05: Topic_Modeling_with_BERTopic_T.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/Topic_Modeling/blob/main/05%3A%20Topic_Modeling_with_BERTopic_T.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vG2NIJ6UcK-b"
      },
      "source": [
        "# Demo of BERTopic\n",
        "\n",
        "## What is BERTopic\n",
        "## BERTopic demo on arxiv dataset\n",
        "## BERTopic demo with custom embeddings,dimesnionality reduction and clustering on arxiv dataset\n",
        "## BERTopic vs LDA \n",
        "## Installation Notes\n",
        "\n",
        "Video walkthrought the code : https://www.youtube.com/watch?v=yIPrgvuxJ5o\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DdQBO9EJcK-i"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONscteJjd_XN",
        "outputId": "ad2bbcb5-5f9d-4961-f8a2-50fa6c463d69"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZrNyWnYcK-n"
      },
      "source": [
        "Loading function taken from: https://www.kaggle.com/foolofatook/zero-shot-classification-with-huggingface-pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1TLGXmw7cK-n"
      },
      "outputs": [],
      "source": [
        "data_file = r'/content/drive/MyDrive/dataset/tredencedb.zip (Unzipped Files)/arxiv-metadata-oai-snapshot.json'\n",
        "\n",
        "\"\"\" Using `yield` to load the JSON file in a loop to prevent Python memory issues if JSON is loaded directly\"\"\"\n",
        "\n",
        "def get_metadata():\n",
        "    with open(data_file, 'r') as f:\n",
        "        for line in f:\n",
        "            yield line"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "T8tF54EacK-o"
      },
      "outputs": [],
      "source": [
        "metadata = get_metadata()\n",
        "ids = []\n",
        "titles = []\n",
        "abstracts = []\n",
        "categories = []\n",
        "\n",
        "for paper in metadata:\n",
        "    metaDict = json.loads(paper)\n",
        "    try:\n",
        "        try:\n",
        "            year = int(metaDict['journal-ref'][-4:])    ### Example Format: \"Phys.Rev.D76:013009,2007\"\n",
        "        except:\n",
        "            year = int(metaDict['journal-ref'][-5:-1])    ### Example Format: \"Phys.Rev.D76:013009,(2007)\"\n",
        "        if(year == 2020 or year == 2019 or year == 2018):\n",
        "            ids.append(metaDict['id'])\n",
        "            titles.append(metaDict['title'])\n",
        "            abstracts.append(metaDict['abstract'])\n",
        "            categories.append(metaDict['categories'])\n",
        "    except:\n",
        "        pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "tX7rMCqjcK-p",
        "outputId": "ba06f604-414f-4ce6-8e72-0c92b2f3a91a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "89327\n"
          ]
        }
      ],
      "source": [
        "df = pd.DataFrame({'id' : ids,'title' : titles,'abstract' : abstracts, 'categories' : categories})\n",
        "\n",
        "\n",
        "print(len(df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "hDIarnFOcK-u",
        "outputId": "f18c48ab-3ade-4384-dce3-55b537b68e52",
        "colab": {
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          "height": 206
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      },
      "outputs": [
        {
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              "          id                                              title  \\\n",
              "0  0708.0070  Bohmian Mechanics at Space-Time Singularities....   \n",
              "1  0709.1457  What happens to geometric phase when spin-orbi...   \n",
              "2  0710.1849  Regularity of solutions of the isoperimetric p...   \n",
              "3  0712.1975         Reentrant spin glass transition in LuFe2O4   \n",
              "4  0804.3104  Teichm\\\"uller Structures and Dual Geometric Gi...   \n",
              "\n",
              "                                            abstract  \\\n",
              "0    We develop an extension of Bohmian mechanics...   \n",
              "1    Spin-orbit interaction lifts accidental band...   \n",
              "2    In this work we consider a question in the c...   \n",
              "3    We have carried out a comprehensive investig...   \n",
              "4    The Gibbs measure theory for smooth potentia...   \n",
              "\n",
              "                          categories  \n",
              "0                           quant-ph  \n",
              "1                     cond-mat.other  \n",
              "2            math.DG math.AP math.MG  \n",
              "3  cond-mat.str-el cond-mat.mtrl-sci  \n",
              "4                    math.DS math.CV  "
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              "      <td>Bohmian Mechanics at Space-Time Singularities....</td>\n",
              "      <td>We develop an extension of Bohmian mechanics...</td>\n",
              "      <td>quant-ph</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0709.1457</td>\n",
              "      <td>What happens to geometric phase when spin-orbi...</td>\n",
              "      <td>Spin-orbit interaction lifts accidental band...</td>\n",
              "      <td>cond-mat.other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0710.1849</td>\n",
              "      <td>Regularity of solutions of the isoperimetric p...</td>\n",
              "      <td>In this work we consider a question in the c...</td>\n",
              "      <td>math.DG math.AP math.MG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0712.1975</td>\n",
              "      <td>Reentrant spin glass transition in LuFe2O4</td>\n",
              "      <td>We have carried out a comprehensive investig...</td>\n",
              "      <td>cond-mat.str-el cond-mat.mtrl-sci</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0804.3104</td>\n",
              "      <td>Teichm\\\"uller Structures and Dual Geometric Gi...</td>\n",
              "      <td>The Gibbs measure theory for smooth potentia...</td>\n",
              "      <td>math.DS math.CV</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MwrDP61IcK-v"
      },
      "outputs": [],
      "source": [
        "cat_list= df['categories'].unique()\n",
        "print(*cat_list, sep = \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "JWY90dYPcK-w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "260aaa04-a24a-4bdb-881f-3393d7a2f9ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26822\n"
          ]
        }
      ],
      "source": [
        "ml_df = df[df['categories'].str.contains(\"cs.\")]\n",
        "sentencesList= ml_df['abstract'].tolist()\n",
        "print(len(ml_df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "69Dt8UHbcK-z",
        "outputId": "9dd24ae6-f086-4981-e972-e48bcd20dc48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  In this paper we leave the neighborhood of the singularity at the origin and\n",
            "turn to the singularity at the horizon. Using nonlinear superdistributional\n",
            "geometry and supergeneralized functions it seems possible to show that the\n",
            "horizon singularity is not only a coordinate singularity without leaving\n",
            "Schwarzschild coordinates. However the Tolman formula for the total energy $E$\n",
            "of a static and asymptotically flat spacetime,gives $E=mc^2$, as it should be.\n",
            "New class Colombeau solutions to Einstein field equations is obtained.New class\n",
            "Colombeau solutions to Einstein field equations is obtained. The vacuum energy\n",
            "density of free scalar quantum field ${\\Phi}$ with a distributional background\n",
            "spacetime also is considered.It has been widely believed that, except in very\n",
            "extreme situations, the influence of acceleration on quantum fields should\n",
            "amount to just small, sub-dominant contributions. Here we argue that this\n",
            "belief is wrong by showing that in a Rindler distributional background\n",
            "spacetime with distributional Levi-Civit\\`a connection the vacuum energy of\n",
            "free quantum fields is forced, by the very same background distributional\n",
            "space-time such a Rindler distributional background space-time, to become\n",
            "dominant over any classical energy density component.This semiclassical gravity\n",
            "effect finds its roots in the singular behavior of quantum fields on a Rindler\n",
            "distributional space-times with distributional Levi-Civit\\`a connection. In\n",
            "particular we obtain that the vacuum fluctuations $<{\\Phi}^2({\\delta})>$ have a\n",
            "singular behavior at a Rindler horizon $\\delta = 0$.Therefore sufficiently\n",
            "strongly accelerated observer burns up near the Rindler horizon. Thus\n",
            "Polchinski account does not violate of the Einstein equivalence principle.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(sentencesList[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ivRsckSncK-3",
        "outputId": "11523018-4368-422a-d248-2b23b304f0fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2499\n"
          ]
        }
      ],
      "source": [
        "sampleSentencesList = sentencesList[1:2500]\n",
        "print(len(sampleSentencesList))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bertopic"
      ],
      "metadata": {
        "id": "9sJitVEeiSva"
      },
      "execution_count": null,
      "outputs": []
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>24</td>\n",
              "      <td>23</td>\n",
              "      <td>24_imaging_speckle_light_ghost</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>25</td>\n",
              "      <td>21</td>\n",
              "      <td>25_neural_neurons_networks_deep</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>26</td>\n",
              "      <td>21</td>\n",
              "      <td>26_imaging_mri_reconstruction_das</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>27</td>\n",
              "      <td>20</td>\n",
              "      <td>27_charge_field_electromagnetic_electrodynamics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>28_shear_stress_granular_flow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>29</td>\n",
              "      <td>19</td>\n",
              "      <td>29_classifier_classifiers_data_classification</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>30</td>\n",
              "      <td>19</td>\n",
              "      <td>30_isotope_nuclear_fractionation_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>31</td>\n",
              "      <td>18</td>\n",
              "      <td>31_privacy_data_users_on</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>32</td>\n",
              "      <td>17</td>\n",
              "      <td>32_magnetic_spin_field_optical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>33</td>\n",
              "      <td>16</td>\n",
              "      <td>33_boltzmann_lattice_flows_lb</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>36</td>\n",
              "      <td>15</td>\n",
              "      <td>36_codes_lcd_code_linear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>35</td>\n",
              "      <td>15</td>\n",
              "      <td>35_power_scheduling_taxi_problem</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>34</td>\n",
              "      <td>15</td>\n",
              "      <td>34_contact_drop_liquid_slip</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>39</td>\n",
              "      <td>15</td>\n",
              "      <td>39_learning_quantum_machine_reinforcement</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>38</td>\n",
              "      <td>15</td>\n",
              "      <td>38_students_student_research_education</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>37</td>\n",
              "      <td>15</td>\n",
              "      <td>37_inference_bayesian_posterior_data</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>40</td>\n",
              "      <td>14</td>\n",
              "      <td>40_quantum_entanglement_coherence_multilevel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>41</td>\n",
              "      <td>14</td>\n",
              "      <td>41_population_genotype_species_genemating</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>42</td>\n",
              "      <td>14</td>\n",
              "      <td>42_learning_bandit_robot_problems</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>43</td>\n",
              "      <td>14</td>\n",
              "      <td>43_control_safety_vehicles_vehicle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>44</td>\n",
              "      <td>13</td>\n",
              "      <td>44_wave_waves_faraday_forcing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>45</td>\n",
              "      <td>13</td>\n",
              "      <td>45_deep_neural_learning_machine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>46</td>\n",
              "      <td>13</td>\n",
              "      <td>46_users_mobile_network_mobility</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>47</td>\n",
              "      <td>13</td>\n",
              "      <td>47_commutativity_timevarying_systems_system</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>48</td>\n",
              "      <td>12</td>\n",
              "      <td>48_semantics_execution_actor_traversal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>49</td>\n",
              "      <td>12</td>\n",
              "      <td>49_transmit_interference_channel_wireless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>50</td>\n",
              "      <td>12</td>\n",
              "      <td>50_hamiltonian_basis_algorithm_quantum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>51</td>\n",
              "      <td>12</td>\n",
              "      <td>51_steganalysis_audio_steganography_intelligib...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>52</td>\n",
              "      <td>12</td>\n",
              "      <td>52_stochastic_pdes_uncertainty_cfd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "from bertopic import BERTopic\n",
        "topic_model = BERTopic(calculate_probabilities= True)\n",
        "topics, probabilities = topic_model.fit_transform(sampleSentencesList)\n",
        "topic_model.get_topic_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "P9kEpkE1cK-5",
        "outputId": "7fcf14d1-3d5f-45a2-d2a0-ad866cf8232f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('networks', 0.018567639612374797),\n",
              " ('network', 0.017792097954209676),\n",
              " ('of', 0.01654872741333266),\n",
              " ('the', 0.015450702155142075),\n",
              " ('to', 0.01490682933764859),\n",
              " ('and', 0.014302353178726284),\n",
              " ('we', 0.01361832880627492),\n",
              " ('in', 0.013535091596725668),\n",
              " ('social', 0.01263761563134332),\n",
              " ('that', 0.012471584890637074)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "topic_model.get_topic(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "yDmP2tMYcK-6",
        "outputId": "3cbaab19-998d-4f3f-fb56-0de4b38e2f32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('students', 0.07391509768206923),\n",
              " ('student', 0.040725220111115165),\n",
              " ('research', 0.032084930994559245),\n",
              " ('education', 0.029909470165185328),\n",
              " ('gender', 0.023927576132148264),\n",
              " ('learning', 0.022844922763222617),\n",
              " ('courses', 0.02196632721483048),\n",
              " ('framing', 0.02196632721483048),\n",
              " ('physics', 0.020360386580328693),\n",
              " ('and', 0.01975721453842239)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "topic_model.get_topic(38)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "O8UZKs5KcK-7",
        "outputId": "df10833c-81dc-40b9-d795-337322312b6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "topic_model.visualize_barchart(top_n_topics=10)"
      ]
    },
    {
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "topic_model.visualize_topics()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "1xs1Up0ncK-9",
        "outputId": "fb8fe493-03cf-4ecc-8285-838189c1f475",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "topic_model.visualize_distribution(probabilities[1001])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "-S88iCvbcK_A"
      },
      "outputs": [],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "dim_model = PCA(n_components=5)\n",
        "cluster_model = KMeans(n_clusters=50)\n",
        "\n",
        "topic_model = BERTopic(umap_model=dim_model, embedding_model=\"allenai-specter\", \n",
        "                       hdbscan_model=cluster_model, calculate_probabilities= True)\n",
        "topics, probabilities = topic_model.fit_transform(sampleSentencesList)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "lUcRhMZ_cK_B",
        "outputId": "93e098d2-09b8-47e1-a744-680400024c30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Topic  Count                                   Name\n",
              "0       0    110                        0_of_and_to_the\n",
              "1       1     91                       1_of_the_to_that\n",
              "2       2     88                       2_we_of_the_that\n",
              "3       3     72                 3_data_to_learning_and\n",
              "4       4     68              4_networks_network_of_the\n",
              "5       5     66              5_problem_we_algorithm_of\n",
              "6       6     66                        6_and_the_of_to\n",
              "7       7     64         7_plasma_electron_the_magnetic\n",
              "8       8     63                    8_optical_the_of_in\n",
              "9       9     61       9_optical_light_photonic_quantum\n",
              "10     10     60          10_image_images_saliency_deep\n",
              "11     11     60              11_the_gravity_of_quantum\n",
              "12     12     59                     12_the_of_flows_is\n",
              "13     13     56                  13_the_control_of_and\n",
              "14     14     55                      14_the_of_flow_in\n",
              "15     15     55             15_networks_of_the_network\n",
              "16     16     54        16_laser_radiation_electron_the\n",
              "17     17     53                   17_of_the_cells_that\n",
              "18     18     53                       18_and_to_the_of\n",
              "19     19     52  19_quantum_channel_channels_classical\n",
              "20     20     51                 20_spin_atoms_state_of\n",
              "21     21     51                 21_image_sparse_the_to\n",
              "22     22     50                       22_the_and_of_is\n",
              "23     23     50                23_quantum_the_atoms_of\n",
              "24     24     49                  24_liquid_the_of_cell\n",
              "25     25     48                    25_the_in_cavity_of\n",
              "26     26     46                   26_of_we_for_quantum\n",
              "27     27     46              27_the_turbulence_of_flow\n",
              "28     28     44                      28_the_to_and_for\n",
              "29     29     43               29_codes_quantum_that_we\n",
              "30     30     43                  30_channel_the_and_is\n",
              "31     31     43    31_information_security_channel_the\n",
              "32     32     42                       32_the_of_and_in\n",
              "33     33     41               33_in_the_topological_of\n",
              "34     34     40                     34_data_to_and_the\n",
              "35     35     40            35_frequency_optical_the_of\n",
              "39     36     39          36_detector_the_experiment_10\n",
              "38     37     39                   37_the_of_systems_in\n",
              "37     38     39              38_photon_phase_of_scheme\n",
              "36     39     39             39_wave_in_the_propagation\n",
              "40     40     38                     40_the_atom_of_for\n",
              "41     41     36                        41_the_of_is_in\n",
              "42     42     35       42_materials_optical_quantum_and\n",
              "43     43     33             43_quantum_learning_of_the\n",
              "44     44     32     44_functional_calculations_the_dft\n",
              "45     45     31        45_dmas_calibration_imaging_the\n",
              "46     46     29               46_imaging_pulses_the_of\n",
              "47     47     27      47_interactions_the_disorder_spin\n",
              "48     48     26              48_widehat_the_of_driving\n",
              "49     49     23            49_gravitational_the_and_km"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-227b1d99-05cd-4d34-843f-ca256792c380\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>0_of_and_to_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>91</td>\n",
              "      <td>1_of_the_to_that</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>88</td>\n",
              "      <td>2_we_of_the_that</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>72</td>\n",
              "      <td>3_data_to_learning_and</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>68</td>\n",
              "      <td>4_networks_network_of_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>66</td>\n",
              "      <td>5_problem_we_algorithm_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>66</td>\n",
              "      <td>6_and_the_of_to</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>64</td>\n",
              "      <td>7_plasma_electron_the_magnetic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>63</td>\n",
              "      <td>8_optical_the_of_in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>61</td>\n",
              "      <td>9_optical_light_photonic_quantum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>60</td>\n",
              "      <td>10_image_images_saliency_deep</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11</td>\n",
              "      <td>60</td>\n",
              "      <td>11_the_gravity_of_quantum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12</td>\n",
              "      <td>59</td>\n",
              "      <td>12_the_of_flows_is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>13</td>\n",
              "      <td>56</td>\n",
              "      <td>13_the_control_of_and</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14</td>\n",
              "      <td>55</td>\n",
              "      <td>14_the_of_flow_in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>15</td>\n",
              "      <td>55</td>\n",
              "      <td>15_networks_of_the_network</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>16</td>\n",
              "      <td>54</td>\n",
              "      <td>16_laser_radiation_electron_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>17</td>\n",
              "      <td>53</td>\n",
              "      <td>17_of_the_cells_that</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>18</td>\n",
              "      <td>53</td>\n",
              "      <td>18_and_to_the_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>52</td>\n",
              "      <td>19_quantum_channel_channels_classical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>20</td>\n",
              "      <td>51</td>\n",
              "      <td>20_spin_atoms_state_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>21</td>\n",
              "      <td>51</td>\n",
              "      <td>21_image_sparse_the_to</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>22</td>\n",
              "      <td>50</td>\n",
              "      <td>22_the_and_of_is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>23</td>\n",
              "      <td>50</td>\n",
              "      <td>23_quantum_the_atoms_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>24</td>\n",
              "      <td>49</td>\n",
              "      <td>24_liquid_the_of_cell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>25</td>\n",
              "      <td>48</td>\n",
              "      <td>25_the_in_cavity_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>26</td>\n",
              "      <td>46</td>\n",
              "      <td>26_of_we_for_quantum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>27</td>\n",
              "      <td>46</td>\n",
              "      <td>27_the_turbulence_of_flow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>28</td>\n",
              "      <td>44</td>\n",
              "      <td>28_the_to_and_for</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>29</td>\n",
              "      <td>43</td>\n",
              "      <td>29_codes_quantum_that_we</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>30</td>\n",
              "      <td>43</td>\n",
              "      <td>30_channel_the_and_is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>31</td>\n",
              "      <td>43</td>\n",
              "      <td>31_information_security_channel_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>32</td>\n",
              "      <td>42</td>\n",
              "      <td>32_the_of_and_in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>33</td>\n",
              "      <td>41</td>\n",
              "      <td>33_in_the_topological_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>34</td>\n",
              "      <td>40</td>\n",
              "      <td>34_data_to_and_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>35_frequency_optical_the_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>36</td>\n",
              "      <td>39</td>\n",
              "      <td>36_detector_the_experiment_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>37</td>\n",
              "      <td>39</td>\n",
              "      <td>37_the_of_systems_in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>38</td>\n",
              "      <td>39</td>\n",
              "      <td>38_photon_phase_of_scheme</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>39</td>\n",
              "      <td>39</td>\n",
              "      <td>39_wave_in_the_propagation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>40</td>\n",
              "      <td>38</td>\n",
              "      <td>40_the_atom_of_for</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>41</td>\n",
              "      <td>36</td>\n",
              "      <td>41_the_of_is_in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>42</td>\n",
              "      <td>35</td>\n",
              "      <td>42_materials_optical_quantum_and</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>43</td>\n",
              "      <td>33</td>\n",
              "      <td>43_quantum_learning_of_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>44</td>\n",
              "      <td>32</td>\n",
              "      <td>44_functional_calculations_the_dft</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>45</td>\n",
              "      <td>31</td>\n",
              "      <td>45_dmas_calibration_imaging_the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>46</td>\n",
              "      <td>29</td>\n",
              "      <td>46_imaging_pulses_the_of</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>47</td>\n",
              "      <td>27</td>\n",
              "      <td>47_interactions_the_disorder_spin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>48</td>\n",
              "      <td>26</td>\n",
              "      <td>48_widehat_the_of_driving</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>49</td>\n",
              "      <td>23</td>\n",
              "      <td>49_gravitational_the_and_km</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 24
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      ],
      "source": [
        "topic_model.get_topic_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "5BGgs_gmcK_B",
        "outputId": "54e6d051-9222-4798-d804-6d50b715ae9e",
        "colab": {
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      "outputs": [
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          "data": {
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      "source": [
        "topic_model.visualize_topics()"
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