Upload Inference.ipynb

Inference.ipynb

@@ -0,0 +1,248 @@
+{
+ "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
+}