word embedding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Embedding Techniques using Embedding Layer in Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.text import one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### sentences\n",
    "sent=[\"I am a good boy\",\n",
    "      \"You are a good girl\",\n",
    "      \"A for Apple\",\n",
    "      \"I am a Data Scientist\",\n",
    "      \"This NLP blog is so easy to understand\",\n",
    "      \"Refer my other blogs too\",\n",
    "      \"I had written blog on Machine Learning\",\n",
    "      \"I had written blog on Data Preprocessing\",\n",
    "      \"I had written blog on Statistics\",\n",
    "      \"I had written blog on Deep Learning\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I am a good boy',\n",
       " 'You are a good girl',\n",
       " 'A for Apple',\n",
       " 'I am a Data Scientist',\n",
       " 'This NLP blog is so easy to understand',\n",
       " 'Refer my other blogs too',\n",
       " 'I had written blog on Machine Learning',\n",
       " 'I had written blog on Data Preprocessing',\n",
       " 'I had written blog on Statistics',\n",
       " 'I had written blog on Deep Learning']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Vocabulary size\n",
    "voc_size=10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One Hot Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6875, 381, 3645, 2034, 7200], [1993, 4558, 3645, 2034, 2286], [3645, 5875, 1001], [6875, 381, 3645, 1799, 4542], [327, 6182, 3680, 4658, 5981, 8525, 675, 8230], [5533, 6974, 8791, 728, 3064], [6875, 62, 5489, 3680, 2105, 3230, 5236], [6875, 62, 5489, 3680, 2105, 1799, 1248], [6875, 62, 5489, 3680, 2105, 5378], [6875, 62, 5489, 3680, 2105, 836, 5236]]\n"
     ]
    }
   ],
   "source": [
    "onehot_repr=[one_hot(words,voc_size)for words in sent] \n",
    "print(onehot_repr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Embedding Represntation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Embedding\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    0    0 6875  381 3645 2034 7200]\n",
      " [   0    0    0 1993 4558 3645 2034 2286]\n",
      " [   0    0    0    0    0 3645 5875 1001]\n",
      " [   0    0    0 6875  381 3645 1799 4542]\n",
      " [ 327 6182 3680 4658 5981 8525  675 8230]\n",
      " [   0    0    0 5533 6974 8791  728 3064]\n",
      " [   0 6875   62 5489 3680 2105 3230 5236]\n",
      " [   0 6875   62 5489 3680 2105 1799 1248]\n",
      " [   0    0 6875   62 5489 3680 2105 5378]\n",
      " [   0 6875   62 5489 3680 2105  836 5236]]\n"
     ]
    }
   ],
   "source": [
    "sent_length=8\n",
    "embedded_docs=pad_sequences(onehot_repr,padding='pre',maxlen=sent_length)\n",
    "print(embedded_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim=10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Embedding(voc_size,10,input_length=sent_length))\n",
    "model.compile('adam','mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 8, 10)             100000    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 100,000\n",
      "Trainable params: 100,000\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 119ms/step\n",
      "[[[ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
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      "    5.03488630e-03 -5.23948669e-03]\n",
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      "    1.42068975e-02 -4.56177369e-02]\n",
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      "    1.07954256e-02  1.77051313e-02 -3.05550229e-02  3.29321139e-02\n",
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      "    4.34179939e-02  1.45388767e-03]]\n",
      "\n",
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      "    3.24268825e-02 -4.24995534e-02 -3.24008241e-02  1.40499212e-02\n",
      "    1.19180307e-02 -1.70343406e-02]\n",
      "  [ 1.99398659e-02  8.99872929e-03 -1.00243576e-02 -4.81173508e-02\n",
      "    1.07954256e-02  1.77051313e-02 -3.05550229e-02  3.29321139e-02\n",
      "    3.68885286e-02 -1.66966543e-02]\n",
      "  [ 1.42280944e-02  2.18499824e-03  3.92125137e-02  1.02446787e-02\n",
      "   -4.46159728e-02  2.36477964e-02 -1.64475217e-02 -1.16877444e-02\n",
      "   -4.88481298e-02 -4.50045951e-02]\n",
      "  [ 2.44570486e-02 -4.49580327e-02  1.32681020e-02  3.73985805e-02\n",
      "    4.38443311e-02  1.98519267e-02 -3.47744599e-02 -3.39813605e-02\n",
      "   -3.18689570e-02 -1.74302831e-02]]\n",
      "\n",
      " [[ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 1.99398659e-02  8.99872929e-03 -1.00243576e-02 -4.81173508e-02\n",
      "    1.07954256e-02  1.77051313e-02 -3.05550229e-02  3.29321139e-02\n",
      "    3.68885286e-02 -1.66966543e-02]\n",
      "  [-1.12685785e-02 -2.93534752e-02 -4.02160287e-02 -3.59884650e-03\n",
      "    2.05774568e-02 -4.75722812e-02  1.62624829e-02 -4.81612459e-02\n",
      "    8.00669193e-03  3.74176614e-02]\n",
      "  [ 1.80112235e-02 -4.86503616e-02 -4.83859889e-02  3.04510705e-02\n",
      "   -6.69252127e-04  1.57081150e-02  2.59489939e-03 -4.41710614e-02\n",
      "   -3.46702226e-02  3.42699140e-03]]\n",
      "\n",
      " [[ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [-4.38719057e-02 -3.56303938e-02 -1.45795718e-02 -2.53742468e-02\n",
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      "    5.03488630e-03 -5.23948669e-03]\n",
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      "   -3.78064513e-02  2.45551132e-02]]\n",
      "\n",
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      "   -4.49974909e-02  6.46623224e-03  3.01781408e-02 -2.48526223e-02\n",
      "    5.09982184e-03  4.62067761e-02]\n",
      "  [-4.53159921e-02 -2.49097701e-02  4.20139693e-02  2.86470726e-03\n",
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      "   -5.62173128e-03  2.77053751e-02]\n",
      "  [ 3.19645889e-02 -3.71550098e-02 -3.37924846e-02 -3.14829834e-02\n",
      "    4.01811674e-03 -2.29580402e-02 -2.18428504e-02 -1.64927728e-02\n",
      "    4.83789928e-02 -2.20050346e-02]\n",
      "  [ 7.53271580e-03 -4.91573326e-02 -4.82509397e-02 -5.09371608e-03\n",
      "    4.57335599e-02 -2.67281067e-02 -2.76951790e-02  3.38571705e-02\n",
      "    1.87625326e-02  1.30485334e-02]\n",
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      "    2.28199847e-02 -7.49018043e-03]]\n",
      "\n",
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      "   -1.70065761e-02  3.36885564e-02]\n",
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      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
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      "    9.92446020e-03  4.55413200e-02]\n",
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      "    1.90403722e-02 -1.41857490e-02]\n",
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      "   -4.82993834e-02  1.18307024e-03]\n",
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      "    4.05535810e-02 -4.44199815e-02  3.72016430e-03  2.39189900e-02\n",
      "    4.51732315e-02 -4.20869105e-02]]\n",
      "\n",
      " [[ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
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      "    3.94769423e-02 -2.41149794e-02 -2.29990967e-02 -1.74435377e-02\n",
      "    5.03488630e-03 -5.23948669e-03]\n",
      "  [ 3.97906788e-02  5.69566339e-03  4.65574153e-02  1.57656111e-02\n",
      "    2.05954574e-02 -1.38984099e-02  4.80513647e-03 -2.60382537e-02\n",
      "   -3.19476500e-02  3.75657566e-02]\n",
      "  [ 3.53084914e-02 -1.26655102e-02  2.98843496e-02  3.71329077e-02\n",
      "    2.32692622e-02 -9.38655064e-03 -1.12107508e-02  2.22596638e-02\n",
      "   -2.13413481e-02 -3.01920176e-02]\n",
      "  [ 3.19645889e-02 -3.71550098e-02 -3.37924846e-02 -3.14829834e-02\n",
      "    4.01811674e-03 -2.29580402e-02 -2.18428504e-02 -1.64927728e-02\n",
      "    4.83789928e-02 -2.20050346e-02]\n",
      "  [ 4.52154391e-02  2.09026001e-02  3.17183882e-03  4.62926663e-02\n",
      "   -3.32700089e-03 -3.43766436e-02 -1.60843730e-02  4.89640571e-02\n",
      "    1.66610517e-02 -4.83851694e-02]\n",
      "  [-1.41094700e-02 -1.42522343e-02  4.08175476e-02 -3.93281356e-02\n",
      "   -3.15067545e-02 -1.71568766e-02 -4.89087105e-02  3.84985544e-02\n",
      "   -2.40988266e-02 -4.16098237e-02]\n",
      "  [ 4.28437851e-02  7.98046589e-03  3.34854983e-02 -2.45920550e-02\n",
      "    2.27784403e-02 -4.56124209e-02 -5.57395071e-03 -1.54349692e-02\n",
      "   -3.94299254e-02  1.37694925e-03]]\n",
      "\n",
      " [[ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [-4.38719057e-02 -3.56303938e-02 -1.45795718e-02 -2.53742468e-02\n",
      "    3.94769423e-02 -2.41149794e-02 -2.29990967e-02 -1.74435377e-02\n",
      "    5.03488630e-03 -5.23948669e-03]\n",
      "  [ 3.97906788e-02  5.69566339e-03  4.65574153e-02  1.57656111e-02\n",
      "    2.05954574e-02 -1.38984099e-02  4.80513647e-03 -2.60382537e-02\n",
      "   -3.19476500e-02  3.75657566e-02]\n",
      "  [ 3.53084914e-02 -1.26655102e-02  2.98843496e-02  3.71329077e-02\n",
      "    2.32692622e-02 -9.38655064e-03 -1.12107508e-02  2.22596638e-02\n",
      "   -2.13413481e-02 -3.01920176e-02]\n",
      "  [ 3.19645889e-02 -3.71550098e-02 -3.37924846e-02 -3.14829834e-02\n",
      "    4.01811674e-03 -2.29580402e-02 -2.18428504e-02 -1.64927728e-02\n",
      "    4.83789928e-02 -2.20050346e-02]\n",
      "  [ 4.52154391e-02  2.09026001e-02  3.17183882e-03  4.62926663e-02\n",
      "   -3.32700089e-03 -3.43766436e-02 -1.60843730e-02  4.89640571e-02\n",
      "    1.66610517e-02 -4.83851694e-02]\n",
      "  [ 1.24661438e-02  3.47317122e-02  4.05186526e-02  1.64044760e-02\n",
      "   -2.70389803e-02  1.22379176e-02  3.45342271e-02 -5.68633154e-03\n",
      "   -1.51615143e-02 -4.87685204e-02]\n",
      "  [-2.96111703e-02  1.42145269e-02 -4.06256542e-02  4.37633432e-02\n",
      "   -3.83774042e-02 -2.30088122e-02 -6.27849251e-03 -2.52708048e-03\n",
      "    5.08926809e-04  8.45441967e-03]]\n",
      "\n",
      " [[ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [-4.38719057e-02 -3.56303938e-02 -1.45795718e-02 -2.53742468e-02\n",
      "    3.94769423e-02 -2.41149794e-02 -2.29990967e-02 -1.74435377e-02\n",
      "    5.03488630e-03 -5.23948669e-03]\n",
      "  [ 3.97906788e-02  5.69566339e-03  4.65574153e-02  1.57656111e-02\n",
      "    2.05954574e-02 -1.38984099e-02  4.80513647e-03 -2.60382537e-02\n",
      "   -3.19476500e-02  3.75657566e-02]\n",
      "  [ 3.53084914e-02 -1.26655102e-02  2.98843496e-02  3.71329077e-02\n",
      "    2.32692622e-02 -9.38655064e-03 -1.12107508e-02  2.22596638e-02\n",
      "   -2.13413481e-02 -3.01920176e-02]\n",
      "  [ 3.19645889e-02 -3.71550098e-02 -3.37924846e-02 -3.14829834e-02\n",
      "    4.01811674e-03 -2.29580402e-02 -2.18428504e-02 -1.64927728e-02\n",
      "    4.83789928e-02 -2.20050346e-02]\n",
      "  [ 4.52154391e-02  2.09026001e-02  3.17183882e-03  4.62926663e-02\n",
      "   -3.32700089e-03 -3.43766436e-02 -1.60843730e-02  4.89640571e-02\n",
      "    1.66610517e-02 -4.83851694e-02]\n",
      "  [-3.91236655e-02  1.49245746e-02  2.95093395e-02 -1.97192561e-02\n",
      "    1.11714490e-02 -2.16312651e-02  2.38418579e-07 -3.06767356e-02\n",
      "   -1.94294695e-02 -3.56176123e-02]]\n",
      "\n",
      " [[ 4.62999195e-03 -2.42123753e-03  2.33042240e-03  4.96025123e-02\n",
      "   -4.54834104e-02 -4.49165106e-02  1.15400665e-02  4.22747992e-02\n",
      "   -1.70065761e-02  3.36885564e-02]\n",
      "  [-4.38719057e-02 -3.56303938e-02 -1.45795718e-02 -2.53742468e-02\n",
      "    3.94769423e-02 -2.41149794e-02 -2.29990967e-02 -1.74435377e-02\n",
      "    5.03488630e-03 -5.23948669e-03]\n",
      "  [ 3.97906788e-02  5.69566339e-03  4.65574153e-02  1.57656111e-02\n",
      "    2.05954574e-02 -1.38984099e-02  4.80513647e-03 -2.60382537e-02\n",
      "   -3.19476500e-02  3.75657566e-02]\n",
      "  [ 3.53084914e-02 -1.26655102e-02  2.98843496e-02  3.71329077e-02\n",
      "    2.32692622e-02 -9.38655064e-03 -1.12107508e-02  2.22596638e-02\n",
      "   -2.13413481e-02 -3.01920176e-02]\n",
      "  [ 3.19645889e-02 -3.71550098e-02 -3.37924846e-02 -3.14829834e-02\n",
      "    4.01811674e-03 -2.29580402e-02 -2.18428504e-02 -1.64927728e-02\n",
      "    4.83789928e-02 -2.20050346e-02]\n",
      "  [ 4.52154391e-02  2.09026001e-02  3.17183882e-03  4.62926663e-02\n",
      "   -3.32700089e-03 -3.43766436e-02 -1.60843730e-02  4.89640571e-02\n",
      "    1.66610517e-02 -4.83851694e-02]\n",
      "  [ 1.46365203e-02  8.25859234e-03 -3.29543576e-02 -9.03822109e-03\n",
      "    6.42921776e-03 -3.82795110e-02 -1.57911070e-02 -4.46204804e-02\n",
      "    4.21719216e-02 -2.77978424e-02]\n",
      "  [ 4.28437851e-02  7.98046589e-03  3.34854983e-02 -2.45920550e-02\n",
      "    2.27784403e-02 -4.56124209e-02 -5.57395071e-03 -1.54349692e-02\n",
      "   -3.94299254e-02  1.37694925e-03]]]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(embedded_docs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    0,    0, 6875,  381, 3645, 2034, 7200])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedded_docs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 41ms/step\n",
      "[[ 0.00462999 -0.00242124  0.00233042  0.04960251 -0.04548341 -0.04491651\n",
      "   0.01154007  0.0422748  -0.01700658  0.03368856]\n",
      " [ 0.00462999 -0.00242124  0.00233042  0.04960251 -0.04548341 -0.04491651\n",
      "   0.01154007  0.0422748  -0.01700658  0.03368856]\n",
      " [ 0.00462999 -0.00242124  0.00233042  0.04960251 -0.04548341 -0.04491651\n",
      "   0.01154007  0.0422748  -0.01700658  0.03368856]\n",
      " [-0.04387191 -0.03563039 -0.01457957 -0.02537425  0.03947694 -0.02411498\n",
      "  -0.0229991  -0.01744354  0.00503489 -0.00523949]\n",
      " [ 0.00294453 -0.01822538 -0.04784692  0.00646306 -0.02394673  0.02493893\n",
      "   0.04911863  0.02870895  0.0142069  -0.04561774]\n",
      " [ 0.01993987  0.00899873 -0.01002436 -0.04811735  0.01079543  0.01770513\n",
      "  -0.03055502  0.03293211  0.03688853 -0.01669665]\n",
      " [ 0.01422809  0.002185    0.03921251  0.01024468 -0.04461597  0.0236478\n",
      "  -0.01644752 -0.01168774 -0.04884813 -0.0450046 ]\n",
      " [-0.02853187  0.01028491 -0.0240731   0.01282595 -0.01442986  0.02615631\n",
      "   0.00317208 -0.00773025  0.04341799  0.00145389]]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(embedded_docs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "version": 3
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   "mimetype": "text/x-python",
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