470 lines
13 KiB
Plaintext
470 lines
13 KiB
Plaintext
{
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"cells": [
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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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"Requirement already satisfied: gensim in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (3.8.3)\n",
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"Requirement already satisfied: smart-open>=1.8.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from gensim) (4.0.1)\n",
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"Requirement already satisfied: scipy>=0.18.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from gensim) (1.5.2)\n",
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"Requirement already satisfied: six>=1.5.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from gensim) (1.15.0)\n",
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"Requirement already satisfied: numpy>=1.11.3 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from gensim) (1.19.2)\n"
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]
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}
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],
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"source": [
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"!pip install gensim "
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.display import Image\n",
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"import gensim.downloader"
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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": 3,
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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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"<style>.container { width:100% !important; }</style>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from IPython.core.display import display, HTML\n",
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"display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
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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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"Mikolov et al. (2013)"
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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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""
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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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"The cat XXX on the"
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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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"Nauka nienadzorowana- nie trzeba zaetykietowanego korpusu"
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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": 4,
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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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"[===============-----------------------------------] 30.2% 38.7/128.1MB downloaded"
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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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"IOPub message rate exceeded.\n",
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"The notebook server will temporarily stop sending output\n",
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"to the client in order to avoid crashing it.\n",
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"To change this limit, set the config variable\n",
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"`--NotebookApp.iopub_msg_rate_limit`.\n",
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"\n",
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"Current values:\n",
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"NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
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"NotebookApp.rate_limit_window=3.0 (secs)\n",
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"\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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"[==================================================] 100.0% 128.1/128.1MB downloaded\n"
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]
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}
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],
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"source": [
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"word_vectors = gensim.downloader.load(\"glove-wiki-gigaword-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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0.30817 , 0.30938 , 0.52803 , -0.92543 , -0.73671 ,\n",
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" 0.63475 , 0.44197 , 0.10262 , -0.09142 , -0.56607 ,\n",
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" -0.5327 , 0.2013 , 0.7704 , -0.13983 , 0.13727 ,\n",
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" 1.1128 , 0.89301 , -0.17869 , -0.0019722, 0.57289 ,\n",
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" 0.59479 , 0.50428 , -0.28991 , -1.3491 , 0.42756 ,\n",
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" 1.2748 , -1.1613 , -0.41084 , 0.042804 , 0.54866 ,\n",
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" 0.18897 , 0.3759 , 0.58035 , 0.66975 , 0.81156 ,\n",
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" 0.93864 , -0.51005 , -0.070079 , 0.82819 , -0.35346 ,\n",
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" 0.21086 , -0.24412 , -0.16554 , -0.78358 , -0.48482 ,\n",
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" 0.38968 , -0.86356 , -0.016391 , 0.31984 , -0.49246 ,\n",
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" -0.069363 , 0.018869 , -0.098286 , 1.3126 , -0.12116 ,\n",
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" -1.2399 , -0.091429 , 0.35294 , 0.64645 , 0.089642 ,\n",
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" 0.70294 , 1.1244 , 0.38639 , 0.52084 , 0.98787 ,\n",
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" 0.79952 , -0.34625 , 0.14095 , 0.80167 , 0.20987 ,\n",
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" -0.86007 , -0.15308 , 0.074523 , 0.40816 , 0.019208 ,\n",
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" 0.51587 , -0.34428 , -0.24525 , -0.77984 , 0.27425 ,\n",
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" 0.22418 , 0.20164 , 0.017431 , -0.014697 , -1.0235 ,\n",
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" -0.39695 , -0.0056188, 0.30569 , 0.31748 , 0.021404 ,\n",
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" 0.11837 , -0.11319 , 0.42456 , 0.53405 , -0.16717 ,\n",
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" -0.27185 , -0.6255 , 0.12883 , 0.62529 , -0.52086 ],\n",
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" dtype=float32)"
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]
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},
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"execution_count": 5,
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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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"word_vectors['dog']"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"100"
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]
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},
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"execution_count": 6,
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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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"len(word_vectors['dog'])"
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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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"$ A = (a_1, a_2, \\ldots, a_n)$\n",
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"\n",
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"$ B = (b_1, b_2, \\ldots, b_n)$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"$A \\cdot B = a_1* b_1 + a_2*b_2 + \\ldots a_n*b_n$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"$A \\cdot B = |A| |B| cos(\\theta)$"
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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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"cosine_similarity = $\\frac{A \\cdot B}{|A||B|}$"
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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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""
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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": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0.24340999, 0.23372999, 0.34519994, -1.19175 , -1.4724072 ,\n",
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" 0.34235 , 0.60779 , 0.261443 , 0.06009999, -1.37846 ,\n",
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" -0.88091004, 0.08861998, 1.05097 , -0.37221998, -0.05504 ,\n",
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" 2.07504 , 1.2128501 , -0.17209001, 0.5188256 , 0.68386996,\n",
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" 0.26919997, 0.977559 , -0.41735998, -2.29253 , 0.06891 ,\n",
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" 1.9723799 , -1.7875899 , -0.1394 , -0.08426201, 0.73421997,\n",
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" 0.449713 , 0.27947 , 1.1328939 , 1.48901 , 1.44769 ,\n",
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" 2.25301 , -0.23492998, -0.721868 , 0.78779006, -0.73836505,\n",
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" 0.88069 , -0.447323 , -1.29005 , -1.39741 , -1.10009 ,\n",
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" 0.50502 , -1.6576351 , -0.055184 , 0.38991004, -0.76956004,\n",
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" 0.185334 , 0.43640798, -0.882702 , 0.83290005, 0.13615999,\n",
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" -0.23210001, 0.58739203, 0.24005997, 0.05180001, -0.398276 ,\n",
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" 0.99437 , 1.40552 , 1.3153701 , 1.20883 , 1.23647 ,\n",
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" 1.692517 , -1.5952799 , -0.22698998, 2.10365 , 0.15522999,\n",
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" -1.87457 , -0.01184002, 0.03998601, 1.0829899 , -0.315964 ,\n",
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" 0.98266095, -0.86874 , 0.09540001, -1.0042601 , 0.83836997,\n",
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" -0.29442003, 0.05798 , 0.063619 , 0.197066 , -0.7356999 ,\n",
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" -0.222 , 0.5118224 , 0.73807997, 0.733638 , 0.577438 ,\n",
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" -0.04933 , 0.14863001, 0.39170003, 1.022125 , -0.08759001,\n",
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" -0.589356 , -0.86798 , 1.19477 , 1.211442 , -0.50261 ],\n",
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" dtype=float32)"
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]
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},
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"execution_count": 7,
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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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"word_vectors['dog'] + word_vectors['dog'] - word_vectors['man']"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('yellow', 0.7358633279800415),\n",
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" ('red', 0.7140780687332153),\n",
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" ('blue', 0.7118035554885864),\n",
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" ('green', 0.7111418843269348),\n",
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" ('pink', 0.6775072813034058),\n",
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" ('purple', 0.6774232387542725),\n",
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" ('black', 0.6709616184234619),\n",
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" ('colored', 0.665260910987854),\n",
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" ('lemon', 0.6251963376998901),\n",
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" ('peach', 0.616862416267395)]"
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]
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},
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"execution_count": 8,
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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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"word_vectors.most_similar(positive=['orange'])"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('queen', 0.7698541283607483),\n",
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" ('monarch', 0.6843380928039551),\n",
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" ('throne', 0.6755737066268921),\n",
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" ('daughter', 0.6594556570053101),\n",
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" ('princess', 0.6520533561706543),\n",
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" ('prince', 0.6517034769058228),\n",
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" ('elizabeth', 0.6464517116546631),\n",
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" ('mother', 0.631171703338623),\n",
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" ('emperor', 0.6106470823287964),\n",
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" ('wife', 0.6098655462265015)]"
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]
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},
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"execution_count": 9,
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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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"word_vectors.most_similar(positive=['woman', 'king'], negative=['man'])"
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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": 10,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('berlin', 0.8846380710601807),\n",
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" ('frankfurt', 0.7985544204711914),\n",
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" ('vienna', 0.76759934425354),\n",
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" ('munich', 0.7542588710784912),\n",
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" ('hamburg', 0.718237042427063),\n",
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" ('bonn', 0.6890878677368164),\n",
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" ('prague', 0.6842441558837891),\n",
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" ('cologne', 0.6762093305587769),\n",
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" ('zurich', 0.6653269529342651),\n",
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" ('leipzig', 0.6619254350662231)]"
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]
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},
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"execution_count": 10,
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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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"word_vectors.most_similar(positive=['paris', 'germany'], negative=['france'])"
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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": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('walked', 0.6780266761779785),\n",
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" ('crawled', 0.6523419618606567),\n",
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" ('wandered', 0.6384279727935791),\n",
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" ('hopped', 0.6131909489631653),\n",
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" ('walks', 0.6122221946716309),\n",
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" ('walk', 0.6120144128799438),\n",
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" ('strolled', 0.6010454893112183),\n",
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" ('slept', 0.5912748575210571),\n",
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" ('wandering', 0.5861444473266602),\n",
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" ('waited', 0.5791574716567993)]"
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]
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},
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"execution_count": 11,
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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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"word_vectors.most_similar(positive=['walking', 'swam'], negative=['swimming'])"
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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": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('puppies', 0.6867596507072449),\n",
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" ('kitten', 0.6866798400878906),\n",
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" ('kittens', 0.6383703947067261),\n",
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" ('monkey', 0.6171090602874756),\n",
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" ('rabbit', 0.6136822700500488),\n",
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" ('pup', 0.6054644584655762),\n",
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" ('tabby', 0.5937005281448364),\n",
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" ('retriever', 0.5934329628944397),\n",
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" ('bitch', 0.5817775726318359),\n",
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" ('hound', 0.5778555870056152)]"
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]
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},
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"execution_count": 12,
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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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"word_vectors.most_similar(positive=['puppy', 'cat'], negative=['dog'])"
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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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{
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"data": {
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"text/plain": [
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"[('dog', 0.8798074722290039),\n",
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" ('rabbit', 0.7424427270889282),\n",
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" ('cats', 0.7323004007339478),\n",
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" ('monkey', 0.7288710474967957),\n",
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" ('pet', 0.7190139293670654),\n",
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" ('dogs', 0.7163873314857483),\n",
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" ('mouse', 0.6915251016616821),\n",
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" ('puppy', 0.6800068616867065),\n",
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" ('rat', 0.6641027331352234),\n",
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" ('spider', 0.6501134634017944)]"
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]
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},
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"execution_count": 13,
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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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"word_vectors.most_similar(positive=['cat'])"
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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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""
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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