File size: 8,737 Bytes
38ca16f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "80b213e0",
"metadata": {},
"outputs": [],
"source": [
"# !pip install termcolor==1.1.0 transformers==4.18.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73f81039",
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"from termcolor import colored\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44668ca1",
"metadata": {},
"outputs": [],
"source": [
"class Ner_Extractor:\n",
" \"\"\"\n",
" Labeling each token in sentence as named entity\n",
"\n",
" :param model_checkpoint: name or path to model \n",
" :type model_checkpoint: string\n",
" \"\"\"\n",
" \n",
" def __init__(self, model_checkpoint: str):\n",
" self.token_pred_pipeline = pipeline(\"token-classification\", \n",
" model=model_checkpoint, \n",
" aggregation_strategy=\"average\")\n",
" \n",
" @staticmethod\n",
" def text_color(txt, txt_c=\"blue\", txt_hglt=\"on_yellow\"):\n",
" \"\"\"\n",
" Coloring part of text \n",
" \n",
" :param txt: part of text from sentence \n",
" :type txt: string\n",
" :param txt_c: text color \n",
" :type txt_c: string \n",
" :param txt_hglt: color of text highlighting \n",
" :type txt_hglt: string\n",
" :return: string with color labeling\n",
" :rtype: string\n",
" \"\"\"\n",
" return colored(txt, txt_c, txt_hglt)\n",
" \n",
" @staticmethod\n",
" def concat_entities(ner_result):\n",
" \"\"\"\n",
" Concatenation entities from model output on grouped entities\n",
" \n",
" :param ner_result: output from model pipeline \n",
" :type ner_result: list\n",
" :return: list of grouped entities with start - end position in text\n",
" :rtype: list\n",
" \"\"\"\n",
" entities = []\n",
" prev_entity = None\n",
" prev_end = 0\n",
" for i in range(len(ner_result)):\n",
" \n",
" if (ner_result[i][\"entity_group\"] == prev_entity) &\\\n",
" (ner_result[i][\"start\"] == prev_end):\n",
" \n",
" entities[i-1][2] = ner_result[i][\"end\"]\n",
" prev_entity = ner_result[i][\"entity_group\"]\n",
" prev_end = ner_result[i][\"end\"]\n",
" else:\n",
" entities.append([ner_result[i][\"entity_group\"], \n",
" ner_result[i][\"start\"], \n",
" ner_result[i][\"end\"]])\n",
" prev_entity = ner_result[i][\"entity_group\"]\n",
" prev_end = ner_result[i][\"end\"]\n",
" \n",
" return entities\n",
" \n",
" \n",
" def colored_text(self, text: str, entities: list):\n",
" \"\"\"\n",
" Highlighting in the text named entities\n",
" \n",
" :param text: sentence or a part of corpus\n",
" :type text: string\n",
" :param entities: concated entities on groups with start - end position in text\n",
" :type entities: list\n",
" :return: Highlighted sentence\n",
" :rtype: string\n",
" \"\"\"\n",
" colored_text = \"\"\n",
" init_pos = 0\n",
" for ent in entities:\n",
" if ent[1] > init_pos:\n",
" colored_text += text[init_pos: ent[1]]\n",
" colored_text += self.text_color(text[ent[1]: ent[2]]) + f\"({ent[0]})\"\n",
" init_pos = ent[2]\n",
" else:\n",
" colored_text += self.text_color(text[ent[1]: ent[2]]) + f\"({ent[0]})\"\n",
" init_pos = ent[2]\n",
" \n",
" return colored_text\n",
" \n",
" \n",
" def get_entities(self, text: str):\n",
" \"\"\"\n",
" Extracting entities from text with them position in text\n",
" \n",
" :param text: input sentence for preparing\n",
" :type text: string\n",
" :return: list with entities from text\n",
" :rtype: list\n",
" \"\"\"\n",
" assert len(text) > 0, text\n",
" entities = self.token_pred_pipeline(text)\n",
" concat_ent = self.concat_entities(entities)\n",
" \n",
" return concat_ent\n",
" \n",
" \n",
" def show_ents_on_text(self, text: str):\n",
" \"\"\"\n",
" Highlighting named entities in input text \n",
" \n",
" :param text: input sentence for preparing\n",
" :type text: string\n",
" :return: Highlighting text\n",
" :rtype: string\n",
" \"\"\"\n",
" assert len(text) > 0, text\n",
" entities = self.get_entities(text)\n",
" \n",
" return self.colored_text(text, entities)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aaa0a5bd",
"metadata": {},
"outputs": [],
"source": [
"seqs_example = [\"Из Дзюбы вышел бы отличный бразилец». Интервью Клаудиньо\",\n",
" \"Самый яркий бразилец «Зенита» рассказал о встрече с Пеле\",\n",
" \"Стали известны подробности нового иска РФС к УЕФА и ФИФА\",\n",
" \"Реванш «Баварии», голы от «Реала» с «Челси»: ставим на ЛЧ\",\n",
" \"Кварацхелия не вернется в «Рубин» и станет игроком «Наполи»\",\n",
" \"«Манчестер Сити» сделал грандиозное предложение по Холанду\",\n",
" \"В России хотят возродить Кубок лиги. Он проводился в 2003 году\",\n",
" \"Экс-игрок «Реала» находится в критическом состоянии после ДТП\",\n",
" \"Аршавин посмеялся над показателями Глушакова в игре с ЦСКА\",\n",
" \"Арьен Роббен пробежал 42-километровый марафон\"\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "380d9824",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"## init model for inference\n",
"extractor = Ner_Extractor(model_checkpoint = \"surdan/LaBSE_ner_nerel\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37ebcf51",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"## get highlighting sentences\n",
"show_entities_in_text = (extractor.show_ents_on_text(i) for i in seqs_example)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e03b28c7",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"## get list of entities from sentence\n",
"l_entities = [extractor.get_entities(i) for i in seqs_example]\n",
"len(l_entities), len(seqs_example)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2d4ae84",
"metadata": {},
"outputs": [],
"source": [
"## print highlighting sentences\n",
"for i in range(len(seqs_example)):\n",
" print(next(show_entities_in_text, \"End of generator\"))\n",
" print(\"-*-\"*25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ce3e083",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|