Ch3/08_Training_Dov2Vec_using_Gensim.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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    "colab_type": "text",
    "id": "NP_Xq8p7WubK"
   },
   "source": [
    "## Doc2Vec\n",
    "In this notebook we demonstrate how to train a doc2vec model on a custom corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
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    "colab_type": "code",
    "id": "jifk_HHmvVWf",
    "outputId": "a1f9b5ac-b706-4dd1-88db-35d41a52131a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
    "from nltk.tokenize import word_tokenize\n",
    "from pprint import pprint\n",
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rLKY1nkIvluD"
   },
   "outputs": [],
   "source": [
    "data = [\"dog bites man\",\n",
    "        \"man bites dog\",\n",
    "        \"dog eats meat\",\n",
    "        \"man eats food\"]\n",
    "\n",
    "tagged_data = [TaggedDocument(words=word_tokenize(word.lower()), tags=[str(i)]) for i, word in enumerate(data)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "rPssbTNiwEd9",
    "outputId": "b634cee4-5f3b-4823-c467-84dee2915ba5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[TaggedDocument(words=['dog', 'bites', 'man'], tags=['0']),\n",
       " TaggedDocument(words=['man', 'bites', 'dog'], tags=['1']),\n",
       " TaggedDocument(words=['dog', 'eats', 'meat'], tags=['2']),\n",
       " TaggedDocument(words=['man', 'eats', 'food'], tags=['3'])]"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dcxU67TCwSd8"
   },
   "outputs": [],
   "source": [
    "#dbow\n",
    "model_dbow = Doc2Vec(tagged_data,vector_size=20, min_count=1, epochs=2,dm=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "QLkpnvcTx6T9",
    "outputId": "b46139d3-bba7-4458-be90-99645fe30cbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.6232926e-02  7.2174487e-03 -1.8149924e-02  1.9396201e-02\n",
      " -1.0752278e-02  2.1854458e-02 -1.0387233e-02  5.0634018e-04\n",
      " -1.0485573e-02 -2.3734096e-02 -2.1500036e-02  1.1494607e-02\n",
      " -7.5711228e-05 -9.6793557e-03 -1.1162300e-02  2.3743849e-02\n",
      "  5.5664307e-03 -2.3691252e-02  1.7469667e-02 -8.0082798e-03]\n"
     ]
    }
   ],
   "source": [
    "print(model_dbow.infer_vector(['man','eats','food']))#feature vector of man eats food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "1KzwAgUJzQLW",
    "outputId": "46706354-9687-41d6-ceaa-cbe865bddf7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dog', 0.1856406182050705),\n",
       " ('meat', 0.12032049894332886),\n",
       " ('bites', 0.037392228841781616),\n",
       " ('food', -0.027777723968029022),\n",
       " ('eats', -0.29439008235931396)]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dbow.wv.most_similar(\"man\",topn=5)#top 5 most simlar words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "myGVWgudz9mW",
    "outputId": "542a19b6-c925-4263-f8df-50ec5ab87f9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1856406"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    " model_dbow.wv.n_similarity([\"dog\"],[\"man\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "1i2Vv2uY4kqg",
    "outputId": "0a74bbf1-4053-4399-a8de-9e0f2404a59d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference Vector of man eats food\n",
      "  [-1.6232852e-02  7.2173858e-03 -1.8149856e-02  1.9396329e-02\n",
      " -1.0752306e-02  2.1854490e-02 -1.0387184e-02  5.0630077e-04\n",
      " -1.0485582e-02 -2.3733964e-02 -2.1500139e-02  1.1494617e-02\n",
      " -7.5761047e-05 -9.6794488e-03 -1.1162374e-02  2.3743976e-02\n",
      "  5.5664619e-03 -2.3691194e-02  1.7469568e-02 -8.0082249e-03]\n",
      "Most similar words to man in our corpus\n",
      " [('dog', 0.1856406182050705), ('meat', 0.12032049894332886), ('bites', 0.037392228841781616), ('food', -0.027777723968029022), ('eats', -0.29439008235931396)]\n",
      "Similarity between man and dog:  0.1856406\n"
     ]
    }
   ],
   "source": [
    "#dm\n",
    "model_dm = Doc2Vec(tagged_data, min_count=1, vector_size=20, epochs=2,dm=1)\n",
    "\n",
    "print(\"Inference Vector of man eats food\\n \",model_dm.infer_vector(['man','eats','food']))\n",
    "\n",
    "print(\"Most similar words to man in our corpus\\n\",model_dm.wv.most_similar(\"man\",topn=5))\n",
    "print(\"Similarity between man and dog: \",model_dm.wv.n_similarity([\"dog\"],[\"man\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NmmM7yI31gMn"
   },
   "source": [
    "What happens when we compare between words which are not in the vocabulary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "YS-mciDx0ZiA",
    "outputId": "dcffc03f-b499-4266-e525-5c5a10d5fca6"
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-21897cb372b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_dm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'covid'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'man'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mn_similarity\u001b[0;34m(self, ws1, ws2)\u001b[0m\n\u001b[1;32m   1010\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mws1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mws2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mZeroDivisionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'At least one of the passed list is empty.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1012\u001b[0;31m         \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mws1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m         \u001b[0mv2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mws2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munitvec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munitvec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1010\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mws1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mws2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mZeroDivisionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'At least one of the passed list is empty.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1012\u001b[0;31m         \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mws1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m         \u001b[0mv2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mws2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munitvec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munitvec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, entities)\u001b[0m\n\u001b[1;32m    335\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentities\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0;31m# allow calls like trained_model['office'], as a shorthand for trained_model[['office']]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentity\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mentity\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentities\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mget_vector\u001b[0;34m(self, word)\u001b[0m\n\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 455\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwords_closer_than\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mword_vec\u001b[0;34m(self, word, use_norm)\u001b[0m\n\u001b[1;32m    450\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"word '%s' not in vocabulary\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"word 'covid' not in vocabulary\""
     ]
    }
   ],
   "source": [
    "model_dm.wv.n_similarity(['covid'],['man'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "17qDsxIpGx5n"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "name": "Training_Dov2Vec_using_Gensim.ipynb",
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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