification.from_pretrained(model)
def _fix_span_error(self, words, ner):
_ner = []
_ner = ner
_new_tag = []
for i, j in zip(words, _ner):
i = self.tokenizer.decode(i)
if i.isspace() and j.startswith("B-"):
j = "O"
if i in ("", "<s>", "</s>"):
continue
if i == "<_>":
i = " "
_new_tag.append((i, j))
return _new_tag
def get_ner(
self, text: str, pos: bool = False, tag: bool = False
) -> Union[List[Tuple[str, str]], str]:
"""
This function tags named entities in text in IOB format.
Powered by wangchanberta from VISTEC-depa\
AI Research Institute of Thailand
:param str text: text in Thai to be tagged
:param bool tag: output HTML-like tags.
:return: a list of tuples associated with tokenized word groups, NER tags, \
and output HTML-like tags (if the parameter `tag` is \
specified as `True`). \
Otherwise, return a list of tuples associated with tokenized \
words and NER tags
:rtype: Union[list[tuple[str, str]]], str
"""
import torch
if pos:
warnings.warn(
"This model doesn't support output postag and It doesn't output the postag."
)
words_token = word_tokenize(text.replace(" ", "<_>"))
inputs = self.tokenizer(
words_token, is_split_into_words=True, return_tensors="pt"
)
ids = inputs["input_ids"]
mask = inputs["attention_mask"]
# forward pass
outputs = self.model(ids, attention_mask=mask)
logits = outputs[0]
predictions = torch.argmax(logits, dim=2)
predicted_token_class = [
self.model.config.id2label[t.item()] for t in predictions[0]
]
ner_tag = self._fix_span_error(
inputs["input_ids"][0], predicted_token_class
)
if tag:
temp = ""
sent = ""
for idx, (word, ner) in enumerate(ner_tag):
if ner.startswith("B-") and temp != "":
sent += "</" + temp + ">"
temp = ner[2:]
sent += "<" + temp + ">"
elif ner.startswith("B-"):
temp = ner[2:]
sent += "<" + temp + ">"
elif ner == "O" and temp != "":
sent += "</" + temp + ">"
temp = ""
sent += word
if idx == len(ner_tag) - 1 and temp != "":
sent += "</" + temp + ">"
return sent
return ner_tag
def segment(text: str) -> List[str]:
"""
Subword tokenize. SentencePiece from wangchanberta model.
:param str text: text to be tokenized
:return: list of subwords
:rtype: list[str]
"""
if not text or not isinstance(text, str):
return []
return _tokenizer.tokenize(text)
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