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            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "tu-2sw-YSQte"
      },
      "outputs": [],
      "source": [
        "!pip install transformers sentence-transformers bertopic safetensors > /dev/null"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-Means Clustering\n",
        "\n",
        " K-Means requires that the number of clusters is specified beforehand."
      ],
      "metadata": {
        "id": "tMSue6Glh2G8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "BjRm7y-9h1WD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embed_minilm = SentenceTransformer('all-MiniLM-L6-v2')"
      ],
      "metadata": {
        "id": "Md7UEI8ZghJc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_corp = ['A man is eating food.',\n",
        "          'A man is eating a piece of bread.',\n",
        "          'A man is eating pasta.',\n",
        "          'The girl is carrying a baby.',\n",
        "          'The baby is carried by the woman',\n",
        "          'A man is riding a horse.',\n",
        "          'A man is riding a white horse on an enclosed ground.',\n",
        "          'A monkey is playing drums.',\n",
        "          'Someone in a gorilla costume is playing a set of drums.',\n",
        "          'A cheetah is running behind its prey.',\n",
        "          'A cheetah chases prey on across a field.'\n",
        "          ]"
      ],
      "metadata": {
        "id": "tD09aDEXiONq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_embed = embed_minilm.encode(data_corp)"
      ],
      "metadata": {
        "id": "27_FogVXiOKr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#starting the clustering process\n",
        "\n",
        "num_cluster = 5\n",
        "\n",
        "kmeans_model = KMeans(n_clusters=num_cluster)"
      ],
      "metadata": {
        "id": "4BvAkfgQiOHB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans_model.fit(data_embed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "1mg2EW6NiOEb",
        "outputId": "9785a585-188d-4554-ddfa-004e56fe5452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=5)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clusters = kmeans_model.labels_"
      ],
      "metadata": {
        "id": "amjc-5nHiOBX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clusters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7qFPGjhiN7U",
        "outputId": "484c8b06-1bfe-4ab4-921f-f22d3d30dc34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 1, 3, 3, 4, 4, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def make_df(cluster_list, text_list):\n",
        "  clustered_dataset = []\n",
        "  for ind, cluster in enumerate(cluster_list):\n",
        "    cluster_dict = {'cluster':cluster,\n",
        "                    'sentence':text_list[ind]}\n",
        "    clustered_dataset.append(cluster_dict)\n",
        "\n",
        "  cluster_dict = pd.DataFrame(clustered_dataset)\n",
        "\n",
        "  return cluster_dict"
      ],
      "metadata": {
        "id": "dm4TMeV9mRaO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_dataset = []\n",
        "for ind, cluster in enumerate(clusters):\n",
        "  cluster_dict = {'cluster':cluster,\n",
        "                  'sentence':data_corp[ind]}\n",
        "  clustered_dataset.append(cluster_dict)"
      ],
      "metadata": {
        "id": "p4bkrLqUiN3P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "cluster_dict = pd.DataFrame(clustered_dataset)\n",
        "\n",
        "cluster_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "-cTynrgfiN0L",
        "outputId": "c395e842-ff5f-4e28-d11b-40225f49fc76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    cluster                                           sentence\n",
              "0         0                              A man is eating food.\n",
              "1         0                  A man is eating a piece of bread.\n",
              "2         0                             A man is eating pasta.\n",
              "3         1                       The girl is carrying a baby.\n",
              "4         1                   The baby is carried by the woman\n",
              "5         3                           A man is riding a horse.\n",
              "6         3  A man is riding a white horse on an enclosed g...\n",
              "7         4                         A monkey is playing drums.\n",
              "8         4  Someone in a gorilla costume is playing a set ...\n",
              "9         2              A cheetah is running behind its prey.\n",
              "10        2           A cheetah chases prey on across a field."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9133764-40d3-42f5-b60f-f0ed721376b6\">\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>cluster</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A man is eating food.</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>A man is eating pasta.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>The girl is carrying a baby.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>The baby is carried by the woman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>A man is riding a horse.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>A man is riding a white horse on an enclosed g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>A monkey is playing drums.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4</td>\n",
              "      <td>Someone in a gorilla costume is playing a set ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>A cheetah is running behind its prey.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>A cheetah chases prey on across a field.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9133764-40d3-42f5-b60f-f0ed721376b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f9133764-40d3-42f5-b60f-f0ed721376b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f9133764-40d3-42f5-b60f-f0ed721376b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agglomerative Clustering\n",
        "\n",
        " Clusters below that threshold are merged. This algorithm can be useful if the number of clusters is unknown."
      ],
      "metadata": {
        "id": "I8pEC9ODk0uz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "OavrIbF0iNxp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.linalg.norm(data_embed, axis=1, keepdims=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BzffAj2iNvL",
        "outputId": "09e4148a-a22a-4d25-a950-fa19bd3d24e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.99999994],\n",
              "       [0.99999994],\n",
              "       [1.        ],\n",
              "       [1.        ],\n",
              "       [1.        ],\n",
              "       [0.9999999 ],\n",
              "       [1.        ],\n",
              "       [1.        ],\n",
              "       [1.        ],\n",
              "       [1.        ],\n",
              "       [0.99999994]], dtype=float32)"
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normalized_embed = data_embed / np.linalg.norm(data_embed, axis=1, keepdims=True)"
      ],
      "metadata": {
        "id": "W-G0rY0PiNso"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agglomerate_model = AgglomerativeClustering(n_clusters=None,\n",
        "                                            distance_threshold = 1.5)"
      ],
      "metadata": {
        "id": "XK2R5r6EiNpu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agglomerate_model.fit(normalized_embed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "A_pWiX77iNnW",
        "outputId": "231d8ece-270f-4c4b-b345-33683bfb9672"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(distance_threshold=1.5, n_clusters=None)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(distance_threshold=1.5, n_clusters=None)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AgglomerativeClustering</label><div class=\"sk-toggleable__content\"><pre>AgglomerativeClustering(distance_threshold=1.5, n_clusters=None)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agglo_clusters = agglomerate_model.labels_"
      ],
      "metadata": {
        "id": "AOK7RECTiNhE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agglo_clusters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAMceLCDiNd9",
        "outputId": "6a203dab-3d1d-43f0-9c22-55a199e5e166"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 4, 4, 1, 1, 2, 2, 3, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agglo_df = make_df(agglo_clusters,data_corp)\n",
        "#agglo_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "xchhTt1IiNZx",
        "outputId": "d389fff4-4e3e-458e-efa8-7bdca4b47f34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    cluster                                           sentence\n",
              "0         0                              A man is eating food.\n",
              "1         0                  A man is eating a piece of bread.\n",
              "2         0                             A man is eating pasta.\n",
              "3         4                       The girl is carrying a baby.\n",
              "4         4                   The baby is carried by the woman\n",
              "5         1                           A man is riding a horse.\n",
              "6         1  A man is riding a white horse on an enclosed g...\n",
              "7         2                         A monkey is playing drums.\n",
              "8         2  Someone in a gorilla costume is playing a set ...\n",
              "9         3              A cheetah is running behind its prey.\n",
              "10        3           A cheetah chases prey on across a field."
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>cluster</th>\n",
              "      <th>sentence</th>\n",
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              "      <td>0</td>\n",
              "      <td>A man is eating pasta.</td>\n",
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              "      <td>4</td>\n",
              "      <td>The girl is carrying a baby.</td>\n",
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              "      <td>4</td>\n",
              "      <td>The baby is carried by the woman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>A man is riding a horse.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>A man is riding a white horse on an enclosed g...</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>A monkey is playing drums.</td>\n",
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              "      <td>Someone in a gorilla costume is playing a set ...</td>\n",
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              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>A cheetah is running behind its prey.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>3</td>\n",
              "      <td>A cheetah chases prey on across a field.</td>\n",
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              "  </tbody>\n",
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              "      \n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-acd1ca7e-4c34-4b8b-9308-6dced41e3989');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fast Clustering for Massive Dataset"
      ],
      "metadata": {
        "id": "DPyw5ZGvoAbv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv\"\n",
        "dataset_path = \"quora_duplicate_questions.tsv\"\n",
        "max_corpus_size = 50000  # We limit our corpus to only the first 50k questions"
      ],
      "metadata": {
        "id": "DxV1S-vfoAOV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import util\n",
        "import os\n",
        "\n",
        "if not os.path.exists(dataset_path):\n",
        "    print(\"Download dataset\")\n",
        "    util.http_get(url, dataset_path)"
      ],
      "metadata": {
        "id": "xBEggoBMn-pC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "# Get all unique sentences from the file upto max_corpus_size\n",
        "\n",
        "corpus_sentences = set()\n",
        "\n",
        "with open(dataset_path, encoding='utf8') as fIn:\n",
        "\n",
        "    reader = csv.DictReader(fIn, delimiter='\\t', quoting=csv.QUOTE_MINIMAL)\n",
        "    for row in reader:\n",
        "        corpus_sentences.add(row['question1'])\n",
        "        corpus_sentences.add(row['question2'])\n",
        "        if len(corpus_sentences) >= max_corpus_size:\n",
        "            break"
      ],
      "metadata": {
        "id": "9DUOGbOvn-mW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corpus_sentences = list(corpus_sentences)"
      ],
      "metadata": {
        "id": "R5t7q7Qxn-kF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "quora_embed = embed_minilm.encode(corpus_sentences,\n",
        "                                  batch_size=64,\n",
        "                                  show_progress_bar=True,\n",
        "                                  convert_to_tensor=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ee454d8458854095b204a3c0e48f6ece",
            "2704ec3e1745420784278b8b6e4cfc53",
            "34ab894f2e6a4eb4b3f4a6a236bd20a5",
            "316eef9ded1c495f9b570c19bae02d70",
            "fdda605a850946be8a1326e656867199",
            "897f332e303e4e448f759181fc653097",
            "8c3bf34cde3d4b1892443e8ba180183a",
            "4932ef5ff3e64e8596d446c5912f80c5",
            "eb85cb4ee8ae4e7f809aee6bb268912a",
            "0422c7fc54fe45fe8cecfeb91d12bf24",
            "234577773b2c4d3c9198526e1789d4d2"
          ]
        },
        "id": "Z7V7ABcBn-gf",
        "outputId": "551cd019-46ed-4705-ba33-d895240d5caa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee454d8458854095b204a3c0e48f6ece"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "clusters_community = util.community_detection(quora_embed,\n",
        "                                              min_community_size=25,\n",
        "                                              threshold=0.75)\n",
        "\n",
        "complete = time.time()\n",
        "\n",
        "print(f\"Clustering completed in {complete - start} time\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EojEx8vHn-dp",
        "outputId": "5153d033-2b9c-496c-85e7-360ddc3b8283"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clustering completed in 51.11092782020569 time\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"There are {len(clusters_community)} clusters\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEmCjpO-n-ZB",
        "outputId": "8f0675e9-09d9-45bb-a618-24875e83d264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 61 clusters\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quora_clustered = []\n",
        "\n",
        "for ind, sentences in enumerate(clusters_community):\n",
        "  for sentence in sentences:\n",
        "    quora_dict = {\"cluster\":ind,\n",
        "                  \"sentence_id\":sentence,\n",
        "                  \"sentence\":corpus_sentences[sentence]}\n",
        "    quora_clustered.append(quora_dict)\n",
        "\n",
        "clustered_df = pd.DataFrame(quora_clustered)"
      ],
      "metadata": {
        "id": "C7yU0Z1Dn-Sf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "km20ZzGPtngI",
        "outputId": "0be97ea5-6507-4eaa-9758-e838c0f1f78e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      cluster  sentence_id                                           sentence\n",
              "0           0          859     How can I improve my English speaking ability?\n",
              "1           0         1010         How can I learn to speak English fluently?\n",
              "2           0         1115  How do I improve my overall native English lan...\n",
              "3           0         1232                  How can I learn to speak English?\n",
              "4           0         2192  How can I improve my english language skills? ...\n",
              "...       ...          ...                                                ...\n",
              "2279       60        45292  What is the step by step guide to invest in sh...\n",
              "2280       60        45399         How do I earn money from the stock market?\n",
              "2281       60        47162  What all does somebody need to know to start i...\n",
              "2282       60        47419        How do I make a profit in the stock market?\n",
              "2283       60        48637  If I want to invest in the stock market, how I...\n",
              "\n",
              "[2284 rows x 3 columns]"
            ],
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              "\n",
              "  <div id=\"df-97493673-7936-45a5-8fc4-27fec712a652\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cluster</th>\n",
              "      <th>sentence_id</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>859</td>\n",
              "      <td>How can I improve my English speaking ability?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1010</td>\n",
              "      <td>How can I learn to speak English fluently?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1115</td>\n",
              "      <td>How do I improve my overall native English lan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1232</td>\n",
              "      <td>How can I learn to speak English?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2192</td>\n",
              "      <td>How can I improve my english language skills? ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2279</th>\n",
              "      <td>60</td>\n",
              "      <td>45292</td>\n",
              "      <td>What is the step by step guide to invest in sh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2280</th>\n",
              "      <td>60</td>\n",
              "      <td>45399</td>\n",
              "      <td>How do I earn money from the stock market?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2281</th>\n",
              "      <td>60</td>\n",
              "      <td>47162</td>\n",
              "      <td>What all does somebody need to know to start i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2282</th>\n",
              "      <td>60</td>\n",
              "      <td>47419</td>\n",
              "      <td>How do I make a profit in the stock market?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2283</th>\n",
              "      <td>60</td>\n",
              "      <td>48637</td>\n",
              "      <td>If I want to invest in the stock market, how I...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2284 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97493673-7936-45a5-8fc4-27fec712a652')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-97493673-7936-45a5-8fc4-27fec712a652 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-97493673-7936-45a5-8fc4-27fec712a652');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_20newsgroups\n",
        "docs = fetch_20newsgroups(subset='all',  remove=('headers',\n",
        "                                                 'footers',\n",
        "                                                 'quotes'))['data']"
      ],
      "metadata": {
        "id": "l7sFWsCJv_v7"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(docs)"
      ],
      "metadata": {
        "id": "Y-AP-ZqQwa5z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "945ebdda-0a96-4999-f7d2-6792bc61c192"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18846"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs[5:50]"
      ],
      "metadata": {
        "id": "Ric-_6HoQ5vc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "docs_embed = embed_minilm.encode(docs[:5000],batch_size=64,\n",
        "                                  show_progress_bar=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1ef0b791c024481f881458a3bce2c9a2",
            "032f2d7b24404b81b7c4e9f563c6143b",
            "b4934fd4255d494ca5ffb18a42d0bd0b",
            "3134321a3645465d9d4ff224a6afebc6",
            "95c9bbb8655c4f3c9c2fbb2f968e8bd1",
            "edd0ee7efbd4452bbf609a374adb33c6",
            "09498c57cfe841f09641a4f9c1f884f7",
            "e88bd5ca17354a65a031d8d8bb008b23",
            "6dc42140b97843b4a37f281bceb230da",
            "b3feea294e7040da9b92109cb897046d",
            "d53cbb72c5144a2780922b98532281be"
          ]
        },
        "id": "GEMI_uOPrgsI",
        "outputId": "ecc4abfa-aa0a-47e2-c214-f11e47d2db15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ef0b791c024481f881458a3bce2c9a2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('docs_embeddings.pkl','wb') as fout:\n",
        "  pickle.dump({'sentences':docs[:5000],\n",
        "               'embeddings':docs_embed},\n",
        "              fout,\n",
        "              protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "metadata": {
        "id": "S0kTiYTTuxos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('/content/docs_embeddings.pkl','rb') as fin:\n",
        "  docs_embed = pickle.load(file=fin,fix_imports=True)"
      ],
      "metadata": {
        "id": "sqUjhIWzMMYG"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "docs_embed"
      ],
      "metadata": {
        "id": "1T5vgNMMMzLB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from bertopic import BERTopic\n",
        "\n",
        "topic_model = BERTopic(language=\"english\",\n",
        "                       calculate_probabilities=True,\n",
        "                       verbose=True)\n",
        "topics, probs = topic_model.fit_transform(embeddings=docs_embed['embeddings'],\n",
        "                                          documents=docs[:5000])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-xQ5BG7vTVb",
        "outputId": "c916b4ed-2a7a-4a44-a4d5-8abfbf157bc8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-06-16 03:16:36,874 - BERTopic - Reduced dimensionality\n",
            "2023-06-16 03:16:38,537 - BERTopic - Clustered reduced embeddings\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Access the frequent topics that were generated:\n",
        "\n",
        "topic_model.get_topic_info()"
      ],
      "metadata": {
        "id": "2yvbAuZvyZiZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.get_topic(0)"
      ],
      "metadata": {
        "id": "s5Q5HgvTvTRC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8f126a1-7438-471d-aa72-08c6fc2739f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('game', 0.013907644172698382),\n",
              " ('team', 0.012423780162947083),\n",
              " ('he', 0.012285414370801205),\n",
              " ('the', 0.011055874442977419),\n",
              " ('10', 0.01015492838806086),\n",
              " ('in', 0.010034090870948023),\n",
              " ('players', 0.009659262746097172),\n",
              " ('to', 0.009234142973611758),\n",
              " ('was', 0.009087920638540196),\n",
              " ('and', 0.008554304237091576)]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.get_document_info(docs[:5000])"
      ],
      "metadata": {
        "id": "uJhm0UAGvTNl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_list = topic_model.generate_topic_labels()"
      ],
      "metadata": {
        "id": "iXyZma3PvTIz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.reduce_topics(docs[:5000],nr_topics=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oH69ajn7043r",
        "outputId": "78437939-c0c4-4b3b-8d80-066381d2cf19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-06-16 01:31:20,805 - BERTopic - Reduced number of topics from 68 to 30\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bertopic._bertopic.BERTopic at 0x7f0304e02ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_reduced = topic_model.generate_topic_labels()\n",
        "topic_reduced"
      ],
      "metadata": {
        "id": "r2r4ZyOa1CQz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.find_topics(\"windows\")"
      ],
      "metadata": {
        "id": "vzVfkNfO1PDm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.save(\"topic_model\",serialization='safetensors')"
      ],
      "metadata": {
        "id": "qMialhQ_1Wnp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "861f9d084c7d4f53873ddf705e305721",
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            "450b8697af3f48a0b9d27650806ca3c9",
            "6b8aa9e2747846dbab94d85a8098cbbe",
            "4b128e326030461b87f49c786459daef",
            "0827ff40995444269e227a9b3c079d1c",
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            "e5d720c0015246e58f53cb4762d3383a",
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            "96588ece1acb462289638312104d83b8",
            "31e9a9e4d03345b6bd325aaa666c0a42",
            "51d3f771b2a0481d968c7266bfce1ff9",
            "b5ad250d4cad4719b8d9c60591a702a6",
            "669aa064c01b4019921e4ce16a52500f",
            "7f071337b6d146bb881d088a5582d2eb",
            "dfea0ffe7a0b47c3b8a9472df96a66eb",
            "de83ee00bbcb4315b8225945c9e6ea95",
            "81fd0cd7dbba4fd4a0969b8aca149a02",
            "7c8984ac1089413eadc9a0802021a25b",
            "10bfca0f897741c9b453248ef2f87d34",
            "161a713d3fb0474583db61f48484182e",
            "fcf4d226fa1e4092b508057a1f110029",
            "996df4a841af4657afc0f4b5dad73478",
            "9b5357ee7a03443f98d4b007c4ea608d",
            "130adc1e54724c88aad81bbac5689752",
            "b52f3ce008eb4cbb9f7b414db46f915b",
            "0754f962383f4fceb92b4220c14fdbc2",
            "9404fce948e243ffbd713f8444e99445",
            "3bdbf8d044d54ef6b47c68bab16f0bf6"
          ]
        },
        "id": "hNQMIj6s2EGV",
        "outputId": "6ac3502c-b783-4fdc-bf09-ebf588b3f77d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "861f9d084c7d4f53873ddf705e305721"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embed_model = 'sentence-transformers/all-MiniLM-L6-v2'"
      ],
      "metadata": {
        "id": "a96THGNu2sIO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.push_to_hf_hub(\n",
        "    repo_id='Kamaljp/topic_docs5000',\n",
        "    save_ctfidf=True,\n",
        "    save_embedding_model=embed_model,\n",
        "    serialization='pytorch'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "referenced_widgets": [
            "0ffc6683d3c44c18b8ede4f250e59d6a",
            "821a6df630e14d13a5c822c4abc8de0d",
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            "f8208d85a51c4c0daf67177ced12e959",
            "0cfdfac963804bedac9563ab30ba6724",
            "6e09cf9381be48f293f5f089b6d499c3",
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            "a68a29e399614f76962e06b96afa3180",
            "4c1c40db27e545599b3cc4b98f4b5c64",
            "d27b7a88b11e449cbc74cfd32a28c42f",
            "d365a118c2bd49e9976691b32bf86ded",
            "8ba0c8b12ec24817b395eb163878c9e5",
            "e29a60c25db94c1881861afe17c6c2de",
            "0ae1f2480fa142ed8fa6d1811c2dcdc9",
            "0afbf058ac504269ab85b2b3681922e8",
            "9998cc4bf88f467ebdd953dcccbf1d36",
            "14f309dfb4e44b1e968213109284a8fd",
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            "4288fec2f03e461bbc82ca22be23b928",
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            "14d0222d52644156a0aaa2727f24c973",
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            "8f1cfc98fc2c4163bdf2f2484752ac1a",
            "281e2697c5ab4cb88cb0808aa996656e"
          ]
        },
        "id": "U0gC_PLt2ECX",
        "outputId": "de46e4d7-1f39-464a-b930-1eb070dffe08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
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          "data": {
            "text/plain": [
              "'https://huggingface.co/Kamaljp/topic_docs5000/tree/main/'"
            ],
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              "type": "string"
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          },
          "metadata": {},
          "execution_count": 29
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    {
      "cell_type": "code",
      "source": [
        "topics_ = topic_model.topics_"
      ],
      "metadata": {
        "id": "qe3LBfCA2D_P"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_sizes = topic_model.topic_sizes_"
      ],
      "metadata": {
        "id": "eRQQeh9a6jIm"
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      "outputs": []
    },
    {
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        "topic_sizes"
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        "outputId": "0418b75f-8fc3-42d5-b825-cd653e1c4b6d"
      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "Counter({0: 450,\n",
              "         1: 344,\n",
              "         3: 220,\n",
              "         -1: 1606,\n",
              "         6: 172,\n",
              "         14: 100,\n",
              "         9: 169,\n",
              "         2: 246,\n",
              "         13: 100,\n",
              "         5: 186,\n",
              "         10: 146,\n",
              "         17: 56,\n",
              "         4: 203,\n",
              "         23: 21,\n",
              "         20: 28,\n",
              "         25: 16,\n",
              "         15: 94,\n",
              "         11: 132,\n",
              "         8: 169,\n",
              "         12: 105,\n",
              "         21: 27,\n",
              "         7: 172,\n",
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              "         18: 45,\n",
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              "         19: 44,\n",
              "         16: 66,\n",
              "         26: 15,\n",
              "         22: 26,\n",
              "         28: 12})"
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "topic_reps = topic_model.topic_representations_\n",
        "topic_reps"
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      "metadata": {
        "id": "oMwITa7a6i_0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_ctfidf = topic_model.c_tf_idf_\n",
        "topic_ctfidf"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ys3W7lat6i6y",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<30x62308 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 137936 stored elements in Compressed Sparse Row format>"
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          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_embedding = topic_model.topic_embeddings_\n",
        "topic_embedding"
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        "id": "EbvLPWtq2D5O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_distr, _ = topic_model.approximate_distribution(docs[:5000])"
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        },
        "id": "0EcDhoJDOD0t",
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      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5/5 [00:12<00:00,  2.48s/it]\n"
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        }
      ]
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    {
      "cell_type": "code",
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        "outputId": "22d7971d-741c-43f3-c546-c69e2454e49d"
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      "outputs": [
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        "topic_distr, topic_token_distr = topic_model.approximate_distribution(docs, calculate_tokens=True)\n",
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        "# Visualize the token-level distributions\n",
        "df = topic_model.visualize_approximate_distribution(docs[1], topic_token_distr[1])\n",
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              "  background-color: #f7fbff;\n",
              "  color: #000000;\n",
              "  color: white;\n",
              "  background-color: white;\n",
              "}\n",
              "#T_d71cb_row0_col6 {\n",
              "  background-color: #d8e7f5;\n",
              "  color: #000000;\n",
              "  color: black;\n",
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              "#T_d71cb_row0_col7 {\n",
              "  background-color: #89bedc;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col8 {\n",
              "  background-color: #2979b9;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col9, #T_d71cb_row0_col10 {\n",
              "  background-color: #08306b;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col11 {\n",
              "  background-color: #2575b7;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col12 {\n",
              "  background-color: #85bcdc;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col13 {\n",
              "  background-color: #d7e6f5;\n",
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              "  color: black;\n",
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              "#T_d71cb_row0_col18 {\n",
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              "  background-color: #e3eef9;\n",
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              "  background-color: #e3eef8;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col39 {\n",
              "  background-color: #b0d2e7;\n",
              "  color: #000000;\n",
              "  color: black;\n",
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              "#T_d71cb_row0_col40 {\n",
              "  background-color: #5da5d1;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col41 {\n",
              "  background-color: #1e6db2;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col42 {\n",
              "  background-color: #084d96;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col43 {\n",
              "  background-color: #3585bf;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row0_col44, #T_d71cb_row4_col44 {\n",
              "  background-color: #7fb9da;\n",
              "  color: #000000;\n",
              "  color: black;\n",
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              "#T_d71cb_row0_col45, #T_d71cb_row2_col46 {\n",
              "  background-color: #cadef0;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row1_col8, #T_d71cb_row1_col9, #T_d71cb_row1_col10, #T_d71cb_row1_col11, #T_d71cb_row1_col18, #T_d71cb_row1_col19, #T_d71cb_row1_col20, #T_d71cb_row1_col21, #T_d71cb_row2_col42, #T_d71cb_row4_col49 {\n",
              "  background-color: #e1edf8;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row2_col43 {\n",
              "  background-color: #c9ddf0;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row2_col44, #T_d71cb_row2_col45 {\n",
              "  background-color: #aacfe5;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row3_col18, #T_d71cb_row3_col19, #T_d71cb_row3_col20, #T_d71cb_row3_col21, #T_d71cb_row9_col43, #T_d71cb_row9_col44, #T_d71cb_row9_col45, #T_d71cb_row9_col46 {\n",
              "  background-color: #deebf7;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row4_col42, #T_d71cb_row6_col15, #T_d71cb_row6_col16, #T_d71cb_row6_col17, #T_d71cb_row6_col18, #T_d71cb_row7_col18, #T_d71cb_row7_col19, #T_d71cb_row7_col20, #T_d71cb_row7_col21, #T_d71cb_row9_col18, #T_d71cb_row9_col19, #T_d71cb_row9_col20, #T_d71cb_row9_col21 {\n",
              "  background-color: #e2edf8;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row4_col43 {\n",
              "  background-color: #bad6eb;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row4_col45 {\n",
              "  background-color: #4493c7;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row4_col46 {\n",
              "  background-color: #4292c6;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row4_col47 {\n",
              "  background-color: #84bcdb;\n",
              "  color: #000000;\n",
              "  color: black;\n",
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              "#T_d71cb_row4_col48 {\n",
              "  background-color: #bed8ec;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col12 {\n",
              "  background-color: #cddff1;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col13 {\n",
              "  background-color: #7ab6d9;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col14 {\n",
              "  background-color: #3383be;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col15 {\n",
              "  background-color: #08519c;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col16 {\n",
              "  background-color: #3989c1;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col17 {\n",
              "  background-color: #8fc2de;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col18 {\n",
              "  background-color: #d1e2f3;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col26 {\n",
              "  background-color: #d3e3f3;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col27 {\n",
              "  background-color: #a0cbe2;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col28 {\n",
              "  background-color: #66abd4;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col29 {\n",
              "  background-color: #3f8fc5;\n",
              "  color: #f1f1f1;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col30 {\n",
              "  background-color: #81badb;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col31 {\n",
              "  background-color: #c1d9ed;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "#T_d71cb_row8_col32 {\n",
              "  background-color: #dfecf7;\n",
              "  color: #000000;\n",
              "  color: black;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d71cb\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d71cb_level0_col0\" class=\"col_heading level0 col0\" >My</th>\n",
              "      <th id=\"T_d71cb_level0_col1\" class=\"col_heading level0 col1\" >brother </th>\n",
              "      <th id=\"T_d71cb_level0_col2\" class=\"col_heading level0 col2\" >is  </th>\n",
              "      <th id=\"T_d71cb_level0_col3\" class=\"col_heading level0 col3\" >in   </th>\n",
              "      <th id=\"T_d71cb_level0_col4\" class=\"col_heading level0 col4\" >the    </th>\n",
              "      <th id=\"T_d71cb_level0_col5\" class=\"col_heading level0 col5\" >market     </th>\n",
              "      <th id=\"T_d71cb_level0_col6\" class=\"col_heading level0 col6\" >for      </th>\n",
              "      <th id=\"T_d71cb_level0_col7\" class=\"col_heading level0 col7\" >high       </th>\n",
              "      <th id=\"T_d71cb_level0_col8\" class=\"col_heading level0 col8\" >performance        </th>\n",
              "      <th id=\"T_d71cb_level0_col9\" class=\"col_heading level0 col9\" >video         </th>\n",
              "      <th id=\"T_d71cb_level0_col10\" class=\"col_heading level0 col10\" >card          </th>\n",
              "      <th id=\"T_d71cb_level0_col11\" class=\"col_heading level0 col11\" >that           </th>\n",
              "      <th id=\"T_d71cb_level0_col12\" class=\"col_heading level0 col12\" >supports            </th>\n",
              "      <th id=\"T_d71cb_level0_col13\" class=\"col_heading level0 col13\" >VESA             </th>\n",
              "      <th id=\"T_d71cb_level0_col14\" class=\"col_heading level0 col14\" >local              </th>\n",
              "      <th id=\"T_d71cb_level0_col15\" class=\"col_heading level0 col15\" >bus               </th>\n",
              "      <th id=\"T_d71cb_level0_col16\" class=\"col_heading level0 col16\" >with                </th>\n",
              "      <th id=\"T_d71cb_level0_col17\" class=\"col_heading level0 col17\" >2MB                 </th>\n",
              "      <th id=\"T_d71cb_level0_col18\" class=\"col_heading level0 col18\" >RAM                  </th>\n",
              "      <th id=\"T_d71cb_level0_col19\" class=\"col_heading level0 col19\" >Does                   </th>\n",
              "      <th id=\"T_d71cb_level0_col20\" class=\"col_heading level0 col20\" >anyone                    </th>\n",
              "      <th id=\"T_d71cb_level0_col21\" class=\"col_heading level0 col21\" >have                     </th>\n",
              "      <th id=\"T_d71cb_level0_col22\" class=\"col_heading level0 col22\" >suggestions                      </th>\n",
              "      <th id=\"T_d71cb_level0_col23\" class=\"col_heading level0 col23\" >ideas                       </th>\n",
              "      <th id=\"T_d71cb_level0_col24\" class=\"col_heading level0 col24\" >on                        </th>\n",
              "      <th id=\"T_d71cb_level0_col25\" class=\"col_heading level0 col25\" >Diamond                         </th>\n",
              "      <th id=\"T_d71cb_level0_col26\" class=\"col_heading level0 col26\" >Stealth                          </th>\n",
              "      <th id=\"T_d71cb_level0_col27\" class=\"col_heading level0 col27\" >Pro                           </th>\n",
              "      <th id=\"T_d71cb_level0_col28\" class=\"col_heading level0 col28\" >Local                            </th>\n",
              "      <th id=\"T_d71cb_level0_col29\" class=\"col_heading level0 col29\" >Bus                             </th>\n",
              "      <th id=\"T_d71cb_level0_col30\" class=\"col_heading level0 col30\" >Orchid                              </th>\n",
              "      <th id=\"T_d71cb_level0_col31\" class=\"col_heading level0 col31\" >Farenheit                               </th>\n",
              "      <th id=\"T_d71cb_level0_col32\" class=\"col_heading level0 col32\" >1280                                </th>\n",
              "      <th id=\"T_d71cb_level0_col33\" class=\"col_heading level0 col33\" >ATI                                 </th>\n",
              "      <th id=\"T_d71cb_level0_col34\" class=\"col_heading level0 col34\" >Graphics                                  </th>\n",
              "      <th id=\"T_d71cb_level0_col35\" class=\"col_heading level0 col35\" >Ultra                                   </th>\n",
              "      <th id=\"T_d71cb_level0_col36\" class=\"col_heading level0 col36\" >Pro                                    </th>\n",
              "      <th id=\"T_d71cb_level0_col37\" class=\"col_heading level0 col37\" >Any                                     </th>\n",
              "      <th id=\"T_d71cb_level0_col38\" class=\"col_heading level0 col38\" >other                                      </th>\n",
              "      <th id=\"T_d71cb_level0_col39\" class=\"col_heading level0 col39\" >high                                       </th>\n",
              "      <th id=\"T_d71cb_level0_col40\" class=\"col_heading level0 col40\" >performance                                        </th>\n",
              "      <th id=\"T_d71cb_level0_col41\" class=\"col_heading level0 col41\" >VLB                                         </th>\n",
              "      <th id=\"T_d71cb_level0_col42\" class=\"col_heading level0 col42\" >card                                          </th>\n",
              "      <th id=\"T_d71cb_level0_col43\" class=\"col_heading level0 col43\" >Please                                           </th>\n",
              "      <th id=\"T_d71cb_level0_col44\" class=\"col_heading level0 col44\" >post                                            </th>\n",
              "      <th id=\"T_d71cb_level0_col45\" class=\"col_heading level0 col45\" >or                                             </th>\n",
              "      <th id=\"T_d71cb_level0_col46\" class=\"col_heading level0 col46\" >email                                              </th>\n",
              "      <th id=\"T_d71cb_level0_col47\" class=\"col_heading level0 col47\" >Thank                                               </th>\n",
              "      <th id=\"T_d71cb_level0_col48\" class=\"col_heading level0 col48\" >you                                                </th>\n",
              "      <th id=\"T_d71cb_level0_col49\" class=\"col_heading level0 col49\" >Matt                                                 </th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row0\" class=\"row_heading level0 row0\" >5_card_monitor_video_drivers</th>\n",
              "      <td id=\"T_d71cb_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col1\" class=\"data row0 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col2\" class=\"data row0 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col3\" class=\"data row0 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col4\" class=\"data row0 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col5\" class=\"data row0 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col6\" class=\"data row0 col6\" >0.161</td>\n",
              "      <td id=\"T_d71cb_row0_col7\" class=\"data row0 col7\" >0.433</td>\n",
              "      <td id=\"T_d71cb_row0_col8\" class=\"data row0 col8\" >0.733</td>\n",
              "      <td id=\"T_d71cb_row0_col9\" class=\"data row0 col9\" >1.012</td>\n",
              "      <td id=\"T_d71cb_row0_col10\" class=\"data row0 col10\" >1.015</td>\n",
              "      <td id=\"T_d71cb_row0_col11\" class=\"data row0 col11\" >0.743</td>\n",
              "      <td id=\"T_d71cb_row0_col12\" class=\"data row0 col12\" >0.443</td>\n",
              "      <td id=\"T_d71cb_row0_col13\" class=\"data row0 col13\" >0.164</td>\n",
              "      <td id=\"T_d71cb_row0_col14\" class=\"data row0 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col15\" class=\"data row0 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col16\" class=\"data row0 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col17\" class=\"data row0 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col18\" class=\"data row0 col18\" >0.123</td>\n",
              "      <td id=\"T_d71cb_row0_col19\" class=\"data row0 col19\" >0.225</td>\n",
              "      <td id=\"T_d71cb_row0_col20\" class=\"data row0 col20\" >0.225</td>\n",
              "      <td id=\"T_d71cb_row0_col21\" class=\"data row0 col21\" >0.225</td>\n",
              "      <td id=\"T_d71cb_row0_col22\" class=\"data row0 col22\" >0.102</td>\n",
              "      <td id=\"T_d71cb_row0_col23\" class=\"data row0 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col24\" class=\"data row0 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col25\" class=\"data row0 col25\" >0.105</td>\n",
              "      <td id=\"T_d71cb_row0_col26\" class=\"data row0 col26\" >0.105</td>\n",
              "      <td id=\"T_d71cb_row0_col27\" class=\"data row0 col27\" >0.105</td>\n",
              "      <td id=\"T_d71cb_row0_col28\" class=\"data row0 col28\" >0.105</td>\n",
              "      <td id=\"T_d71cb_row0_col29\" class=\"data row0 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col30\" class=\"data row0 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col31\" class=\"data row0 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col32\" class=\"data row0 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col33\" class=\"data row0 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col34\" class=\"data row0 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col35\" class=\"data row0 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col36\" class=\"data row0 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col37\" class=\"data row0 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col38\" class=\"data row0 col38\" >0.107</td>\n",
              "      <td id=\"T_d71cb_row0_col39\" class=\"data row0 col39\" >0.322</td>\n",
              "      <td id=\"T_d71cb_row0_col40\" class=\"data row0 col40\" >0.549</td>\n",
              "      <td id=\"T_d71cb_row0_col41\" class=\"data row0 col41\" >0.773</td>\n",
              "      <td id=\"T_d71cb_row0_col42\" class=\"data row0 col42\" >0.901</td>\n",
              "      <td id=\"T_d71cb_row0_col43\" class=\"data row0 col43\" >0.685</td>\n",
              "      <td id=\"T_d71cb_row0_col44\" class=\"data row0 col44\" >0.459</td>\n",
              "      <td id=\"T_d71cb_row0_col45\" class=\"data row0 col45\" >0.234</td>\n",
              "      <td id=\"T_d71cb_row0_col46\" class=\"data row0 col46\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col47\" class=\"data row0 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col48\" class=\"data row0 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row0_col49\" class=\"data row0 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row1\" class=\"row_heading level0 row1\" >9_mhz_cpu_speed_motherboard</th>\n",
              "      <td id=\"T_d71cb_row1_col0\" class=\"data row1 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col3\" class=\"data row1 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col4\" class=\"data row1 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col5\" class=\"data row1 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col6\" class=\"data row1 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col7\" class=\"data row1 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col8\" class=\"data row1 col8\" >0.114</td>\n",
              "      <td id=\"T_d71cb_row1_col9\" class=\"data row1 col9\" >0.114</td>\n",
              "      <td id=\"T_d71cb_row1_col10\" class=\"data row1 col10\" >0.114</td>\n",
              "      <td id=\"T_d71cb_row1_col11\" class=\"data row1 col11\" >0.114</td>\n",
              "      <td id=\"T_d71cb_row1_col12\" class=\"data row1 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col13\" class=\"data row1 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col14\" class=\"data row1 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col15\" class=\"data row1 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col16\" class=\"data row1 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col17\" class=\"data row1 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col18\" class=\"data row1 col18\" >0.115</td>\n",
              "      <td id=\"T_d71cb_row1_col19\" class=\"data row1 col19\" >0.115</td>\n",
              "      <td id=\"T_d71cb_row1_col20\" class=\"data row1 col20\" >0.115</td>\n",
              "      <td id=\"T_d71cb_row1_col21\" class=\"data row1 col21\" >0.115</td>\n",
              "      <td id=\"T_d71cb_row1_col22\" class=\"data row1 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col23\" class=\"data row1 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col24\" class=\"data row1 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col25\" class=\"data row1 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col26\" class=\"data row1 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col27\" class=\"data row1 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col28\" class=\"data row1 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col29\" class=\"data row1 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col30\" class=\"data row1 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col31\" class=\"data row1 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col32\" class=\"data row1 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col33\" class=\"data row1 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col34\" class=\"data row1 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col35\" class=\"data row1 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col36\" class=\"data row1 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col37\" class=\"data row1 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col38\" class=\"data row1 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col39\" class=\"data row1 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col40\" class=\"data row1 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col41\" class=\"data row1 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col42\" class=\"data row1 col42\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col43\" class=\"data row1 col43\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col44\" class=\"data row1 col44\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col45\" class=\"data row1 col45\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col46\" class=\"data row1 col46\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col47\" class=\"data row1 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col48\" class=\"data row1 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row1_col49\" class=\"data row1 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row2\" class=\"row_heading level0 row2\" >10_jim_you_context_your</th>\n",
              "      <td id=\"T_d71cb_row2_col0\" class=\"data row2 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col3\" class=\"data row2 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col4\" class=\"data row2 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col5\" class=\"data row2 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col9\" class=\"data row2 col9\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col10\" class=\"data row2 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col11\" class=\"data row2 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col12\" class=\"data row2 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col13\" class=\"data row2 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col14\" class=\"data row2 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col15\" class=\"data row2 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col16\" class=\"data row2 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col17\" class=\"data row2 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col18\" class=\"data row2 col18\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col19\" class=\"data row2 col19\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col20\" class=\"data row2 col20\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col21\" class=\"data row2 col21\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col22\" class=\"data row2 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col23\" class=\"data row2 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col24\" class=\"data row2 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col25\" class=\"data row2 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col26\" class=\"data row2 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col27\" class=\"data row2 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col28\" class=\"data row2 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col29\" class=\"data row2 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col30\" class=\"data row2 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col31\" class=\"data row2 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col32\" class=\"data row2 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col33\" class=\"data row2 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col34\" class=\"data row2 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col35\" class=\"data row2 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col36\" class=\"data row2 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col37\" class=\"data row2 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col38\" class=\"data row2 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col39\" class=\"data row2 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col40\" class=\"data row2 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col41\" class=\"data row2 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col42\" class=\"data row2 col42\" >0.114</td>\n",
              "      <td id=\"T_d71cb_row2_col43\" class=\"data row2 col43\" >0.242</td>\n",
              "      <td id=\"T_d71cb_row2_col44\" class=\"data row2 col44\" >0.345</td>\n",
              "      <td id=\"T_d71cb_row2_col45\" class=\"data row2 col45\" >0.345</td>\n",
              "      <td id=\"T_d71cb_row2_col46\" class=\"data row2 col46\" >0.230</td>\n",
              "      <td id=\"T_d71cb_row2_col47\" class=\"data row2 col47\" >0.103</td>\n",
              "      <td id=\"T_d71cb_row2_col48\" class=\"data row2 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row2_col49\" class=\"data row2 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row3\" class=\"row_heading level0 row3\" >16_windows_memory_problem_running</th>\n",
              "      <td id=\"T_d71cb_row3_col0\" class=\"data row3 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col1\" class=\"data row3 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col2\" class=\"data row3 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col4\" class=\"data row3 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col5\" class=\"data row3 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col6\" class=\"data row3 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col7\" class=\"data row3 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col8\" class=\"data row3 col8\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col9\" class=\"data row3 col9\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col10\" class=\"data row3 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col11\" class=\"data row3 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col12\" class=\"data row3 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col13\" class=\"data row3 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col14\" class=\"data row3 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col15\" class=\"data row3 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col16\" class=\"data row3 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col17\" class=\"data row3 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col18\" class=\"data row3 col18\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row3_col19\" class=\"data row3 col19\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row3_col20\" class=\"data row3 col20\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row3_col21\" class=\"data row3 col21\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row3_col22\" class=\"data row3 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col23\" class=\"data row3 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col24\" class=\"data row3 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col25\" class=\"data row3 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col26\" class=\"data row3 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col27\" class=\"data row3 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col28\" class=\"data row3 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col29\" class=\"data row3 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col30\" class=\"data row3 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col31\" class=\"data row3 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col32\" class=\"data row3 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col33\" class=\"data row3 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col34\" class=\"data row3 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col35\" class=\"data row3 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col36\" class=\"data row3 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col37\" class=\"data row3 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col38\" class=\"data row3 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col39\" class=\"data row3 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col40\" class=\"data row3 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col41\" class=\"data row3 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col42\" class=\"data row3 col42\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col43\" class=\"data row3 col43\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col44\" class=\"data row3 col44\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col45\" class=\"data row3 col45\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col46\" class=\"data row3 col46\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col47\" class=\"data row3 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col48\" class=\"data row3 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row3_col49\" class=\"data row3 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row4\" class=\"row_heading level0 row4\" >18_address_mail_me_email</th>\n",
              "      <td id=\"T_d71cb_row4_col0\" class=\"data row4 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col1\" class=\"data row4 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col2\" class=\"data row4 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col3\" class=\"data row4 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col5\" class=\"data row4 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col6\" class=\"data row4 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col7\" class=\"data row4 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col9\" class=\"data row4 col9\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col10\" class=\"data row4 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col11\" class=\"data row4 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col12\" class=\"data row4 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col13\" class=\"data row4 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col14\" class=\"data row4 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col15\" class=\"data row4 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col16\" class=\"data row4 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col17\" class=\"data row4 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col18\" class=\"data row4 col18\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col19\" class=\"data row4 col19\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col20\" class=\"data row4 col20\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col21\" class=\"data row4 col21\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col22\" class=\"data row4 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col23\" class=\"data row4 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col24\" class=\"data row4 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col25\" class=\"data row4 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col26\" class=\"data row4 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col27\" class=\"data row4 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col28\" class=\"data row4 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col29\" class=\"data row4 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col30\" class=\"data row4 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col31\" class=\"data row4 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col32\" class=\"data row4 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col33\" class=\"data row4 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col34\" class=\"data row4 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col35\" class=\"data row4 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col36\" class=\"data row4 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col37\" class=\"data row4 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col38\" class=\"data row4 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col39\" class=\"data row4 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col40\" class=\"data row4 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col41\" class=\"data row4 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row4_col42\" class=\"data row4 col42\" >0.108</td>\n",
              "      <td id=\"T_d71cb_row4_col43\" class=\"data row4 col43\" >0.293</td>\n",
              "      <td id=\"T_d71cb_row4_col44\" class=\"data row4 col44\" >0.459</td>\n",
              "      <td id=\"T_d71cb_row4_col45\" class=\"data row4 col45\" >0.627</td>\n",
              "      <td id=\"T_d71cb_row4_col46\" class=\"data row4 col46\" >0.632</td>\n",
              "      <td id=\"T_d71cb_row4_col47\" class=\"data row4 col47\" >0.447</td>\n",
              "      <td id=\"T_d71cb_row4_col48\" class=\"data row4 col48\" >0.281</td>\n",
              "      <td id=\"T_d71cb_row4_col49\" class=\"data row4 col49\" >0.113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row5\" class=\"row_heading level0 row5\" >28_shipping_bid_sell_cdtv</th>\n",
              "      <td id=\"T_d71cb_row5_col0\" class=\"data row5 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col1\" class=\"data row5 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col2\" class=\"data row5 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col3\" class=\"data row5 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col4\" class=\"data row5 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col6\" class=\"data row5 col6\" >0.102</td>\n",
              "      <td id=\"T_d71cb_row5_col7\" class=\"data row5 col7\" >0.102</td>\n",
              "      <td id=\"T_d71cb_row5_col8\" class=\"data row5 col8\" >0.102</td>\n",
              "      <td id=\"T_d71cb_row5_col9\" class=\"data row5 col9\" >0.102</td>\n",
              "      <td id=\"T_d71cb_row5_col10\" class=\"data row5 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col11\" class=\"data row5 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col12\" class=\"data row5 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col13\" class=\"data row5 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col14\" class=\"data row5 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col15\" class=\"data row5 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col16\" class=\"data row5 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col17\" class=\"data row5 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col18\" class=\"data row5 col18\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col19\" class=\"data row5 col19\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col20\" class=\"data row5 col20\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col21\" class=\"data row5 col21\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col22\" class=\"data row5 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col23\" class=\"data row5 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col24\" class=\"data row5 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col25\" class=\"data row5 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col26\" class=\"data row5 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col27\" class=\"data row5 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col28\" class=\"data row5 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col29\" class=\"data row5 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col30\" class=\"data row5 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col31\" class=\"data row5 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col32\" class=\"data row5 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col33\" class=\"data row5 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col34\" class=\"data row5 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col35\" class=\"data row5 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col36\" class=\"data row5 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col37\" class=\"data row5 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col38\" class=\"data row5 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col39\" class=\"data row5 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col40\" class=\"data row5 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col41\" class=\"data row5 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col42\" class=\"data row5 col42\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col43\" class=\"data row5 col43\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col44\" class=\"data row5 col44\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col45\" class=\"data row5 col45\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col46\" class=\"data row5 col46\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col47\" class=\"data row5 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col48\" class=\"data row5 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row5_col49\" class=\"data row5 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row6\" class=\"row_heading level0 row6\" >44_os_mac_ibm_32</th>\n",
              "      <td id=\"T_d71cb_row6_col0\" class=\"data row6 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col1\" class=\"data row6 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col3\" class=\"data row6 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col4\" class=\"data row6 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col5\" class=\"data row6 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col7\" class=\"data row6 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col9\" class=\"data row6 col9\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col10\" class=\"data row6 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col11\" class=\"data row6 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col12\" class=\"data row6 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col13\" class=\"data row6 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col14\" class=\"data row6 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col15\" class=\"data row6 col15\" >0.111</td>\n",
              "      <td id=\"T_d71cb_row6_col16\" class=\"data row6 col16\" >0.111</td>\n",
              "      <td id=\"T_d71cb_row6_col17\" class=\"data row6 col17\" >0.111</td>\n",
              "      <td id=\"T_d71cb_row6_col18\" class=\"data row6 col18\" >0.111</td>\n",
              "      <td id=\"T_d71cb_row6_col19\" class=\"data row6 col19\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col20\" class=\"data row6 col20\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col21\" class=\"data row6 col21\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col22\" class=\"data row6 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col23\" class=\"data row6 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col24\" class=\"data row6 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col25\" class=\"data row6 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col26\" class=\"data row6 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col27\" class=\"data row6 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col28\" class=\"data row6 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col29\" class=\"data row6 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col30\" class=\"data row6 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col31\" class=\"data row6 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col32\" class=\"data row6 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col33\" class=\"data row6 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col34\" class=\"data row6 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col35\" class=\"data row6 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col36\" class=\"data row6 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col37\" class=\"data row6 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col38\" class=\"data row6 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col39\" class=\"data row6 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col40\" class=\"data row6 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col41\" class=\"data row6 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col42\" class=\"data row6 col42\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col43\" class=\"data row6 col43\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col44\" class=\"data row6 col44\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col45\" class=\"data row6 col45\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col46\" class=\"data row6 col46\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col47\" class=\"data row6 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col48\" class=\"data row6 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row6_col49\" class=\"data row6 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row7\" class=\"row_heading level0 row7\" >56_simms_jh_memory_70ns</th>\n",
              "      <td id=\"T_d71cb_row7_col0\" class=\"data row7 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col1\" class=\"data row7 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col3\" class=\"data row7 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col4\" class=\"data row7 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col5\" class=\"data row7 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col6\" class=\"data row7 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col8\" class=\"data row7 col8\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col9\" class=\"data row7 col9\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col10\" class=\"data row7 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col11\" class=\"data row7 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col12\" class=\"data row7 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col13\" class=\"data row7 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col14\" class=\"data row7 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col15\" class=\"data row7 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col16\" class=\"data row7 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col17\" class=\"data row7 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col18\" class=\"data row7 col18\" >0.110</td>\n",
              "      <td id=\"T_d71cb_row7_col19\" class=\"data row7 col19\" >0.110</td>\n",
              "      <td id=\"T_d71cb_row7_col20\" class=\"data row7 col20\" >0.110</td>\n",
              "      <td id=\"T_d71cb_row7_col21\" class=\"data row7 col21\" >0.110</td>\n",
              "      <td id=\"T_d71cb_row7_col22\" class=\"data row7 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col23\" class=\"data row7 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col24\" class=\"data row7 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col25\" class=\"data row7 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col26\" class=\"data row7 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col27\" class=\"data row7 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col28\" class=\"data row7 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col29\" class=\"data row7 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col30\" class=\"data row7 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col31\" class=\"data row7 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col32\" class=\"data row7 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col33\" class=\"data row7 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col34\" class=\"data row7 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col35\" class=\"data row7 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col36\" class=\"data row7 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col37\" class=\"data row7 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col38\" class=\"data row7 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col39\" class=\"data row7 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col40\" class=\"data row7 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col41\" class=\"data row7 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col42\" class=\"data row7 col42\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col43\" class=\"data row7 col43\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col44\" class=\"data row7 col44\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col45\" class=\"data row7 col45\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col46\" class=\"data row7 col46\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col47\" class=\"data row7 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col48\" class=\"data row7 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row7_col49\" class=\"data row7 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row8\" class=\"row_heading level0 row8\" >67_isa_bus_eisa_ide</th>\n",
              "      <td id=\"T_d71cb_row8_col0\" class=\"data row8 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col1\" class=\"data row8 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col3\" class=\"data row8 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col7\" class=\"data row8 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col9\" class=\"data row8 col9\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col10\" class=\"data row8 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col11\" class=\"data row8 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col12\" class=\"data row8 col12\" >0.219</td>\n",
              "      <td id=\"T_d71cb_row8_col13\" class=\"data row8 col13\" >0.470</td>\n",
              "      <td id=\"T_d71cb_row8_col14\" class=\"data row8 col14\" >0.691</td>\n",
              "      <td id=\"T_d71cb_row8_col15\" class=\"data row8 col15\" >0.887</td>\n",
              "      <td id=\"T_d71cb_row8_col16\" class=\"data row8 col16\" >0.668</td>\n",
              "      <td id=\"T_d71cb_row8_col17\" class=\"data row8 col17\" >0.417</td>\n",
              "      <td id=\"T_d71cb_row8_col18\" class=\"data row8 col18\" >0.196</td>\n",
              "      <td id=\"T_d71cb_row8_col19\" class=\"data row8 col19\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col20\" class=\"data row8 col20\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col21\" class=\"data row8 col21\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col22\" class=\"data row8 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col23\" class=\"data row8 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col24\" class=\"data row8 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col25\" class=\"data row8 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col26\" class=\"data row8 col26\" >0.190</td>\n",
              "      <td id=\"T_d71cb_row8_col27\" class=\"data row8 col27\" >0.374</td>\n",
              "      <td id=\"T_d71cb_row8_col28\" class=\"data row8 col28\" >0.523</td>\n",
              "      <td id=\"T_d71cb_row8_col29\" class=\"data row8 col29\" >0.645</td>\n",
              "      <td id=\"T_d71cb_row8_col30\" class=\"data row8 col30\" >0.455</td>\n",
              "      <td id=\"T_d71cb_row8_col31\" class=\"data row8 col31\" >0.271</td>\n",
              "      <td id=\"T_d71cb_row8_col32\" class=\"data row8 col32\" >0.122</td>\n",
              "      <td id=\"T_d71cb_row8_col33\" class=\"data row8 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col34\" class=\"data row8 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col35\" class=\"data row8 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col36\" class=\"data row8 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col37\" class=\"data row8 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col38\" class=\"data row8 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col39\" class=\"data row8 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col40\" class=\"data row8 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col41\" class=\"data row8 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col42\" class=\"data row8 col42\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col43\" class=\"data row8 col43\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col44\" class=\"data row8 col44\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col45\" class=\"data row8 col45\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col46\" class=\"data row8 col46\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col47\" class=\"data row8 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col48\" class=\"data row8 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row8_col49\" class=\"data row8 col49\" >0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d71cb_level0_row9\" class=\"row_heading level0 row9\" >70_anyone_itll_dean_place</th>\n",
              "      <td id=\"T_d71cb_row9_col0\" class=\"data row9 col0\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col1\" class=\"data row9 col1\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col2\" class=\"data row9 col2\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col3\" class=\"data row9 col3\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col4\" class=\"data row9 col4\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col5\" class=\"data row9 col5\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col6\" class=\"data row9 col6\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col7\" class=\"data row9 col7\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col8\" class=\"data row9 col8\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col9\" class=\"data row9 col9\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col10\" class=\"data row9 col10\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col11\" class=\"data row9 col11\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col12\" class=\"data row9 col12\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col13\" class=\"data row9 col13\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col14\" class=\"data row9 col14\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col15\" class=\"data row9 col15\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col16\" class=\"data row9 col16\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col17\" class=\"data row9 col17\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col18\" class=\"data row9 col18\" >0.108</td>\n",
              "      <td id=\"T_d71cb_row9_col19\" class=\"data row9 col19\" >0.108</td>\n",
              "      <td id=\"T_d71cb_row9_col20\" class=\"data row9 col20\" >0.108</td>\n",
              "      <td id=\"T_d71cb_row9_col21\" class=\"data row9 col21\" >0.108</td>\n",
              "      <td id=\"T_d71cb_row9_col22\" class=\"data row9 col22\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col23\" class=\"data row9 col23\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col24\" class=\"data row9 col24\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col25\" class=\"data row9 col25\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col26\" class=\"data row9 col26\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col27\" class=\"data row9 col27\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col28\" class=\"data row9 col28\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col29\" class=\"data row9 col29\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col30\" class=\"data row9 col30\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col31\" class=\"data row9 col31\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col32\" class=\"data row9 col32\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col33\" class=\"data row9 col33\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col34\" class=\"data row9 col34\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col35\" class=\"data row9 col35\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col36\" class=\"data row9 col36\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col37\" class=\"data row9 col37\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col38\" class=\"data row9 col38\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col39\" class=\"data row9 col39\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col40\" class=\"data row9 col40\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col41\" class=\"data row9 col41\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col42\" class=\"data row9 col42\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col43\" class=\"data row9 col43\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row9_col44\" class=\"data row9 col44\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row9_col45\" class=\"data row9 col45\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row9_col46\" class=\"data row9 col46\" >0.128</td>\n",
              "      <td id=\"T_d71cb_row9_col47\" class=\"data row9 col47\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col48\" class=\"data row9 col48\" >0.000</td>\n",
              "      <td id=\"T_d71cb_row9_col49\" class=\"data row9 col49\" >0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Online Topic Modeling\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "E4t3g_vnUqY8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc_chunks = [docs[5000:i+1000] for i in range(5000, len(docs), 1000)]"
      ],
      "metadata": {
        "id": "469lFnMkOVYg"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_chunks[2]"
      ],
      "metadata": {
        "id": "2hgBX2-zQlyx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import MiniBatchKMeans\n",
        "from sklearn.decomposition import IncrementalPCA\n",
        "from bertopic.vectorizers import OnlineCountVectorizer\n",
        "\n",
        "# Prepare sub-models that support online learning\n",
        "umap_model = IncrementalPCA(n_components=5)\n",
        "cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)\n",
        "vectorizer_model = OnlineCountVectorizer(stop_words=\"english\", decay=.01)"
      ],
      "metadata": {
        "id": "tGHtwU9NOVTp"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from bertopic import BERTopic\n",
        "\n",
        "topic_model_ol = BERTopic(umap_model=umap_model,\n",
        "                       hdbscan_model=cluster_model,\n",
        "                       vectorizer_model=vectorizer_model)\n",
        "\n",
        "# Incrementally fit the topic model by training on 1000 documents at a time\n",
        "for docs in doc_chunks:\n",
        "    topic_model_ol.partial_fit(docs)"
      ],
      "metadata": {
        "id": "dXQo6j-xOVOo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Semi Supervised"
      ],
      "metadata": {
        "id": "ZowmIsM2UtfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))\n",
        "\n",
        "docs = data[\"data\"]\n",
        "categories = data[\"target\"]\n",
        "category_names = data[\"target_names\"]"
      ],
      "metadata": {
        "id": "Pk7WZsapOVJx"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Initiating the topic modeling with categories\n",
        "topic_model_ss = BERTopic(verbose=True).fit(docs[:100], y=category_names)"
      ],
      "metadata": {
        "id": "OlBScAxdVCl9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dZAPiInFVCgS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}