IOB
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Dominik Strzako
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951
07-parsing-semantyczny-uczenie.ipynb
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951
07-parsing-semantyczny-uczenie.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Parsing semantyczny z wykorzystaniem technik uczenia maszynowego\n",
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"================================================================\n",
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"\n",
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"Wprowadzenie\n",
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"------------\n",
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"Problem wykrywania slotów i ich wartości w wypowiedziach użytkownika można sformułować jako zadanie\n",
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"polegające na przewidywaniu dla poszczególnych słów etykiet wskazujących na to czy i do jakiego\n",
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"slotu dane słowo należy.\n",
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"\n",
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"> chciałbym zarezerwować stolik na jutro**/day** na godzinę dwunastą**/hour** czterdzieści**/hour** pięć**/hour** na pięć**/size** osób\n",
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"\n",
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"Granice slotów oznacza się korzystając z wybranego schematu etykietowania.\n",
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"\n",
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"### Schemat IOB\n",
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"\n",
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"| Prefix | Znaczenie |\n",
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"|:------:|:---------------------------|\n",
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"| I | wnętrze slotu (inside) |\n",
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"| O | poza slotem (outside) |\n",
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"| B | początek slotu (beginning) |\n",
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"\n",
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"> chciałbym zarezerwować stolik na jutro**/B-day** na godzinę dwunastą**/B-hour** czterdzieści**/I-hour** pięć**/I-hour** na pięć**/B-size** osób\n",
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"\n",
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"### Schemat IOBES\n",
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"\n",
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"| Prefix | Znaczenie |\n",
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"|:------:|:---------------------------|\n",
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"| I | wnętrze slotu (inside) |\n",
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"| O | poza slotem (outside) |\n",
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"| B | początek slotu (beginning) |\n",
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"| E | koniec slotu (ending) |\n",
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"| S | pojedyncze słowo (single) |\n",
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"\n",
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"> chciałbym zarezerwować stolik na jutro**/S-day** na godzinę dwunastą**/B-hour** czterdzieści**/I-hour** pięć**/E-hour** na pięć**/S-size** osób\n",
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"\n",
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"Jeżeli dla tak sformułowanego zadania przygotujemy zbiór danych\n",
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"złożony z wypowiedzi użytkownika z oznaczonymi slotami (tzw. *zbiór uczący*),\n",
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"to możemy zastosować techniki (nadzorowanego) uczenia maszynowego w celu zbudowania modelu\n",
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"annotującego wypowiedzi użytkownika etykietami slotów.\n",
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"\n",
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"Do zbudowania takiego modelu można wykorzystać między innymi:\n",
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"\n",
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" 1. warunkowe pola losowe (Lafferty i in.; 2001),\n",
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"\n",
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" 2. rekurencyjne sieci neuronowe, np. sieci LSTM (Hochreiter i Schmidhuber; 1997),\n",
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"\n",
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" 3. transformery (Vaswani i in., 2017).\n",
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"\n",
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"Przykład\n",
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"--------\n",
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"Skorzystamy ze zbioru danych przygotowanego przez Schustera (2019)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"C:\\Users\\domstr2\\l07\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" % Total % Received % Xferd Average Speed Time Time Time Current\n",
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" Dload Upload Total Spent Left Speed\n",
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"\n",
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" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n",
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" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n",
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"\n",
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" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n",
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" 1 8714k 1 95352 0 0 66216 0 0:02:14 0:00:01 0:02:13 93666\n",
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"100 8714k 100 8714k 0 0 4211k 0 0:00:02 0:00:02 --:--:-- 5290k\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"C:\\Users\\domstr2\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"'unzip' is not recognized as an internal or external command,\n",
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"operable program or batch file.\n"
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]
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}
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],
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"source": [
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"!mkdir -p l07\n",
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"%cd l07\n",
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"!curl -L -C - https://fb.me/multilingual_task_oriented_data -o data.zip\n",
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"%cd .."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Zbiór ten gromadzi wypowiedzi w trzech językach opisane slotami dla dwunastu ram należących do trzech dziedzin `Alarm`, `Reminder` oraz `Weather`. Dane wczytamy korzystając z biblioteki [conllu](https://pypi.org/project/conllu/)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: conllu in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (4.4)\n"
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]
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}
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],
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"source": [
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"!pip3 install conllu\n",
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"import codecs\n",
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"from conllu import parse_incr\n",
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"fields = ['id', 'form', 'frame', 'slot']\n",
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"\n",
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"def nolabel2o(line, i):\n",
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" return 'O' if line[i] == 'NoLabel' else line[i]\n",
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"\n",
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"with open('l07/Janet_test.conllu', encoding='utf-8') as trainfile:\n",
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" trainset = list(parse_incr(trainfile, fields=fields, field_parsers={'slot': nolabel2o}))\n",
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"with open('l07/Janet_test.conllu', encoding='utf-8') as testfile:\n",
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" testset = list(parse_incr(testfile, fields=fields, field_parsers={'slot': nolabel2o}))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Zobaczmy kilka przykładowych wypowiedzi z tego zbioru."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: tabulate in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (0.8.9)"
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]
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},
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{
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"data": {
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"text/html": [
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"<table>\n",
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"<tbody>\n",
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"<tr><td style=\"text-align: right;\">1</td><td>hej</td><td>greeting</td><td>O</td></tr>\n",
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"</tbody>\n",
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"</table>"
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],
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"text/plain": [
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"'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>hej</td><td>greeting</td><td>O</td></tr>\\n</tbody>\\n</table>'"
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]
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},
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"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"!pip3 install tabulate\n",
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"from tabulate import tabulate\n",
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"tabulate(trainset[0], tablefmt='html')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table>\n",
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"<tbody>\n",
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"<tr><td style=\"text-align: right;\">1</td><td>chcialbym</td><td>prescription/collect</td><td>O</td></tr>\n",
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"<tr><td style=\"text-align: right;\">2</td><td>odebrac </td><td>prescription/collect</td><td>O</td></tr>\n",
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"<tr><td style=\"text-align: right;\">3</td><td>receptę </td><td>prescription/collect</td><td>O</td></tr>\n",
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"</tbody>\n",
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"</table>"
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],
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"text/plain": [
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"'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>chcialbym</td><td>prescription/collect</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">2</td><td>odebrac </td><td>prescription/collect</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">3</td><td>receptę </td><td>prescription/collect</td><td>O</td></tr>\\n</tbody>\\n</table>'"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tabulate(trainset[10], tablefmt='html')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table>\n",
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"<tbody>\n",
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"<tr><td style=\"text-align: right;\"> 1</td><td>dzień </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 2</td><td>dobry, </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 3</td><td>chciałbym </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 4</td><td>umówić </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 5</td><td>się </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 6</td><td>na </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 7</td><td>wizytę </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 8</td><td>do </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\"> 9</td><td>lekarza </td><td>appoinment/create_appointment</td><td>B-appoinment/doctor</td></tr>\n",
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"<tr><td style=\"text-align: right;\">10</td><td>rodzinnego. </td><td>appoinment/create_appointment</td><td>I-appoinment/doctor</td></tr>\n",
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"<tr><td style=\"text-align: right;\">11</td><td>najlepiej </td><td>appoinment/create_appointment</td><td>O </td></tr>\n",
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"<tr><td style=\"text-align: right;\">12</td><td>dzisiaj </td><td>appoinment/create_appointment</td><td>B-datetime </td></tr>\n",
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"<tr><td style=\"text-align: right;\">13</td><td>w </td><td>appoinment/create_appointment</td><td>I-datetime </td></tr>\n",
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"<tr><td style=\"text-align: right;\">14</td><td>godzinach </td><td>appoinment/create_appointment</td><td>I-datetime </td></tr>\n",
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"<tr><td style=\"text-align: right;\">15</td><td>popołudniowych.</td><td>appoinment/create_appointment</td><td>I-datetime </td></tr>\n",
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"</tbody>\n",
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"</table>"
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],
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"text/plain": [
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"'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\"> 1</td><td>dzień </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 2</td><td>dobry, </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 3</td><td>chciałbym </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 4</td><td>umówić </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 5</td><td>się </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 6</td><td>na </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 7</td><td>wizytę </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 8</td><td>do </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\"> 9</td><td>lekarza </td><td>appoinment/create_appointment</td><td>B-appoinment/doctor</td></tr>\\n<tr><td style=\"text-align: right;\">10</td><td>rodzinnego. </td><td>appoinment/create_appointment</td><td>I-appoinment/doctor</td></tr>\\n<tr><td style=\"text-align: right;\">11</td><td>najlepiej </td><td>appoinment/create_appointment</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\">12</td><td>dzisiaj </td><td>appoinment/create_appointment</td><td>B-datetime </td></tr>\\n<tr><td style=\"text-align: right;\">13</td><td>w </td><td>appoinment/create_appointment</td><td>I-datetime </td></tr>\\n<tr><td style=\"text-align: right;\">14</td><td>godzinach </td><td>appoinment/create_appointment</td><td>I-datetime </td></tr>\\n<tr><td style=\"text-align: right;\">15</td><td>popołudniowych.</td><td>appoinment/create_appointment</td><td>I-datetime </td></tr>\\n</tbody>\\n</table>'"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tabulate(trainset[1], tablefmt='html')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"lines_to_next_cell": 0
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},
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"source": [
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"Na potrzeby prezentacji procesu uczenia w jupyterowym notatniku zawęzimy zbiór danych do początkowych przykładów."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"trainset = trainset[:100]\n",
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"testset = testset[:100]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ąę\n"
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]
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}
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],
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"source": [
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"print('ąę')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Budując model skorzystamy z architektury opartej o rekurencyjne sieci neuronowe\n",
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"zaimplementowanej w bibliotece [flair](https://github.com/flairNLP/flair) (Akbik i in. 2018)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: flair in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (0.8.0.post1)\n",
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"Requirement already satisfied: tqdm>=4.26.0 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (4.50.2)\n",
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"Requirement already satisfied: matplotlib>=2.2.3 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (3.3.2)\n",
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"Requirement already satisfied: hyperopt>=0.1.1 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (0.2.5)\n",
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"Requirement already satisfied: ftfy in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (6.0.1)\n",
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"Requirement already satisfied: konoha<5.0.0,>=4.0.0 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (4.6.4)\n",
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"Requirement already satisfied: bpemb>=0.3.2 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (0.3.3)\n",
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"Requirement already satisfied: janome in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (0.4.1)\n",
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"Requirement already satisfied: scikit-learn>=0.21.3 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (0.23.2)\n",
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"Requirement already satisfied: transformers>=4.0.0 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (4.5.1)\n",
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"Requirement already satisfied: gdown==3.12.2 in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (3.12.2)\n",
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"Requirement already satisfied: tabulate in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (0.8.9)\n",
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"Requirement already satisfied: langdetect in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from flair) (1.0.9)\n",
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"Collecting requests<3.0.0,>=2.25.1\n",
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" Using cached requests-2.25.1-py2.py3-none-any.whl (61 kB)\n",
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"Installing collected packages: requests\n",
|
||||
" Attempting uninstall: requests\n",
|
||||
" Found existing installation: requests 2.24.0\n",
|
||||
" Uninstalling requests-2.24.0:\n",
|
||||
" Successfully uninstalled requests-2.24.0\n",
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"Successfully installed requests-2.25.1\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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||||
"text": [
|
||||
"ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n",
|
||||
"\n",
|
||||
"We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n",
|
||||
"\n",
|
||||
"conda 4.10.1 requires ruamel_yaml_conda>=0.11.14, which is not installed.\n"
|
||||
]
|
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},
|
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|
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|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip3 install flair\n",
|
||||
"from flair.data import Corpus, Sentence, Token\n",
|
||||
"from flair.datasets import SentenceDataset\n",
|
||||
"from flair.embeddings import StackedEmbeddings\n",
|
||||
"from flair.embeddings import WordEmbeddings\n",
|
||||
"from flair.embeddings import CharacterEmbeddings\n",
|
||||
"from flair.embeddings import FlairEmbeddings\n",
|
||||
"from flair.models import SequenceTagger\n",
|
||||
"from flair.trainers import ModelTrainer\n",
|
||||
"\n",
|
||||
"!pip3 install torch\n",
|
||||
"# determinizacja obliczeń\n",
|
||||
"import random\n",
|
||||
"import torch\n",
|
||||
"random.seed(42)\n",
|
||||
"torch.manual_seed(42)\n",
|
||||
"\n",
|
||||
"if torch.cuda.is_available():\n",
|
||||
" torch.cuda.manual_seed(0)\n",
|
||||
" torch.cuda.manual_seed_all(0)\n",
|
||||
" torch.backends.cudnn.enabled = False\n",
|
||||
" torch.backends.cudnn.benchmark = False\n",
|
||||
" torch.backends.cudnn.deterministic = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Dane skonwertujemy do formatu wykorzystywanego przez `flair`, korzystając z następującej funkcji."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Corpus: 37 train + 4 dev + 41 test sentences\n",
|
||||
"Dictionary with 13 tags: <unk>, O, B-appoinment/doctor, I-appoinment/doctor, B-datetime, I-datetime, B-login/id, B-login/password, B-appointment/type, I-appointment/type, B-prescription/type, <START>, <STOP>\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def conllu2flair(sentences, label=None):\n",
|
||||
" fsentences = []\n",
|
||||
"\n",
|
||||
" for sentence in sentences:\n",
|
||||
" fsentence = Sentence()\n",
|
||||
"\n",
|
||||
" for token in sentence:\n",
|
||||
" ftoken = Token(token['form'])\n",
|
||||
"\n",
|
||||
" if label:\n",
|
||||
" ftoken.add_tag(label, token[label])\n",
|
||||
"\n",
|
||||
" fsentence.add_token(ftoken)\n",
|
||||
"\n",
|
||||
" fsentences.append(fsentence)\n",
|
||||
"\n",
|
||||
" return SentenceDataset(fsentences)\n",
|
||||
"\n",
|
||||
"corpus = Corpus(train=conllu2flair(trainset, 'slot'), test=conllu2flair(testset, 'slot'))\n",
|
||||
"print(corpus)\n",
|
||||
"tag_dictionary = corpus.make_tag_dictionary(tag_type='slot')\n",
|
||||
"print(tag_dictionary)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Nasz model będzie wykorzystywał wektorowe reprezentacje słów (zob. [Word Embeddings](https://github.com/flairNLP/flair/blob/master/resources/docs/TUTORIAL_3_WORD_EMBEDDING.md))."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2021-05-12 17:01:27,807 https://flair.informatik.hu-berlin.de/resources/embeddings/token/pl-wiki-fasttext-300d-1M.vectors.npy not found in cache, downloading to C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpq9mlzfps\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
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||||
"text": [
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|
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|
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|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2021-05-12 17:02:20,552 copying C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpq9mlzfps to cache at C:\\Users\\domstr2\\.flair\\embeddings\\pl-wiki-fasttext-300d-1M.vectors.npy\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
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|
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|
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|
||||
"\n"
|
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|
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"2021-05-12 17:02:32,864 removing temp file C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpq9mlzfps\n",
|
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"2021-05-12 17:02:33,344 https://flair.informatik.hu-berlin.de/resources/embeddings/token/pl-wiki-fasttext-300d-1M not found in cache, downloading to C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpp2reld0s\n"
|
||||
]
|
||||
},
|
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{
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|
||||
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|
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"2021-05-12 17:02:35,412 copying C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpp2reld0s to cache at C:\\Users\\domstr2\\.flair\\embeddings\\pl-wiki-fasttext-300d-1M\n"
|
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]
|
||||
},
|
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{
|
||||
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|
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"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
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},
|
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{
|
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|
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"text": [
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"2021-05-12 17:02:36,260 removing temp file C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpp2reld0s\n",
|
||||
"2021-05-12 17:02:39,489 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/lm-polish-forward-v0.2.pt not found in cache, downloading to C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpin9zi6n_\n"
|
||||
]
|
||||
},
|
||||
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|
||||
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|
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"output_type": "stream",
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|
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|
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|
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|
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|
||||
"2021-05-12 17:02:42,804 copying C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpin9zi6n_ to cache at C:\\Users\\domstr2\\.flair\\embeddings\\lm-polish-forward-v0.2.pt\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2021-05-12 17:02:42,861 removing temp file C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmpin9zi6n_\n",
|
||||
"2021-05-12 17:02:43,329 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/lm-polish-backward-v0.2.pt not found in cache, downloading to C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmp30skh32n\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
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|
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|
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|
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|
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|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2021-05-12 17:02:46,769 copying C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmp30skh32n to cache at C:\\Users\\domstr2\\.flair\\embeddings\\lm-polish-backward-v0.2.pt\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2021-05-12 17:02:46,828 removing temp file C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmp30skh32n\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"embedding_types = [\n",
|
||||
" WordEmbeddings('pl'),\n",
|
||||
" FlairEmbeddings('pl-forward'),\n",
|
||||
" FlairEmbeddings('pl-backward'),\n",
|
||||
" CharacterEmbeddings(),\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"embeddings = StackedEmbeddings(embeddings=embedding_types)\n",
|
||||
"tagger = SequenceTagger(hidden_size=256, embeddings=embeddings,\n",
|
||||
" tag_dictionary=tag_dictionary,\n",
|
||||
" tag_type='slot', use_crf=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Zobaczmy jak wygląda architektura sieci neuronowej, która będzie odpowiedzialna za przewidywanie\n",
|
||||
"slotów w wypowiedziach."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SequenceTagger(\n",
|
||||
" (embeddings): StackedEmbeddings(\n",
|
||||
" (list_embedding_0): WordEmbeddings('pl')\n",
|
||||
" (list_embedding_1): FlairEmbeddings(\n",
|
||||
" (lm): LanguageModel(\n",
|
||||
" (drop): Dropout(p=0.25, inplace=False)\n",
|
||||
" (encoder): Embedding(1602, 100)\n",
|
||||
" (rnn): LSTM(100, 2048)\n",
|
||||
" (decoder): Linear(in_features=2048, out_features=1602, bias=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (list_embedding_2): FlairEmbeddings(\n",
|
||||
" (lm): LanguageModel(\n",
|
||||
" (drop): Dropout(p=0.25, inplace=False)\n",
|
||||
" (encoder): Embedding(1602, 100)\n",
|
||||
" (rnn): LSTM(100, 2048)\n",
|
||||
" (decoder): Linear(in_features=2048, out_features=1602, bias=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (list_embedding_3): CharacterEmbeddings(\n",
|
||||
" (char_embedding): Embedding(275, 25)\n",
|
||||
" (char_rnn): LSTM(25, 25, bidirectional=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (word_dropout): WordDropout(p=0.05)\n",
|
||||
" (locked_dropout): LockedDropout(p=0.5)\n",
|
||||
" (embedding2nn): Linear(in_features=4446, out_features=4446, bias=True)\n",
|
||||
" (rnn): LSTM(4446, 256, batch_first=True, bidirectional=True)\n",
|
||||
" (linear): Linear(in_features=512, out_features=13, bias=True)\n",
|
||||
" (beta): 1.0\n",
|
||||
" (weights): None\n",
|
||||
" (weight_tensor) None\n",
|
||||
")\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(tagger)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Wykonamy dziesięć iteracji (epok) uczenia a wynikowy model zapiszemy w katalogu `slot-model`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2021-05-12 17:07:41,538 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:41,539 Model: \"SequenceTagger(\n",
|
||||
" (embeddings): StackedEmbeddings(\n",
|
||||
" (list_embedding_0): WordEmbeddings('pl')\n",
|
||||
" (list_embedding_1): FlairEmbeddings(\n",
|
||||
" (lm): LanguageModel(\n",
|
||||
" (drop): Dropout(p=0.25, inplace=False)\n",
|
||||
" (encoder): Embedding(1602, 100)\n",
|
||||
" (rnn): LSTM(100, 2048)\n",
|
||||
" (decoder): Linear(in_features=2048, out_features=1602, bias=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (list_embedding_2): FlairEmbeddings(\n",
|
||||
" (lm): LanguageModel(\n",
|
||||
" (drop): Dropout(p=0.25, inplace=False)\n",
|
||||
" (encoder): Embedding(1602, 100)\n",
|
||||
" (rnn): LSTM(100, 2048)\n",
|
||||
" (decoder): Linear(in_features=2048, out_features=1602, bias=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (list_embedding_3): CharacterEmbeddings(\n",
|
||||
" (char_embedding): Embedding(275, 25)\n",
|
||||
" (char_rnn): LSTM(25, 25, bidirectional=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (word_dropout): WordDropout(p=0.05)\n",
|
||||
" (locked_dropout): LockedDropout(p=0.5)\n",
|
||||
" (embedding2nn): Linear(in_features=4446, out_features=4446, bias=True)\n",
|
||||
" (rnn): LSTM(4446, 256, batch_first=True, bidirectional=True)\n",
|
||||
" (linear): Linear(in_features=512, out_features=13, bias=True)\n",
|
||||
" (beta): 1.0\n",
|
||||
" (weights): None\n",
|
||||
" (weight_tensor) None\n",
|
||||
")\"\n",
|
||||
"2021-05-12 17:07:41,540 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:41,541 Corpus: \"Corpus: 37 train + 4 dev + 41 test sentences\"\n",
|
||||
"2021-05-12 17:07:41,541 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:41,542 Parameters:\n",
|
||||
"2021-05-12 17:07:41,542 - learning_rate: \"0.1\"\n",
|
||||
"2021-05-12 17:07:41,543 - mini_batch_size: \"32\"\n",
|
||||
"2021-05-12 17:07:41,543 - patience: \"3\"\n",
|
||||
"2021-05-12 17:07:41,544 - anneal_factor: \"0.5\"\n",
|
||||
"2021-05-12 17:07:41,544 - max_epochs: \"10\"\n",
|
||||
"2021-05-12 17:07:41,545 - shuffle: \"True\"\n",
|
||||
"2021-05-12 17:07:41,546 - train_with_dev: \"False\"\n",
|
||||
"2021-05-12 17:07:41,546 - batch_growth_annealing: \"False\"\n",
|
||||
"2021-05-12 17:07:41,547 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:41,547 Model training base path: \"slot-model\"\n",
|
||||
"2021-05-12 17:07:41,548 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:41,549 Device: cpu\n",
|
||||
"2021-05-12 17:07:41,549 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:41,550 Embeddings storage mode: cpu\n",
|
||||
"2021-05-12 17:07:41,552 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:46,139 epoch 1 - iter 1/2 - loss 9.51263237 - samples/sec: 6.98 - lr: 0.100000\n",
|
||||
"2021-05-12 17:07:47,186 epoch 1 - iter 2/2 - loss 7.22621894 - samples/sec: 30.58 - lr: 0.100000\n",
|
||||
"2021-05-12 17:07:47,188 ----------------------------------------------------------------------------------------------------\n",
|
||||
"2021-05-12 17:07:47,189 EPOCH 1 done: loss 7.2262 - lr 0.1000000\n",
|
||||
"2021-05-12 17:07:48,466 DEV : loss 5.046579837799072 - score 0.0\n",
|
||||
"2021-05-12 17:07:48,468 BAD EPOCHS (no improvement): 0\n",
|
||||
"saving best model\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "RuntimeError",
|
||||
"evalue": "[enforce fail at ..\\caffe2\\serialize\\inline_container.cc:274] . unexpected pos 64 vs 0",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[0;32m 371\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 372\u001b[1;33m \u001b[0m_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 373\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m_save\u001b[1;34m(obj, zip_file, pickle_module, pickle_protocol)\u001b[0m\n\u001b[0;32m 477\u001b[0m \u001b[0mdata_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_buf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 478\u001b[1;33m \u001b[0mzip_file\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_record\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 479\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mOSError\u001b[0m: [Errno 28] No space left on device",
|
||||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-36-6f4b58920804>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mModelTrainer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtagger\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcorpus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m trainer.train('slot-model',\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mmini_batch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\flair\\trainers\\trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, base_path, learning_rate, mini_batch_size, mini_batch_chunk_size, max_epochs, scheduler, cycle_momentum, anneal_factor, patience, initial_extra_patience, min_learning_rate, train_with_dev, train_with_test, monitor_train, monitor_test, embeddings_storage_mode, checkpoint, save_final_model, anneal_with_restarts, anneal_with_prestarts, batch_growth_annealing, shuffle, param_selection_mode, write_weights, num_workers, sampler, use_amp, amp_opt_level, eval_on_train_fraction, eval_on_train_shuffle, save_model_at_each_epoch, **kwargs)\u001b[0m\n\u001b[0;32m 592\u001b[0m ):\n\u001b[0;32m 593\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"saving best model\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 594\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_path\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;34m\"best-model.pt\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 595\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 596\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0manneal_with_prestarts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\flair\\nn.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, model_file)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0mmodel_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_state_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[0;32m 371\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 372\u001b[0m \u001b[0m_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 373\u001b[1;33m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 374\u001b[0m \u001b[0m_legacy_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 375\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 259\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfile_like\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_end_of_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 260\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 261\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m: [enforce fail at ..\\caffe2\\serialize\\inline_container.cc:274] . unexpected pos 64 vs 0"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"trainer = ModelTrainer(tagger, corpus)\n",
|
||||
"trainer.train('slot-model',\n",
|
||||
" learning_rate=0.1,\n",
|
||||
" mini_batch_size=32,\n",
|
||||
" max_epochs=10,\n",
|
||||
" train_with_dev=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Jakość wyuczonego modelu możemy ocenić, korzystając z zaraportowanych powyżej metryk, tj.:\n",
|
||||
"\n",
|
||||
" - *tp (true positives)*\n",
|
||||
"\n",
|
||||
" > liczba słów oznaczonych w zbiorze testowym etykietą $e$, które model oznaczył tą etykietą\n",
|
||||
"\n",
|
||||
" - *fp (false positives)*\n",
|
||||
"\n",
|
||||
" > liczba słów nieoznaczonych w zbiorze testowym etykietą $e$, które model oznaczył tą etykietą\n",
|
||||
"\n",
|
||||
" - *fn (false negatives)*\n",
|
||||
"\n",
|
||||
" > liczba słów oznaczonych w zbiorze testowym etykietą $e$, którym model nie nadał etykiety $e$\n",
|
||||
"\n",
|
||||
" - *precision*\n",
|
||||
"\n",
|
||||
" > $$\\frac{tp}{tp + fp}$$\n",
|
||||
"\n",
|
||||
" - *recall*\n",
|
||||
"\n",
|
||||
" > $$\\frac{tp}{tp + fn}$$\n",
|
||||
"\n",
|
||||
" - $F_1$\n",
|
||||
"\n",
|
||||
" > $$\\frac{2 \\cdot precision \\cdot recall}{precision + recall}$$\n",
|
||||
"\n",
|
||||
" - *micro* $F_1$\n",
|
||||
"\n",
|
||||
" > $F_1$ w którym $tp$, $fp$ i $fn$ są liczone łącznie dla wszystkich etykiet, tj. $tp = \\sum_{e}{{tp}_e}$, $fn = \\sum_{e}{{fn}_e}$, $fp = \\sum_{e}{{fp}_e}$\n",
|
||||
"\n",
|
||||
" - *macro* $F_1$\n",
|
||||
"\n",
|
||||
" > średnia arytmetyczna z $F_1$ obliczonych dla poszczególnych etykiet z osobna.\n",
|
||||
"\n",
|
||||
"Wyuczony model możemy wczytać z pliku korzystając z metody `load`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2021-05-12 16:58:59,033 loading file slot-model/final-model.pt\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model = SequenceTagger.load('slot-model/final-model.pt')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Wczytany model możemy wykorzystać do przewidywania slotów w wypowiedziach użytkownika, korzystając\n",
|
||||
"z przedstawionej poniżej funkcji `predict`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def predict(model, sentence):\n",
|
||||
" csentence = [{'form': word} for word in sentence]\n",
|
||||
" fsentence = conllu2flair([csentence])[0]\n",
|
||||
" model.predict(fsentence)\n",
|
||||
" return [(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Jak pokazuje przykład poniżej model wyuczony tylko na 100 przykładach popełnia w dosyć prostej\n",
|
||||
"wypowiedzi błąd etykietując słowo `alarm` tagiem `B-weather/noun`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<table>\n",
|
||||
"<tbody>\n",
|
||||
"<tr><td>doktor </td><td>O</td></tr>\n",
|
||||
"<tr><td>lekarz </td><td>O</td></tr>\n",
|
||||
"<tr><td>wizyta </td><td>O</td></tr>\n",
|
||||
"<tr><td>kolano </td><td>O</td></tr>\n",
|
||||
"<tr><td>na </td><td>O</td></tr>\n",
|
||||
"<tr><td>godzine</td><td>O</td></tr>\n",
|
||||
"<tr><td>jutro </td><td>O</td></tr>\n",
|
||||
"<tr><td>dzisiaj</td><td>O</td></tr>\n",
|
||||
"<tr><td>13:00 </td><td>O</td></tr>\n",
|
||||
"</tbody>\n",
|
||||
"</table>"
|
||||
],
|
||||
"text/plain": [
|
||||
"'<table>\\n<tbody>\\n<tr><td>doktor </td><td>O</td></tr>\\n<tr><td>lekarz </td><td>O</td></tr>\\n<tr><td>wizyta </td><td>O</td></tr>\\n<tr><td>kolano </td><td>O</td></tr>\\n<tr><td>na </td><td>O</td></tr>\\n<tr><td>godzine</td><td>O</td></tr>\\n<tr><td>jutro </td><td>O</td></tr>\\n<tr><td>dzisiaj</td><td>O</td></tr>\\n<tr><td>13:00 </td><td>O</td></tr>\\n</tbody>\\n</table>'"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tabulate(predict(model, 'doktor lekarz wizyta kolano na godzine jutro dzisiaj 13:00'.split()), tablefmt='html')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Literatura\n",
|
||||
"----------\n",
|
||||
" 1. Sebastian Schuster, Sonal Gupta, Rushin Shah, Mike Lewis, Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog. NAACL-HLT (1) 2019, pp. 3795-3805\n",
|
||||
" 2. John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML '01). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 282–289, https://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers\n",
|
||||
" 3. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Comput. 9, 8 (November 15, 1997), 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735\n",
|
||||
" 4. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, Attention is All you Need, NIPS 2017, pp. 5998-6008, https://arxiv.org/abs/1706.03762\n",
|
||||
" 5. Alan Akbik, Duncan Blythe, Roland Vollgraf, Contextual String Embeddings for Sequence Labeling, Proceedings of the 27th International Conference on Computational Linguistics, pp. 1638–1649, https://www.aclweb.org/anthology/C18-1139.pdf\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"jupytext": {
|
||||
"cell_metadata_filter": "-all",
|
||||
"main_language": "python",
|
||||
"notebook_metadata_filter": "-all"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
7
IOB_Zasady.txt
Normal file
7
IOB_Zasady.txt
Normal file
@ -0,0 +1,7 @@
|
||||
B-greeting
|
||||
B-doctor
|
||||
I-doctor
|
||||
B-datetime
|
||||
I-datetime
|
||||
B-id
|
||||
B-password
|
||||
321
Janet_test.conllu
Normal file
321
Janet_test.conllu
Normal file
@ -0,0 +1,321 @@
|
||||
# text: Hej
|
||||
# intent: greeting
|
||||
# slots:
|
||||
1 hej greeting NoLabel
|
||||
|
||||
# text: Dzień dobry, chciałbym umówić się na wizytę do lekarza rodzinnego. Najlepiej dzisiaj w godzinach popołudniowych.
|
||||
# intent: appoinment/create_appointment
|
||||
# slots:
|
||||
1 dzień appoinment/create_appointment NoLabel
|
||||
2 dobry, appoinment/create_appointment NoLabel
|
||||
3 chciałbym appoinment/create_appointment NoLabel
|
||||
4 umówić appoinment/create_appointment NoLabel
|
||||
5 się appoinment/create_appointment NoLabel
|
||||
6 na appoinment/create_appointment NoLabel
|
||||
7 wizytę appoinment/create_appointment NoLabel
|
||||
8 do appoinment/create_appointment NoLabel
|
||||
9 lekarza appoinment/create_appointment B-appoinment/doctor
|
||||
10 rodzinnego. appoinment/create_appointment I-appoinment/doctor
|
||||
11 najlepiej appoinment/create_appointment NoLabel
|
||||
12 dzisiaj appoinment/create_appointment B-datetime
|
||||
13 w appoinment/create_appointment I-datetime
|
||||
14 godzinach appoinment/create_appointment I-datetime
|
||||
15 popołudniowych. appoinment/create_appointment I-datetime
|
||||
|
||||
# text: 12345678AFD
|
||||
# intent: login/enter_id
|
||||
# slots:
|
||||
1 12345678afd login/enter_id B-login/id
|
||||
|
||||
# text: 2febjs45
|
||||
# intent: login/enter_password
|
||||
# slots:
|
||||
1 2febjs45 login/enter_password B-login/password
|
||||
|
||||
# text: A czy mogę zapisać się do Pani doktor Zofii Wątroby?
|
||||
# intent: appoinment/create_appointment
|
||||
# slots:
|
||||
1 a appoinment/create_appointment NoLabel
|
||||
2 czy appoinment/create_appointment NoLabel
|
||||
3 mogę appoinment/create_appointment NoLabel
|
||||
4 zapisać appoinment/create_appointment NoLabel
|
||||
5 się appoinment/create_appointment NoLabel
|
||||
6 do appoinment/create_appointment B-appoinment/doctor
|
||||
7 pani appoinment/create_appointment I-appoinment/doctor
|
||||
8 doktor appoinment/create_appointment I-appoinment/doctor
|
||||
9 zofii appoinment/create_appointment I-appoinment/doctor
|
||||
10 wątroby? appoinment/create_appointment I-appoinment/doctor
|
||||
|
||||
# text: Ten termin mi odpowiada!
|
||||
# intent: appoinment/confirm
|
||||
# slots:
|
||||
1 ten appoinment/confirm NoLabel
|
||||
2 termin appoinment/confirm NoLabel
|
||||
3 mi appoinment/confirm NoLabel
|
||||
4 odpowiada! appoinment/confirm NoLabel
|
||||
|
||||
# text: Tak, bardzo dziękuję.
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 tak, affirm NoLabel
|
||||
2 bardzo affirm NoLabel
|
||||
3 dziękuję. affirm NoLabel
|
||||
|
||||
# text: Chciałbym też od razu zrobić badania morfologii krwi. Kiedy mogę przyjść na pobranie krwi?
|
||||
# intent: appoinment/create_appointment request_information/opening_hours
|
||||
# slots:
|
||||
1 chciałbym appoinment/create_appointment NoLabel
|
||||
2 też appoinment/create_appointment NoLabel
|
||||
3 od appoinment/create_appointment NoLabel
|
||||
4 razu appoinment/create_appointment NoLabel
|
||||
5 zrobić appoinment/create_appointment NoLabel
|
||||
6 badania appoinment/create_appointment B-appointment/type
|
||||
7 morfologii appoinment/create_appointment I-appointment/type
|
||||
8 krwi. appoinment/create_appointment I-appointment/type
|
||||
9 kiedy request_information/opening_hours NoLabel
|
||||
10 mogę request_information/opening_hours NoLabel
|
||||
11 przyjść request_information/opening_hours NoLabel
|
||||
12 na request_information/opening_hours NoLabel
|
||||
13 pobranie request_information/opening_hours B-appointment/type
|
||||
14 krwi? request_information/opening_hours I-appointment/type
|
||||
|
||||
# text: Dziękuję bardzo za informację. W takim przypadku to wszystko.
|
||||
# intent: end_conversation
|
||||
# slots:
|
||||
1 dziękuję end_conversation NoLabel
|
||||
2 bardzo end_conversation NoLabel
|
||||
3 za end_conversation NoLabel
|
||||
4 informację. end_conversation NoLabel
|
||||
5 w end_conversation NoLabel
|
||||
6 takim end_conversation NoLabel
|
||||
7 przypadku end_conversation NoLabel
|
||||
8 to end_conversation NoLabel
|
||||
9 wszystko. end_conversation NoLabel
|
||||
|
||||
# text: Dzień dobry
|
||||
# intent: greeting
|
||||
# slots:
|
||||
1 dzień greeting NoLabel
|
||||
2 dobry greeting NoLabel
|
||||
|
||||
# text: Chcialbym odebrac receptę
|
||||
# intent: prescription/collect
|
||||
# slots:
|
||||
1 chcialbym prescription/collect NoLabel
|
||||
2 odebrac prescription/collect NoLabel
|
||||
3 receptę prescription/collect NoLabel
|
||||
|
||||
# text: e-receptę
|
||||
# intent: prescription/type
|
||||
# slots:
|
||||
1 e-receptę prescription/type B-prescription/type
|
||||
|
||||
# text: Tak
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 tak affirm NoLabel
|
||||
|
||||
# text: 123123
|
||||
# intent: login/enter_id
|
||||
# slots:
|
||||
1 123123 login/enter_id B-login/id
|
||||
|
||||
# text: 321321
|
||||
# intent: login/enter_password
|
||||
# slots:
|
||||
1 321321 login/enter_password B-login/password
|
||||
|
||||
# text: Chciałbym również umówić spotkanie z lekarzem internistą
|
||||
# intent: appoinment/create_appointment
|
||||
# slots:
|
||||
1 chciałbym appoinment/create_appointment NoLabel
|
||||
2 również appoinment/create_appointment NoLabel
|
||||
3 umówić appoinment/create_appointment NoLabel
|
||||
4 spotkanie appoinment/create_appointment NoLabel
|
||||
5 z appoinment/create_appointment NoLabel
|
||||
6 lekarzem appoinment/create_appointment B-appoinment/doctor
|
||||
7 internistą appoinment/create_appointment I-appoinment/doctor
|
||||
|
||||
# text: Tak
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 tak affirm NoLabel
|
||||
|
||||
# text: 12.04.2021
|
||||
# intent: appoinment/set_date
|
||||
# slots:
|
||||
1 12.04.2021 appoinment/set_date B-datetime
|
||||
|
||||
# text: 13:00
|
||||
# intent: appoinment/set_time
|
||||
# slots:
|
||||
1 13:00 appoinment/set_time B-datetime
|
||||
|
||||
# text: Tak
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 tak affirm NoLabel
|
||||
|
||||
# text: Gdzie obędzie się wizyta?
|
||||
# intent: appoinment/where
|
||||
# slots:
|
||||
1 gdzie appoinment/where NoLabel
|
||||
2 obędzie appoinment/where NoLabel
|
||||
3 się appoinment/where NoLabel
|
||||
4 wizyta? appoinment/where NoLabel
|
||||
|
||||
# text: Dziękuję za pomoc
|
||||
# intent: end_conversation
|
||||
# slots:
|
||||
1 dziękuję end_conversation NoLabel
|
||||
2 za end_conversation NoLabel
|
||||
3 pomoc end_conversation NoLabel
|
||||
|
||||
# text: Cześć
|
||||
# intent: greeting
|
||||
# slots:
|
||||
1 cześć greeting NoLabel
|
||||
|
||||
# text: Chciałbym się dowiedzieć, czy mam umówione jakieś wizyty.
|
||||
# intent: appoinment/check_appointments
|
||||
# slots:
|
||||
1 chciałbym appoinment/check_appointments NoLabel
|
||||
2 się appoinment/check_appointments NoLabel
|
||||
3 dowiedzieć, appoinment/check_appointments NoLabel
|
||||
4 czy appoinment/check_appointments NoLabel
|
||||
5 mam appoinment/check_appointments NoLabel
|
||||
6 umówione appoinment/check_appointments NoLabel
|
||||
7 jakieś appoinment/check_appointments NoLabel
|
||||
8 wizyty. appoinment/check_appointments NoLabel
|
||||
|
||||
# text: 34534535
|
||||
# intent: login/enter_id
|
||||
# slots:
|
||||
1 34534535 login/enter_id B-login/id
|
||||
|
||||
# text: janusz123
|
||||
# intent: login/enter_password
|
||||
# slots:
|
||||
1 janusz123 login/enter_password B-login/password
|
||||
|
||||
# text: Chciałbym odwołać wizytę u internisty
|
||||
# intent: appoinment/cancel
|
||||
# slots:
|
||||
1 chciałbym appoinment/cancel NoLabel
|
||||
2 odwołać appoinment/cancel NoLabel
|
||||
3 wizytę appoinment/cancel NoLabel
|
||||
4 u appoinment/cancel NoLabel
|
||||
5 internisty appoinment/cancel B-appoinment/doctor
|
||||
|
||||
# text: Tak
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 tak affirm NoLabel
|
||||
|
||||
# text: Jacy lekarze specjaliści przyjmują w państwa przychodni?
|
||||
# intent: request_information/doctors
|
||||
# slots:
|
||||
1 jacy request_information/doctors NoLabel
|
||||
2 lekarze request_information/doctors NoLabel
|
||||
3 specjaliści request_information/doctors NoLabel
|
||||
4 przyjmują request_information/doctors NoLabel
|
||||
5 w request_information/doctors NoLabel
|
||||
6 państwa request_information/doctors NoLabel
|
||||
7 przychodni? request_information/doctors NoLabel
|
||||
|
||||
# text: Chciałbym umówić wizytę do doktora Kolano.
|
||||
# intent: appoinment/create_appointment
|
||||
# slots:
|
||||
1 chciałbym appoinment/create_appointment NoLabel
|
||||
2 umówić appoinment/create_appointment NoLabel
|
||||
3 wizytę appoinment/create_appointment NoLabel
|
||||
4 do appoinment/create_appointment NoLabel
|
||||
5 doktora appoinment/create_appointment B-appoinment/doctor
|
||||
6 kolano. appoinment/create_appointment I-appoinment/doctor
|
||||
|
||||
# text: Ten termin mi odpowiada.
|
||||
# intent: appoinment/confirm
|
||||
# slots:
|
||||
1 ten appoinment/confirm NoLabel
|
||||
2 termin appoinment/confirm NoLabel
|
||||
3 mi appoinment/confirm NoLabel
|
||||
4 odpowiada. appoinment/confirm NoLabel
|
||||
|
||||
# text: tak
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 tak affirm NoLabel
|
||||
|
||||
# text: Nie, to wszystko. Do widzenia.
|
||||
# intent: end_conversation
|
||||
# slots:
|
||||
1 nie, end_conversation NoLabel
|
||||
2 to end_conversation NoLabel
|
||||
3 wszystko. end_conversation NoLabel
|
||||
4 do end_conversation NoLabel
|
||||
5 widzenia. end_conversation NoLabel
|
||||
|
||||
# text: Cześć :)
|
||||
# intent: greeting
|
||||
# slots:
|
||||
1 cześć greeting NoLabel
|
||||
2 :) greeting NoLabel
|
||||
|
||||
# text: Jakie usługi medyczne są dostępne?
|
||||
# intent: request_information/medical_services
|
||||
# slots:
|
||||
1 jakie request_information/medical_services NoLabel
|
||||
2 usługi request_information/medical_services NoLabel
|
||||
3 medyczne request_information/medical_services NoLabel
|
||||
4 są request_information/medical_services NoLabel
|
||||
5 dostępne? request_information/medical_services NoLabel
|
||||
|
||||
# text: Chciałbym zapisać się do okulisty. Ile kosztuje wizyta?
|
||||
# intent: appoinment/create_appointment request_information/cost
|
||||
# slots:
|
||||
1 chciałbym appoinment/create_appointment NoLabel
|
||||
2 zapisać appoinment/create_appointment NoLabel
|
||||
3 się appoinment/create_appointment NoLabel
|
||||
4 do appoinment/create_appointment NoLabel
|
||||
5 okulisty. appoinment/create_appointment B-appoinment/doctor
|
||||
6 ile request_information/cost NoLabel
|
||||
7 kosztuje request_information/cost NoLabel
|
||||
8 wizyta? request_information/cost NoLabel
|
||||
|
||||
# text: Nie?
|
||||
# intent: deny
|
||||
# slots:
|
||||
1 nie? deny NoLabel
|
||||
|
||||
# text: Nie, ten jest idealny.
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 nie, affirm NoLabel
|
||||
2 ten affirm NoLabel
|
||||
3 jest affirm NoLabel
|
||||
4 idealny. affirm NoLabel
|
||||
|
||||
# text: Tak.
|
||||
# intent: affirm
|
||||
# slots:
|
||||
1 tak. affirm NoLabel
|
||||
|
||||
# text: Dziękuję za informację : ).
|
||||
# intent: end_conversation
|
||||
# slots:
|
||||
1 dziękuję end_conversation NoLabel
|
||||
2 za end_conversation NoLabel
|
||||
3 informację end_conversation NoLabel
|
||||
4 : end_conversation NoLabel
|
||||
5 ). end_conversation NoLabel
|
||||
|
||||
# text: Nie, dziękuję - to wszystko : ).
|
||||
# intent: end_conversation
|
||||
# slots:
|
||||
1 nie, end_conversation NoLabel
|
||||
2 dziękuję end_conversation NoLabel
|
||||
3 - end_conversation NoLabel
|
||||
4 to end_conversation NoLabel
|
||||
5 wszystko end_conversation NoLabel
|
||||
6 : end_conversation NoLabel
|
||||
7 ). end_conversation NoLabel
|
||||
Loading…
Reference in New Issue
Block a user