{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part-of-Speech Recognition"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"This tutorial is available as an IPython notebook at [Malaya/example/part-of-speech](https://github.com/huseinzol05/Malaya/tree/master/example/part-of-speech).\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"This module only trained on standard language structure, so it is not save to use it for local language structure.\n",
" \n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.94 s, sys: 1.17 s, total: 7.11 s\n",
"Wall time: 8.41 s\n"
]
}
],
"source": [
"%%time\n",
"import malaya"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Models accuracy\n",
"\n",
"We use `sklearn.metrics.classification_report` for accuracy reporting, check at https://malaya.readthedocs.io/en/latest/models-accuracy.html#pos-recognition"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Describe supported POS"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" Tag \n",
" Description \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" ADJ \n",
" Adjective, kata sifat \n",
" \n",
" \n",
" 1 \n",
" ADP \n",
" Adposition \n",
" \n",
" \n",
" 2 \n",
" ADV \n",
" Adverb, kata keterangan \n",
" \n",
" \n",
" 3 \n",
" ADX \n",
" Auxiliary verb, kata kerja tambahan \n",
" \n",
" \n",
" 4 \n",
" CCONJ \n",
" Coordinating conjuction, kata hubung \n",
" \n",
" \n",
" 5 \n",
" DET \n",
" Determiner, kata penentu \n",
" \n",
" \n",
" 6 \n",
" NOUN \n",
" Noun, kata nama \n",
" \n",
" \n",
" 7 \n",
" NUM \n",
" Number, nombor \n",
" \n",
" \n",
" 8 \n",
" PART \n",
" Particle \n",
" \n",
" \n",
" 9 \n",
" PRON \n",
" Pronoun, kata ganti \n",
" \n",
" \n",
" 10 \n",
" PROPN \n",
" Proper noun, kata ganti nama khas \n",
" \n",
" \n",
" 11 \n",
" SCONJ \n",
" Subordinating conjunction \n",
" \n",
" \n",
" 12 \n",
" SYM \n",
" Symbol \n",
" \n",
" \n",
" 13 \n",
" VERB \n",
" Verb, kata kerja \n",
" \n",
" \n",
" 14 \n",
" X \n",
" Other \n",
" \n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" Size (MB) \n",
" Quantized Size (MB) \n",
" macro precision \n",
" macro recall \n",
" macro f1-score \n",
" \n",
" \n",
" \n",
" \n",
" bert \n",
" 426.4 \n",
" 111.00 \n",
" 0.93280 \n",
" 0.93129 \n",
" 0.93181 \n",
" \n",
" \n",
" tiny-bert \n",
" 57.7 \n",
" 15.40 \n",
" 0.92810 \n",
" 0.92649 \n",
" 0.92704 \n",
" \n",
" \n",
" albert \n",
" 48.7 \n",
" 12.80 \n",
" 0.93199 \n",
" 0.91948 \n",
" 0.92547 \n",
" \n",
" \n",
" tiny-albert \n",
" 22.4 \n",
" 5.98 \n",
" 0.90579 \n",
" 0.89501 \n",
" 0.90002 \n",
" \n",
" \n",
" xlnet \n",
" 446.6 \n",
" 118.00 \n",
" 0.93303 \n",
" 0.93222 \n",
" 0.93236 \n",
" \n",
" \n",
" alxlnet \n",
" 46.8 \n",
" 13.30 \n",
" 0.92732 \n",
" 0.93046 \n",
" 0.92819 \n",
" \n",
" \n",
"
\n",
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (7, 7))\n",
"plt.scatter(tsne[:, 0], tsne[:, 1])\n",
"labels = x\n",
"for label, x, y in zip(\n",
" labels, tsne[:, 0], tsne[:, 1]\n",
"):\n",
" label = (\n",
" '%s, %.3f' % (label[0], label[1])\n",
" if isinstance(label, list)\n",
" else label\n",
" )\n",
" plt.annotate(\n",
" label,\n",
" xy = (x, y),\n",
" xytext = (0, 0),\n",
" textcoords = 'offset points',\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pretty good, the model able to know cluster similar part-of-speech."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Voting stack model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('KUALA', 'PROPN'),\n",
" ('LUMPUR:', 'PROPN'),\n",
" ('Sempena', 'ADP'),\n",
" ('sambutan', 'NOUN'),\n",
" ('Aidilfitri', 'PROPN'),\n",
" ('minggu', 'NOUN'),\n",
" ('depan,', 'ADJ'),\n",
" ('Perdana', 'PROPN'),\n",
" ('Menteri', 'PROPN'),\n",
" ('Tun', 'PROPN'),\n",
" ('Dr', 'PROPN'),\n",
" ('Mahathir', 'PROPN'),\n",
" ('Mohamad', 'PROPN'),\n",
" ('dan', 'CCONJ'),\n",
" ('Menteri', 'PROPN'),\n",
" ('Pengangkutan', 'PROPN'),\n",
" ('Anthony', 'PROPN'),\n",
" ('Loke', 'PROPN'),\n",
" ('Siew', 'PROPN'),\n",
" ('Fook', 'PROPN'),\n",
" ('menitipkan', 'VERB'),\n",
" ('pesanan', 'NOUN'),\n",
" ('khas', 'ADJ'),\n",
" ('kepada', 'ADP'),\n",
" ('orang', 'NOUN'),\n",
" ('ramai', 'ADJ'),\n",
" ('yang', 'PRON'),\n",
" ('mahu', 'ADV'),\n",
" ('pulang', 'VERB'),\n",
" ('ke', 'ADP'),\n",
" ('kampung', 'NOUN'),\n",
" ('halaman', 'NOUN'),\n",
" ('masing-masing.', 'ADV'),\n",
" ('Dalam', 'ADP'),\n",
" ('video', 'NOUN'),\n",
" ('pendek', 'ADJ'),\n",
" ('terbitan', 'NOUN'),\n",
" ('Jabatan', 'NOUN'),\n",
" ('Keselamatan', 'PROPN'),\n",
" ('Jalan', 'PROPN'),\n",
" ('Raya', 'PROPN'),\n",
" ('(JKJR)', 'PUNCT'),\n",
" ('itu,', 'DET'),\n",
" ('Dr', 'PROPN'),\n",
" ('Mahathir', 'PROPN'),\n",
" ('menasihati', 'VERB'),\n",
" ('mereka', 'PRON'),\n",
" ('supaya', 'SCONJ'),\n",
" ('berhenti', 'VERB'),\n",
" ('berehat', 'VERB'),\n",
" ('dan', 'CCONJ'),\n",
" ('tidur', 'VERB'),\n",
" ('sebentar', 'ADV'),\n",
" ('sekiranya', 'SCONJ'),\n",
" ('mengantuk', 'ADJ'),\n",
" ('ketika', 'SCONJ'),\n",
" ('memandu.', 'VERB')]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alxlnet = malaya.pos.transformer(model = 'alxlnet')\n",
"malaya.stack.voting_stack([model, alxlnet, alxlnet], string)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}