ML_project_greenwashing_EDA.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZGD6SMHY6JJz"
      },
      "outputs": [],
      "source": [
        "# Import standard libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Import for text analytics\n",
        "import spacy\n",
        "from spacy import displacy\n",
        "import string\n",
        "from collections import Counter\n",
        "\n",
        "\n",
        "# Import for text analytics\n",
        "import spacy\n",
        "from spacy import displacy\n",
        "import string\n",
        "\n",
        "# Load English language model of spacy\n",
        "sp = spacy.load('en_core_web_sm')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "RtnVrPaTqHXb"
      },
      "source": [
        "# Final Project Machine Learning (MGT-502) - Corporate Greenwashing"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "o07nrdoyqTJc"
      },
      "source": [
        "Contributors:\n",
        "\n",
        "*   Noé Lopez\n",
        "*   Guillaume Rico\n",
        "*   Charlotte Ahrens\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "4mTqbuG16ObS"
      },
      "source": [
        "## Prelimanary Analysis"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data explanation \n",
        "The dataset from the ETH Zürich research department is publicly available on [huggingface](https://huggingface.co/datasets/climatebert/environmental_claims/tree/main) to stimulate continuous research and collaboration to advance on sustainability actions globally. The dataset contains 3000 human-annotated environmental claims. Within the dataset, 16 domain experts labelled a claim to be environmentally based on the definition of an environmental claim, mentioned above. Important to note is that the dataset solely focuses on claims of a length between 10 to 40 words. In addition, all the claims in the dataset have been made in sustainability report, earning calls, and annual reports. Lastly, it is important to keep in mind that the dataset, might be biased, since it was mainly trained on financial claim statements. Therefore, when exploring Greenwashing in another sector, the accuracy of the model can not be guaranteed.\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "1Hk47yXz5FCa"
      },
      "source": [
        "### Import data set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "buoSufuM4_nm"
      },
      "outputs": [],
      "source": [
        "# Import training dataset\n",
        "url_train = \"https://huggingface.co/datasets/climatebert/environmental_claims/raw/main/data/train.jsonl\"\n",
        "df_train = pd.read_json(url_train, lines=True)\n",
        "#df_train\n",
        "\n",
        "# Import testing dataset\n",
        "url_test = \"https://huggingface.co/datasets/climatebert/environmental_claims/raw/main/data/test.jsonl\"\n",
        "df_test = pd.read_json(url_test, lines=True)\n",
        "#df_test"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "5KCoTkV-5JUZ"
      },
      "source": [
        "### Dataset Discovery"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "import spacy\n",
        "import unidecode\n",
        "sp = spacy.load(\"en_core_web_sm\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>It will enable E.ON to acquire and leverage a ...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>And that's honestly most markets in the U.S. S...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A key pillar of our strategy is to continue to...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The goal to reduce relative GHG emissions by 2...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>As you know, we drove about $75 million of cos...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>2395</th>\n",
              "      <td>The collaboration will enable Nestlé to delive...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2396</th>\n",
              "      <td>Through the MOBILITY THAT ADAPTS TO MEET NEEDS...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>While we are making progress towards our near ...</td>\n",
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              "      <td>On 5 December 2019, the Federal Network Agency...</td>\n",
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            "text/plain": [
              "                                                   text  label\n",
              "0     It will enable E.ON to acquire and leverage a ...      0\n",
              "1     And that's honestly most markets in the U.S. S...      0\n",
              "2     A key pillar of our strategy is to continue to...      0\n",
              "3     The goal to reduce relative GHG emissions by 2...      1\n",
              "4     As you know, we drove about $75 million of cos...      0\n",
              "...                                                 ...    ...\n",
              "2395  The collaboration will enable Nestlé to delive...      1\n",
              "2396  Through the MOBILITY THAT ADAPTS TO MEET NEEDS...      0\n",
              "2397  While we are making progress towards our near ...      0\n",
              "2398  On 5 December 2019, the Federal Network Agency...      0\n",
              "2399  Emission reductions are subtracted from gross ...      0\n",
              "\n",
              "[2400 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(df_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "ZZZVFih24_p2",
        "outputId": "1a8e6f0a-b910-40a8-c0af-b8084f23a4d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Information on the training dataset:\n",
            "---------------------------------------------\n",
            "\n",
            "There are 2400 observations in the training dataset.\n",
            "\n",
            "\n",
            "There are 1858 observations that are not related to the environment (label = 0)\n",
            "There are 542 observations that are environmental claims (label = 1).\n",
            "The ratio is 29.17%.\n"
          ]
        },
        {
          "data": {
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Z6+HhoR49eqhHjx4qKCjQo48+qrfeektPP/206tWrJ0ny9/fXAw88oAceeECnT59Wu3btlJKSQiCqxJgyg6XUrVtX9913n9566y1zLUQhHx8fBQQEFFn78Prrr1+1et577z2dOnXK3P7000915MgRJSQkSJJiYmJUt25dTZkyRadPny7y/L/ean0lunXrpo0bNyotLc1sy8nJ0YwZMxQZGVmiKYtu3bopPz9fr732mlP7Sy+9JJvNZp5XobS0NKf1HQcPHtTnn3+uuLg4ubu7Kz8/v8g0VFBQkMLCwszpn9J8jXr16iV3d3d99NFHmj9/vm655RZVr17d7C+8MvPnq32GYTjdsl2o8Hl/Ddh/5e7urri4OH3++efmdJR0/i7ADz/8UG3atHGasrscx48fL3JFsjBcXM602eHDh/XZZ5+Z2w6HQ++9956aN2+ukJAQSeev3KWlpWnp0qVFnn/ixAnl5eVdUc2FevbsKTc3N02YMKHIFbeLXWUt7nuzYcMGp/e3JB09etRp283NzfwPSOFr89cxNWrUUL169S57yhEVE1eIYDn/+te/9P7772vnzp1q3LixU9/AgQM1ceJEDRw4UC1bttTatWvN/xlfDf7+/mrTpo0eeOABZWVladq0aapXr54GDRok6fw/1u+8844SEhLUuHFjPfDAA/rHP/6hQ4cOafXq1fLx8dEXX3xRomOPHj1aH330kRISEjR8+HD5+/trzpw52rt3r/79738XWdB6OXr06KGOHTvqX//6l/bt26dmzZpp2bJl+vzzz/X444+bV3MKXXfddYqPj3e67V46f9u1dP4ziK655hrdeeedatasmWrUqKEVK1Zo06ZN5mc7leZrFBQUpI4dO2rq1Kk6deqU7rnnHqf+Ro0aqW7dunryySd16NAh+fj46N///nex68MKF4sPHz5c8fHxcnd317333lvscZ999lktX75cbdq00aOPPqoqVarorbfeUm5uriZNmnRZtf/ZnDlz9Prrr+v2229X3bp1derUKb399tvy8fFRt27dLvn8Bg0aaMCAAdq0aZOCg4M1c+ZMZWVladasWeaYUaNG6T//+Y9uueUW9e/fXzExMcrJydEPP/ygTz/9VPv27Suylu5y1KtXT//617/0zDPPqG3btrrjjjtkt9u1adMmhYWFKTU1tdjn3XLLLVqwYIFuv/12de/eXXv37tWbb76p6Ohop6A8cOBAHTt2TDfffLOuueYa7d+/X6+++qqaN29urnWLjo5Whw4dFBMTI39/f23evNn86AdUYi65tw0oA3++7f6vEhMTDUlOt90bxvlbqgcMGGD4+voa3t7ext13321kZ2df8Lb7X3/9tch+q1evXuR4f73Fv/C2+48++shITk42goKCjGrVqhndu3d3unW70HfffWfccccdRq1atQy73W5EREQYd999t7Fy5cpL1nQxe/bsMe68807Dz8/P8PT0NFq1amUsWrSoyDhd5m33hnH+1v0RI0YYYWFhRtWqVY369esbkydPdrpl+s/7/OCDD4z69esbdrvdaNGihbF69WpzTG5urjFq1CijWbNmhre3t1G9enWjWbNmxuuvv17kuKX1Gr399tuGJMPb29v4/fffi/Tv2LHD6Ny5s1GjRg0jICDAGDRokPH9998X+ciGvLw8Y9iwYUZgYKBhs9mcbsH/6/vJMAxj69atRnx8vFGjRg3Dy8vL6Nixo7Fu3TqnMRd6Txe+nwpfu61btxq9e/c2ateubdjtdiMoKMi45ZZbnD7i4EIiIiKM7t27G0uXLjWaNm1q2O12o1GjRsb8+fOLjD116pSRnJxs1KtXz/Dw8DACAgKMm266yZgyZYpx9uxZwzD+77b7yZMnX/LYfzZz5kyjRYsWht1uN2rWrGm0b9/eWL58udn/19vuCwoKjOeff96IiIgw30uLFi0yEhMTjYiICHPcp59+asTFxRlBQUGGh4eHUbt2beOhhx4yjhw5Yo559tlnjVatWhl+fn5GtWrVjEaNGhnPPfeceU6onGyGUYKVngCASikyMlLXXXedFi1a5OpSgDLFGiIAAGB5BCIAAGB5BCIAAGB5rCECAACWxxUiAABgeQQiAABgeXww42UoKCjQ4cOH5e3tfdkfxw8AAFzLMAydOnVKYWFhl/ywWQLRZTh8+HCJ/5YQAABwrYMHD+qaa6656BgC0WUo/COVBw8evOK/KQQAAFzD4XAoPDzc6Y9NXwiB6DIUTpP5+PgQiAAAqGAuZ7kLi6oBAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlVXF1AZAiR3/p6hKAcmvfxO6uLgGABXCFCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWJ5LA9HatWvVo0cPhYWFyWazaeHChU79Nput2MfkyZPNMZGRkUX6J06c6LSfjIwMtW3bVp6engoPD9ekSZPK4vQAAEAF4dJAlJOTo2bNmmn69OnF9h85csTpMXPmTNlsNvXq1ctp3IQJE5zGDRs2zOxzOByKi4tTRESEtmzZosmTJyslJUUzZsy4qucGAAAqjiquPHhCQoISEhIu2B8SEuK0/fnnn6tjx46qU6eOU7u3t3eRsYXmzp2rs2fPaubMmfLw8FDjxo2Vnp6uqVOnavDgwX//JAAAQIVXYdYQZWVl6csvv9SAAQOK9E2cOFG1atVSixYtNHnyZOXl5Zl9aWlpateunTw8PMy2+Ph47dy5U8ePHy/2WLm5uXI4HE4PAABQebn0CtGVmDNnjry9vXXHHXc4tQ8fPlzXX3+9/P39tW7dOiUnJ+vIkSOaOnWqJCkzM1NRUVFOzwkODjb7atasWeRYqampGj9+/FU6EwAAUN5UmEA0c+ZM9e3bV56enk7tI0eONL9u2rSpPDw89NBDDyk1NVV2u71Ex0pOTnbar8PhUHh4eMkKBwAA5V6FCERff/21du7cqXnz5l1ybOvWrZWXl6d9+/apYcOGCgkJUVZWltOYwu0LrTuy2+0lDlMAAKDiqRBriN59913FxMSoWbNmlxybnp4uNzc3BQUFSZJiY2O1du1anTt3zhyzfPlyNWzYsNjpMgAAYD0uDUSnT59Wenq60tPTJUl79+5Venq6Dhw4YI5xOByaP3++Bg4cWOT5aWlpmjZtmr7//nv98ssvmjt3rkaMGKH77rvPDDt9+vSRh4eHBgwYoO3bt2vevHl6+eWXnabEAACAtbl0ymzz5s3q2LGjuV0YUhITEzV79mxJ0scffyzDMNS7d+8iz7fb7fr444+VkpKi3NxcRUVFacSIEU5hx9fXV8uWLdOQIUMUExOjgIAAjR07llvuAQCAyWYYhuHqIso7h8MhX19fnTx5Uj4+PqW+/8jRX5b6PoHKYt/E7q4uAUAFdSW/vyvEGiIAAICriUAEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsz6WBaO3aterRo4fCwsJks9m0cOFCp/7+/fvLZrM5Pbp27eo05tixY+rbt698fHzk5+enAQMG6PTp005jMjIy1LZtW3l6eio8PFyTJk262qcGAAAqEJcGopycHDVr1kzTp0+/4JiuXbvqyJEj5uOjjz5y6u/bt6+2b9+u5cuXa9GiRVq7dq0GDx5s9jscDsXFxSkiIkJbtmzR5MmTlZKSohkzZly18wIAABVLFVcePCEhQQkJCRcdY7fbFRISUmzfjz/+qCVLlmjTpk1q2bKlJOnVV19Vt27dNGXKFIWFhWnu3Lk6e/asZs6cKQ8PDzVu3Fjp6emaOnWqU3ACAADWVe7XEK1Zs0ZBQUFq2LChHnnkER09etTsS0tLk5+fnxmGJKlz585yc3PThg0bzDHt2rWTh4eHOSY+Pl47d+7U8ePHy+5EAABAueXSK0SX0rVrV91xxx2KiorSnj179NRTTykhIUFpaWlyd3dXZmamgoKCnJ5TpUoV+fv7KzMzU5KUmZmpqKgopzHBwcFmX82aNYscNzc3V7m5uea2w+Eo7VMDAADlSLkORPfee6/5dZMmTdS0aVPVrVtXa9asUadOna7acVNTUzV+/Pirtn8AAFC+lPspsz+rU6eOAgICtHv3bklSSEiIsrOzncbk5eXp2LFj5rqjkJAQZWVlOY0p3L7Q2qTk5GSdPHnSfBw8eLC0TwUAAJQjFSoQ/e9//9PRo0cVGhoqSYqNjdWJEye0ZcsWc8yqVatUUFCg1q1bm2PWrl2rc+fOmWOWL1+uhg0bFjtdJp1fyO3j4+P0AAAAlZdLA9Hp06eVnp6u9PR0SdLevXuVnp6uAwcO6PTp0xo1apTWr1+vffv2aeXKlbrttttUr149xcfHS5KuvfZade3aVYMGDdLGjRv17bffaujQobr33nsVFhYmSerTp488PDw0YMAAbd++XfPmzdPLL7+skSNHuuq0AQBAOePSQLR582a1aNFCLVq0kCSNHDlSLVq00NixY+Xu7q6MjAzdeuutatCggQYMGKCYmBh9/fXXstvt5j7mzp2rRo0aqVOnTurWrZvatGnj9BlDvr6+WrZsmfbu3auYmBg98cQTGjt2LLfcAwAAk80wDMPVRZR3DodDvr6+Onny5FWZPosc/WWp7xOoLPZN7O7qEgBUUFfy+7tCrSECAAC4GghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8lwaiNauXasePXooLCxMNptNCxcuNPvOnTunpKQkNWnSRNWrV1dYWJj69eunw4cPO+0jMjJSNpvN6TFx4kSnMRkZGWrbtq08PT0VHh6uSZMmlcXpAQCACsKlgSgnJ0fNmjXT9OnTi/SdOXNGW7du1dNPP62tW7dqwYIF2rlzp2699dYiYydMmKAjR46Yj2HDhpl9DodDcXFxioiI0JYtWzR58mSlpKRoxowZV/XcAABAxVHFlQdPSEhQQkJCsX2+vr5avny5U9trr72mVq1a6cCBA6pdu7bZ7u3trZCQkGL3M3fuXJ09e1YzZ86Uh4eHGjdurPT0dE2dOlWDBw8uvZMBAAAVVoVaQ3Ty5EnZbDb5+fk5tU+cOFG1atVSixYtNHnyZOXl5Zl9aWlpateunTw8PMy2+Ph47dy5U8ePHy/2OLm5uXI4HE4PAABQebn0CtGV+OOPP5SUlKTevXvLx8fHbB8+fLiuv/56+fv7a926dUpOTtaRI0c0depUSVJmZqaioqKc9hUcHGz21axZs8ixUlNTNX78+Kt4NgAAoDypEIHo3Llzuvvuu2UYht544w2nvpEjR5pfN23aVB4eHnrooYeUmpoqu91eouMlJyc77dfhcCg8PLxkxQMAgHKv3AeiwjC0f/9+rVq1yunqUHFat26tvLw87du3Tw0bNlRISIiysrKcxhRuX2jdkd1uL3GYAgAAFU+5XkNUGIZ27dqlFStWqFatWpd8Tnp6utzc3BQUFCRJio2N1dq1a3Xu3DlzzPLly9WwYcNip8sAAID1uPQK0enTp7V7925ze+/evUpPT5e/v79CQ0N15513auvWrVq0aJHy8/OVmZkpSfL395eHh4fS0tK0YcMGdezYUd7e3kpLS9OIESN03333mWGnT58+Gj9+vAYMGKCkpCRt27ZNL7/8sl566SWXnDMAACh/bIZhGK46+Jo1a9SxY8ci7YmJiUpJSSmyGLrQ6tWr1aFDB23dulWPPvqofvrpJ+Xm5ioqKkr333+/Ro4c6TTllZGRoSFDhmjTpk0KCAjQsGHDlJSUdNl1OhwO+fr66uTJk5ecsiuJyNFflvo+gcpi38Turi4BQAV1Jb+/XRqIKgoCEeA6BCIAJXUlv7/L9RoiAACAskAgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlleiQFSnTh0dPXq0SPuJEydUp06dv10UAABAWSpRINq3b5/y8/OLtOfm5urQoUN/uygAAICyVOVKBv/nP/8xv166dKl8fX3N7fz8fK1cuVKRkZGlVhwAAEBZuKJA1LNnT0mSzWZTYmKiU1/VqlUVGRmpF198sdSKAwAAKAtXFIgKCgokSVFRUdq0aZMCAgKuSlEAAABl6YoCUaG9e/eWdh0AAAAuU6JAJEkrV67UypUrlZ2dbV45KjRz5sy/XRgAAEBZKVEgGj9+vCZMmKCWLVsqNDRUNputtOsCAAAoMyUKRG+++aZmz56t+++/v7TrAQAAKHMl+hyis2fP6qabbvrbB1+7dq169OihsLAw2Ww2LVy40KnfMAyNHTtWoaGhqlatmjp37qxdu3Y5jTl27Jj69u0rHx8f+fn5acCAATp9+rTTmIyMDLVt21aenp4KDw/XpEmT/nbtAACg8ihRIBo4cKA+/PDDv33wnJwcNWvWTNOnTy+2f9KkSXrllVf05ptvasOGDapevbri4+P1xx9/mGP69u2r7du3a/ny5Vq0aJHWrl2rwYMHm/0Oh0NxcXGKiIjQli1bNHnyZKWkpGjGjBl/u34AAFA5lGjK7I8//tCMGTO0YsUKNW3aVFWrVnXqnzp16mXtJyEhQQkJCcX2GYahadOmacyYMbrtttskSe+9956Cg4O1cOFC3Xvvvfrxxx+1ZMkSbdq0SS1btpQkvfrqq+rWrZumTJmisLAwzZ07V2fPntXMmTPl4eGhxo0bKz09XVOnTnUKTgAAwLpKdIUoIyNDzZs3l5ubm7Zt26bvvvvOfKSnp5dKYXv37lVmZqY6d+5stvn6+qp169ZKS0uTJKWlpcnPz88MQ5LUuXNnubm5acOGDeaYdu3aycPDwxwTHx+vnTt36vjx48UeOzc3Vw6Hw+kBAAAqrxJdIVq9enVp11FEZmamJCk4ONipPTg42OzLzMxUUFCQU3+VKlXk7+/vNCYqKqrIPgr7atasWeTYqampGj9+fOmcCAAAKPdKdIWosktOTtbJkyfNx8GDB11dEgAAuIpKdIWoY8eOF/3soVWrVpW4oEIhISGSpKysLIWGhprtWVlZat68uTkmOzvb6Xl5eXk6duyY+fyQkBBlZWU5jSncLhzzV3a7XXa7/W+fAwAAqBhKdIWoefPmatasmfmIjo7W2bNntXXrVjVp0qRUCouKilJISIhWrlxptjkcDm3YsEGxsbGSpNjYWJ04cUJbtmwxx6xatUoFBQVq3bq1OWbt2rU6d+6cOWb58uVq2LBhsdNlAADAekp0heill14qtj0lJaXIZwBdzOnTp7V7925ze+/evUpPT5e/v79q166txx9/XM8++6zq16+vqKgoPf300woLC1PPnj0lSddee626du2qQYMG6c0339S5c+c0dOhQ3XvvvQoLC5Mk9enTR+PHj9eAAQOUlJSkbdu26eWXX77gOQAAAOuxGYZhlNbOdu/erVatWunYsWOXNX7NmjXq2LFjkfbExETNnj1bhmFo3LhxmjFjhk6cOKE2bdro9ddfV4MGDcyxx44d09ChQ/XFF1/Izc1NvXr10iuvvKIaNWqYYzIyMjRkyBBt2rRJAQEBGjZsmJKSki77vBwOh3x9fXXy5En5+Phc9vMuV+ToL0t9n0BlsW9id1eXAKCCupLf36UaiN5//30lJSXp8OHDpbXLcoFABLgOgQhASV3J7+8STZndcccdTtuGYejIkSPavHmznn766ZLsEgAAwGVKFIh8fX2dtt3c3NSwYUNNmDBBcXFxpVIYAABAWSlRIJo1a1Zp1wEAAOAyJQpEhbZs2aIff/xRktS4cWO1aNGiVIoCAAAoSyUKRNnZ2br33nu1Zs0a+fn5SZJOnDihjh076uOPP1ZgYGBp1ggAAHBVleiDGYcNG6ZTp05p+/btOnbsmI4dO6Zt27bJ4XBo+PDhpV0jAADAVVWiK0RLlizRihUrdO2115pt0dHRmj59OouqAQBAhVOiK0QFBQWqWrVqkfaqVauqoKDgbxcFAABQlkoUiG6++WY99thjTh/AeOjQIY0YMUKdOnUqteIAAADKQokC0WuvvSaHw6HIyEjVrVtXdevWVVRUlBwOh1599dXSrhEAAOCqKtEaovDwcG3dulUrVqzQTz/9JOn8H1rt3LlzqRYHAABQFq7oCtGqVasUHR0th8Mhm82mLl26aNiwYRo2bJhuuOEGNW7cWF9//fXVqhUAAOCquKJANG3aNA0aNKjYP5Dm6+urhx56SFOnTi214gAAAMrCFQWi77//Xl27dr1gf1xcnLZs2fK3iwIAAChLVxSIsrKyir3dvlCVKlX066+//u2iAAAAytIVBaJ//OMf2rZt2wX7MzIyFBoa+reLAgAAKEtXFIi6deump59+Wn/88UeRvt9//13jxo3TLbfcUmrFAQAAlIUruu1+zJgxWrBggRo0aKChQ4eqYcOGkqSffvpJ06dPV35+vv71r39dlUIBAACulisKRMHBwVq3bp0eeeQRJScnyzAMSZLNZlN8fLymT5+u4ODgq1IoAADA1XLFH8wYERGhxYsX6/jx49q9e7cMw1D9+vVVs2bNq1EfAADAVVeiT6qWpJo1a+qGG24ozVoAAABcokR/ywwAAKAyIRABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLK/eBKDIyUjabrchjyJAhkqQOHToU6Xv44Yed9nHgwAF1795dXl5eCgoK0qhRo5SXl+eK0wEAAOVQFVcXcCmbNm1Sfn6+ub1t2zZ16dJFd911l9k2aNAgTZgwwdz28vIyv87Pz1f37t0VEhKidevW6ciRI+rXr5+qVq2q559/vmxOAgAAlGvlPhAFBgY6bU+cOFF169ZV+/btzTYvLy+FhIQU+/xly5Zpx44dWrFihYKDg9W8eXM988wzSkpKUkpKijw8PK5q/QAAoPwr91Nmf3b27Fl98MEHevDBB2Wz2cz2uXPnKiAgQNddd52Sk5N15swZsy8tLU1NmjRRcHCw2RYfHy+Hw6Ht27eXaf0AAKB8KvdXiP5s4cKFOnHihPr372+29enTRxEREQoLC1NGRoaSkpK0c+dOLViwQJKUmZnpFIYkmduZmZnFHic3N1e5ubnmtsPhKOUzAQAA5UmFCkTvvvuuEhISFBYWZrYNHjzY/LpJkyYKDQ1Vp06dtGfPHtWtW7dEx0lNTdX48eP/dr0AAKBiqDBTZvv379eKFSs0cODAi45r3bq1JGn37t2SpJCQEGVlZTmNKdy+0Lqj5ORknTx50nwcPHjw75YPAADKsQoTiGbNmqWgoCB17979ouPS09MlSaGhoZKk2NhY/fDDD8rOzjbHLF++XD4+PoqOji52H3a7XT4+Pk4PAABQeVWIKbOCggLNmjVLiYmJqlLl/0res2ePPvzwQ3Xr1k21atVSRkaGRowYoXbt2qlp06aSpLi4OEVHR+v+++/XpEmTlJmZqTFjxmjIkCGy2+2uOiUAAFCOVIhAtGLFCh04cEAPPvigU7uHh4dWrFihadOmKScnR+Hh4erVq5fGjBljjnF3d9eiRYv0yCOPKDY2VtWrV1diYqLT5xYBAABrqxCBKC4uToZhFGkPDw/XV199dcnnR0REaPHixVejNAAAUAlUmDVEAAAAVwuBCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWF4VVxcAAFYQOfpLV5cAlGv7JnZ36fG5QgQAACyPQAQAACyPQAQAACyvXAeilJQU2Ww2p0ejRo3M/j/++ENDhgxRrVq1VKNGDfXq1UtZWVlO+zhw4IC6d+8uLy8vBQUFadSoUcrLyyvrUwEAAOVYuV9U3bhxY61YscLcrlLl/0oeMWKEvvzyS82fP1++vr4aOnSo7rjjDn377beSpPz8fHXv3l0hISFat26djhw5on79+qlq1ap6/vnny/xcAABA+VTuA1GVKlUUEhJSpP3kyZN699139eGHH+rmm2+WJM2aNUvXXnut1q9frxtvvFHLli3Tjh07tGLFCgUHB6t58+Z65plnlJSUpJSUFHl4eJT16QAAgHKoXE+ZSdKuXbsUFhamOnXqqG/fvjpw4IAkacuWLTp37pw6d+5sjm3UqJFq166ttLQ0SVJaWpqaNGmi4OBgc0x8fLwcDoe2b99+wWPm5ubK4XA4PQAAQOVVrgNR69atNXv2bC1ZskRvvPGG9u7dq7Zt2+rUqVPKzMyUh4eH/Pz8nJ4THByszMxMSVJmZqZTGCrsL+y7kNTUVPn6+pqP8PDw0j0xAABQrpTrKbOEhATz66ZNm6p169aKiIjQJ598omrVql214yYnJ2vkyJHmtsPhIBQBAFCJlesrRH/l5+enBg0aaPfu3QoJCdHZs2d14sQJpzFZWVnmmqOQkJAid50Vbhe3LqmQ3W6Xj4+P0wMAAFReFSoQnT59Wnv27FFoaKhiYmJUtWpVrVy50uzfuXOnDhw4oNjYWElSbGysfvjhB2VnZ5tjli9fLh8fH0VHR5d5/QAAoHwq11NmTz75pHr06KGIiAgdPnxY48aNk7u7u3r37i1fX18NGDBAI0eOlL+/v3x8fDRs2DDFxsbqxhtvlCTFxcUpOjpa999/vyZNmqTMzEyNGTNGQ4YMkd1ud/HZAQCA8qJcB6L//e9/6t27t44eParAwEC1adNG69evV2BgoCTppZdekpubm3r16qXc3FzFx8fr9ddfN5/v7u6uRYsW6ZFHHlFsbKyqV6+uxMRETZgwwVWnBAAAyqFyHYg+/vjji/Z7enpq+vTpmj59+gXHREREaPHixaVdGgAAqEQq1BoiAACAq4FABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALK9cB6LU1FTdcMMN8vb2VlBQkHr27KmdO3c6jenQoYNsNpvT4+GHH3Yac+DAAXXv3l1eXl4KCgrSqFGjlJeXV5anAgAAyrEqri7gYr766isNGTJEN9xwg/Ly8vTUU08pLi5OO3bsUPXq1c1xgwYN0oQJE8xtLy8v8+v8/Hx1795dISEhWrdunY4cOaJ+/fqpatWqev7558v0fAAAQPlUrgPRkiVLnLZnz56toKAgbdmyRe3atTPbvby8FBISUuw+li1bph07dmjFihUKDg5W8+bN9cwzzygpKUkpKSny8PC4qucAAADKv3I9ZfZXJ0+elCT5+/s7tc+dO1cBAQG67rrrlJycrDNnzph9aWlpatKkiYKDg822+Ph4ORwObd++vdjj5ObmyuFwOD0AAEDlVa6vEP1ZQUGBHn/8cf2///f/dN1115ntffr0UUREhMLCwpSRkaGkpCTt3LlTCxYskCRlZmY6hSFJ5nZmZmaxx0pNTdX48eOv0pkAAIDypsIEoiFDhmjbtm365ptvnNoHDx5sft2kSROFhoaqU6dO2rNnj+rWrVuiYyUnJ2vkyJHmtsPhUHh4eMkKBwAA5V6FmDIbOnSoFi1apNWrV+uaa6656NjWrVtLknbv3i1JCgkJUVZWltOYwu0LrTuy2+3y8fFxegAAgMqrXAciwzA0dOhQffbZZ1q1apWioqIu+Zz09HRJUmhoqCQpNjZWP/zwg7Kzs80xy5cvl4+Pj6Kjo69K3QAAoGIp11NmQ4YM0YcffqjPP/9c3t7e5pofX19fVatWTXv27NGHH36obt26qVatWsrIyNCIESPUrl07NW3aVJIUFxen6Oho3X///Zo0aZIyMzM1ZswYDRkyRHa73ZWnBwAAyolyfYXojTfe0MmTJ9WhQweFhoaaj3nz5kmSPDw8tGLFCsXFxalRo0Z64okn1KtXL33xxRfmPtzd3bVo0SK5u7srNjZW9913n/r16+f0uUUAAMDayvUVIsMwLtofHh6ur7766pL7iYiI0OLFi0urLAAAUMmU6ytEAAAAZYFABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALM9SgWj69OmKjIyUp6enWrdurY0bN7q6JAAAUA5YJhDNmzdPI0eO1Lhx47R161Y1a9ZM8fHxys7OdnVpAADAxSwTiKZOnapBgwbpgQceUHR0tN588015eXlp5syZri4NAAC4mCUC0dmzZ7VlyxZ17tzZbHNzc1Pnzp2VlpbmwsoAAEB5UMXVBZSF3377Tfn5+QoODnZqDw4O1k8//VRkfG5urnJzc83tkydPSpIcDsdVqa8g98xV2S9QGVytn7uyxs85cHFX42e9cJ+GYVxyrCUC0ZVKTU3V+PHji7SHh4e7oBrA2nynuboCAGXhav6snzp1Sr6+vhcdY4lAFBAQIHd3d2VlZTm1Z2VlKSQkpMj45ORkjRw50twuKCjQsWPHVKtWLdlstqteL1zH4XAoPDxcBw8elI+Pj6vLAXCV8LNuDYZh6NSpUwoLC7vkWEsEIg8PD8XExGjlypXq2bOnpPMhZ+XKlRo6dGiR8Xa7XXa73anNz8+vDCpFeeHj48M/koAF8LNe+V3qylAhSwQiSRo5cqQSExPVsmVLtWrVStOmTVNOTo4eeOABV5cGAABczDKB6J577tGvv/6qsWPHKjMzU82bN9eSJUuKLLQGAADWY5lAJElDhw4tdooMKGS32zVu3LgiU6YAKhd+1vFXNuNy7kUDAACoxCzxwYwAAAAXQyACAACWRyACAACWRyACAACWRyAC/mT69OmKjIyUp6enWrdurY0bN7q6JAClaO3aterRo4fCwsJks9m0cOFCV5eEcoJABPz/5s2bp5EjR2rcuHHaunWrmjVrpvj4eGVnZ7u6NAClJCcnR82aNdP06dNdXQrKGW67B/5/rVu31g033KDXXntN0vk/7xIeHq5hw4Zp9OjRLq4OQGmz2Wz67LPPzD/pBGvjChEg6ezZs9qyZYs6d+5strm5ualz585KS0tzYWUAgLJAIAIk/fbbb8rPzy/yp1yCg4OVmZnpoqoAAGWFQAQAACyPQARICggIkLu7u7Kyspzas7KyFBIS4qKqAABlhUAESPLw8FBMTIxWrlxpthUUFGjlypWKjY11YWUAgLJgqb92D1zMyJEjlZiYqJYtW6pVq1aaNm2acnJy9MADD7i6NACl5PTp09q9e7e5vXfvXqWnp8vf31+1a9d2YWVwNW67B/7ktdde0+TJk5WZmanmzZvrlVdeUevWrV1dFoBSsmbNGnXs2LFIe2JiombPnl32BaHcIBABAADLYw0RAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRgEqhQ4cOevzxxy9r7Jo1a2Sz2XTixIm/dczIyEhNmzbtb+0DQPlAIAIAAJZHIAIAAJZHIAJQ6bz//vtq2bKlvL29FRISoj59+ig7O7vIuG+//VZNmzaVp6enbrzxRm3bts2p/5tvvlHbtm1VrVo1hYeHa/jw4crJySmr0wBQhghEACqdc+fO6ZlnntH333+vhQsXat++ferfv3+RcaNGjdKLL76oTZs2KTAwUD169NC5c+ckSXv27FHXrl3Vq1cvZWRkaN68efrmm280dOjQMj4bAGWhiqsLAIDS9uCDD5pf16lTR6+88opuuOEGnT59WjVq1DD7xo0bpy5dukiS5syZo2uuuUafffaZ7r77bqWmpqpv377mQu369evrlVdeUfv27fXGG2/I09OzTM8JwNXFFSIAlc6WLVvUo0cP1a5dW97e3mrfvr0k6cCBA07jYmNjza/9/f3VsGFD/fjjj5Kk77//XrNnz1aNGjXMR3x8vAoKCrR3796yOxkAZYIrRAAqlZycHMXHxys+Pl5z585VYGCgDhw4oPj4eJ09e/ay93P69Gk99NBDGj58eJG+2rVrl2bJAMoBAhGASuWnn37S0aNHNXHiRIWHh0uSNm/eXOzY9evXm+Hm+PHj+vnnn3XttddKkq6//nrt2LFD9erVK5vCAbgUU2YAKpXatWvLw8NDr776qn755Rf95z//0TPPPFPs2AkTJmjlypXatm2b+vfvr4CAAPXs2VOSlJSUpHXr1mno0KFKT0/Xrl279Pnnn7OoGqikCEQAKpXAwEDNnj1b8+fPV3R0tCZOnKgpU6YUO3bixIl67LHHFBMTo8zMTH3xxRfy8PCQJDVt2lRfffWVfv75Z7Vt21YtWrTQ2LFjFRYWVpanA6CM2AzDMFxdBAAAgCtxhQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFje/wfLC6CSY8quZAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Example of Environmental Claim:\n",
            "The goal to reduce relative GHG emissions by 20% by 2020 was achieved in 2013.\n",
            "\n",
            "Exemple of Non Environmental Claim:\n",
            "As part of this program we are expecting to increase the seat density on our Airbus A320 fleet.\n"
          ]
        }
      ],
      "source": [
        "# Information on the training dataset\n",
        "print(\"Information on the training dataset:\")\n",
        "print(f\"{'-' * 45}\")\n",
        "print('')\n",
        "\n",
        "# display the number of observations\n",
        "num_observations = len(df_train)\n",
        "print(f\"There are {num_observations} observations in the training dataset.\")\n",
        "print('')\n",
        "print('')\n",
        "\n",
        "\n",
        "# display the number of observations per class\n",
        "num_0 = (df_train['label'] == 0).sum()\n",
        "num_1 = (df_train['label'] == 1).sum()\n",
        "ratio = (num_1/num_0)*100\n",
        "print(f\"There are {num_0} observations that are not related to the environment (label = 0)\")\n",
        "print(f\"There are {num_1} observations that are environmental claims (label = 1).\")\n",
        "print(f\"The ratio is {ratio:.2f}%.\")\n",
        "\n",
        "#plot \n",
        "column_name = 'label'\n",
        "value_counts = df_train[column_name].value_counts()\n",
        "plt.bar(value_counts.index, value_counts.values)\n",
        "plt.title(f'Number of observations per class')\n",
        "plt.xlabel(column_name)\n",
        "plt.ylabel('Count')\n",
        "plt.xticks([0, 1])\n",
        "plt.show()\n",
        "\n",
        "# Diclose exemples of claims  \n",
        "label_1_indexes = df_train[df_train['label'] == 1].index\n",
        "label_1_observation = df_train.loc[label_1_indexes[0], 'text']\n",
        "print(f\"Example of Environmental Claim:\\n{label_1_observation}\")\n",
        "print('')\n",
        "label_0_indexes = df_train[df_train['label'] == 0].index\n",
        "label_0_observation = df_train.loc[label_0_indexes[123], 'text']\n",
        "print(f\"Exemple of Non Environmental Claim:\\n{label_0_observation}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "Aapg_n834_r8",
        "outputId": "0a6fb401-b393-45be-b289-c229668fb5c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Information on the testing dataset:\n",
            "---------------------------------------------\n",
            "\n",
            "There are 300 observations in the testing dataset.\n",
            "\n",
            "\n",
            "There are 236 observations that are not related to the environment (label = 0)\n",
            "There are 64 observations that are environmental claims (label = 1).\n",
            "The ratio is 27.12%.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Exemple of Environmental Claim:\n",
            "All operations at Panasonic Eco Technology Center are now powered by electricity derived from 100% renewable energy.\n",
            "\n",
            "Exemple of Non Environmental Claim:\n",
            "So cohorts like young moms and young dads and broader audiences, that's been very important.\n"
          ]
        }
      ],
      "source": [
        "# Information on the testing dataset\n",
        "print(\"Information on the testing dataset:\")\n",
        "print(f\"{'-' * 45}\")\n",
        "print('')\n",
        "\n",
        "# display the number of observations\n",
        "num_observations = len(df_test)\n",
        "print(f\"There are {num_observations} observations in the testing dataset.\")\n",
        "print('')\n",
        "print('')\n",
        "\n",
        "# display the number of observations per class\n",
        "num_0 = (df_test['label'] == 0).sum()\n",
        "num_1 = (df_test['label'] == 1).sum()\n",
        "ratio = (num_1/num_0)*100\n",
        "print(f\"There are {num_0} observations that are not related to the environment (label = 0)\")\n",
        "print(f\"There are {num_1} observations that are environmental claims (label = 1).\")\n",
        "print(f\"The ratio is {ratio:.2f}%.\")\n",
        "\n",
        "#plot \n",
        "column_name = 'label'\n",
        "value_counts = df_test[column_name].value_counts()\n",
        "plt.bar(value_counts.index, value_counts.values)\n",
        "plt.title(f'Number of observations per class')\n",
        "plt.xlabel(column_name)\n",
        "plt.ylabel('Count')\n",
        "plt.xticks([0, 1])\n",
        "plt.show()\n",
        "\n",
        "# Diclose exemples of claims  \n",
        "label_1_indexes = df_test[df_test['label'] == 1].index\n",
        "label_1_observation = df_test.loc[label_1_indexes[0], 'text']\n",
        "print(f\"Exemple of Environmental Claim:\\n{label_1_observation}\")\n",
        "print('')\n",
        "label_0_indexes = df_test[df_test['label'] == 0].index\n",
        "label_0_observation = df_test.loc[label_0_indexes[123], 'text']\n",
        "print(f\"Exemple of Non Environmental Claim:\\n{label_0_observation}\")\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "aKqcFJL95Tlb"
      },
      "source": [
        "\n",
        "**<span style=\"color:green\"> Warning!! It is not because a claim is labelled 0, that the sentence does not talk about the environment! See example below</span>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNi2jPqf4_uF",
        "outputId": "d77edf94-79c0-4230-dd4c-9ee166a92bf2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Label: 0\n",
            "Overview ESG Framework and Oversight Across Scope 1, 2 and 3, emissions per square metre fell 14.1 per cent to 100.51 kg CO2e/m2.\n"
          ]
        }
      ],
      "source": [
        "# access the row with index 15\n",
        "observation = df_train.loc[15]\n",
        "print(f\"Label: {observation['label']}\")\n",
        "print(observation['text'])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### EDA"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let us take a closer look at the training set. "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Explore words within claims"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "In total there are: 364767 words within the training dataset containing 2400 claims.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "count    2400.00000\n",
              "mean       24.44125\n",
              "std         7.54516\n",
              "min        11.00000\n",
              "25%        18.00000\n",
              "50%        24.00000\n",
              "75%        30.00000\n",
              "max        39.00000\n",
              "Name: word_count, dtype: float64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Receive more information on the words within the claim\n",
        "\n",
        "# Total number of words per claim in the training set\n",
        "observations = len(df_train)\n",
        "words_number = df_train[\"text\"].apply(lambda x: len(x)).sum()\n",
        "print(\"In total there are:\", words_number, \"words within the training dataset containing\", observations,\"claims.\")\n",
        "\n",
        "# Number of words per claim\n",
        "df_train[\"word_count\"] = df_train[\"text\"].apply(lambda x: len(x.split()))\n",
        "df_train[\"word_count\"].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Frequency</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Word</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>the</th>\n",
              "      <td>2796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>and</th>\n",
              "      <td>1975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>to</th>\n",
              "      <td>1969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>of</th>\n",
              "      <td>1770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>in</th>\n",
              "      <td>1320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>our</th>\n",
              "      <td>843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>we</th>\n",
              "      <td>696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>that</th>\n",
              "      <td>635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is</th>\n",
              "      <td>587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>for</th>\n",
              "      <td>549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>with</th>\n",
              "      <td>463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on</th>\n",
              "      <td>452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>as</th>\n",
              "      <td>406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>are</th>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>by</th>\n",
              "      <td>336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The</th>\n",
              "      <td>312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>have</th>\n",
              "      <td>275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>from</th>\n",
              "      <td>253</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Frequency\n",
              "Word             \n",
              "the          2796\n",
              "and          1975\n",
              "to           1969\n",
              "of           1770\n",
              "in           1320\n",
              "a             895\n",
              "our           843\n",
              "we            696\n",
              "that          635\n",
              "is            587\n",
              "for           549\n",
              "with          463\n",
              "on            452\n",
              "as            406\n",
              "are           356\n",
              "by            336\n",
              "The           312\n",
              "energy        290\n",
              "have          275\n",
              "from          253"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Combine all claims' text into a single string\n",
        "all_text = ' '.join(df_train['text'].tolist())\n",
        "\n",
        "# Tokenize the text by splitting on whitespace\n",
        "words = all_text.split()\n",
        "\n",
        "# Count the occurrences of each word\n",
        "word_counts = Counter(words)\n",
        "\n",
        "# Get n most frequent words\n",
        "n = 20  # Specify the number of most frequent words I want to retrieve\n",
        "most_frequent_words = word_counts.most_common(n)\n",
        "\n",
        "# create a list of most frequent words\n",
        "most_common = [{\"Word\": word, \"Frequency\": frequency} for word, frequency in most_frequent_words]\n",
        "word_common = pd.DataFrame(most_common)\n",
        "word_common.set_index(\"Word\",inplace=True)\n",
        "display(word_common)\n",
        "\n",
        "#visualize the 10 most common words in a bar chart \n",
        "plt.figure(figsize=(12,8))\n",
        "word_common[\"Frequency\"].plot(kind=\"bar\",color=\"red\")\n",
        "plt.xlabel(\"Word\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Top 20 most common words across all claims\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Sentence content - company name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>text</th>\n",
              "      <th>label</th>\n",
              "      <th>word_count</th>\n",
              "      <th>companies</th>\n",
              "      <th>entities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>It will enable E.ON to acquire and leverage a ...</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>[EON]</td>\n",
              "      <td>[EON]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>And that's honestly most markets in the U.S. S...</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>[]</td>\n",
              "      <td>[70, US]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A key pillar of our strategy is to continue to...</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The goal to reduce relative GHG emissions by 2...</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>[GHG]</td>\n",
              "      <td>[2020, GHG, 2013, 20]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>As you know, we drove about $75 million of cos...</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>[]</td>\n",
              "      <td>[fiscal year 18, about 75 million, last year]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>The collaboration will enable Nestlé to delive...</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>[Nestle]</td>\n",
              "      <td>[Nestle]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2396</th>\n",
              "      <td>Through the MOBILITY THAT ADAPTS TO MEET NEEDS...</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>[Renault, Renault Tech]</td>\n",
              "      <td>[European, Renault, 2009, Renault Tech]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2397</th>\n",
              "      <td>While we are making progress towards our near ...</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2398</th>\n",
              "      <td>On 5 December 2019, the Federal Network Agency...</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>[the Federal Network Agency]</td>\n",
              "      <td>[the Federal Network Agency, 5 December 2019, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2399</th>\n",
              "      <td>Emission reductions are subtracted from gross ...</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>[]</td>\n",
              "      <td>[Scope 1 to, 3]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2400 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text  label  word_count  \\\n",
              "0     It will enable E.ON to acquire and leverage a ...      0          34   \n",
              "1     And that's honestly most markets in the U.S. S...      0          37   \n",
              "2     A key pillar of our strategy is to continue to...      0          39   \n",
              "3     The goal to reduce relative GHG emissions by 2...      1          15   \n",
              "4     As you know, we drove about $75 million of cos...      0          17   \n",
              "...                                                 ...    ...         ...   \n",
              "2395  The collaboration will enable Nestlé to delive...      1          26   \n",
              "2396  Through the MOBILITY THAT ADAPTS TO MEET NEEDS...      0          35   \n",
              "2397  While we are making progress towards our near ...      0          25   \n",
              "2398  On 5 December 2019, the Federal Network Agency...      0          27   \n",
              "2399  Emission reductions are subtracted from gross ...      0          17   \n",
              "\n",
              "                         companies  \\\n",
              "0                            [EON]   \n",
              "1                               []   \n",
              "2                               []   \n",
              "3                            [GHG]   \n",
              "4                               []   \n",
              "...                            ...   \n",
              "2395                      [Nestle]   \n",
              "2396       [Renault, Renault Tech]   \n",
              "2397                            []   \n",
              "2398  [the Federal Network Agency]   \n",
              "2399                            []   \n",
              "\n",
              "                                               entities  \n",
              "0                                                 [EON]  \n",
              "1                                              [70, US]  \n",
              "2                                                    []  \n",
              "3                                 [2020, GHG, 2013, 20]  \n",
              "4         [fiscal year 18, about 75 million, last year]  \n",
              "...                                                 ...  \n",
              "2395                                           [Nestle]  \n",
              "2396            [European, Renault, 2009, Renault Tech]  \n",
              "2397                                                 []  \n",
              "2398  [the Federal Network Agency, 5 December 2019, ...  \n",
              "2399                                    [Scope 1 to, 3]  \n",
              "\n",
              "[2400 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "entities = []\n",
        "companies = []\n",
        "\n",
        "for text in df_train[\"text\"]:\n",
        "    # Remove punctuation\n",
        "    text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
        "    # Change accented characters like é -> e\n",
        "    text = unidecode.unidecode(text)\n",
        "    doc = sp(text)\n",
        "    orgs = [ent.text for ent in doc.ents if ent.label_ == \"ORG\"]\n",
        "    ent = [ent.text for ent in doc.ents if ent.label_]\n",
        "    # Remove duplicates and empty strings\n",
        "    orgs = list(set([org.strip() for org in orgs if org.strip()]))\n",
        "    ent = list(set([e.strip() for e in ent if e.strip()]))\n",
        "    companies.append(orgs)\n",
        "    entities.append(ent)\n",
        "\n",
        "# Add companies and entities as new columns to the DataFrame\n",
        "df_train[\"companies\"] = companies\n",
        "df_train[\"entities\"] = entities\n",
        "\n",
        "display(df_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 1:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\Charlotte Ahrens\\anaconda3\\lib\\site-packages\\spacy\\displacy\\__init__.py:215: UserWarning: [W006] No entities to visualize found in Doc object. If this is surprising to you, make sure the Doc was processed using a model that supports named entity recognition, and check the `doc.ents` property manually if necessary.\n",
            "  warnings.warn(Warnings.W006)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">It will enable E.ON to acquire and leverage a comprehensive understanding of the transfor- mation of the energy system and the interplay between the individual submarkets in regional and local energy supply sys- tems.</div></span>"
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 2:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">And that's honestly most markets in the \n",
              "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    U.S.\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
              "</mark>\n",
              " So that is a fact that we think there is opportunity for us to bring some tuck-in acquisitions that help us get to our goal of \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    70%\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERCENT</span>\n",
              "</mark>\n",
              " and beyond.</div></span>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 3:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">A key pillar of our strategy is to continue to adapt our business mix to position us for longer-term low interest rates whilst continuing to serve our customers’ needs through the provision of attractive unit-linked and capital-light products. •</div></span>"
            ],
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            ]
          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 4:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">The goal to reduce relative \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    GHG\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " emissions by \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    20%\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERCENT</span>\n",
              "</mark>\n",
              " by \n",
              "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    2020\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
              "</mark>\n",
              " was achieved in \n",
              "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    2013\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
              "</mark>\n",
              ".</div></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 5:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">As you know, we drove \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    about $75 million\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
              "</mark>\n",
              " of cost synergies \n",
              "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    last year\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
              "</mark>\n",
              ", in \n",
              "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    fiscal year '18\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
              "</mark>\n",
              ".</div></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 6:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    1.2\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">CARDINAL</span>\n",
              "</mark>\n",
              " Significant Changes to Assets In \n",
              "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    February 2016\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
              "</mark>\n",
              ", \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    AGL\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " created \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    the Powering Australian Renewables Fund\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " (PARF), of which \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    AGL\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " would own \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    20%\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERCENT</span>\n",
              "</mark>\n",
              " of the equity, to develop and own \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    approximately 1,000\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">CARDINAL</span>\n",
              "</mark>\n",
              " \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    MW\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " of large-scale renewable energy generation assets.</div></span>"
            ],
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            ]
          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 7:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    35%\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERCENT</span>\n",
              "</mark>\n",
              " of the remuneration that is determined by the qualitative internal assessment of \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    the Executive Board\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " of Directors by \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    the Remuneration Committee of the General and Supervisory Board\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " is reserved.</div></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 8:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">This not only provides a clean energy solution to improve basic sanitation, but also makes effective use of plastic and other waste materials.</div></span>"
            ],
            "text/plain": [
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            ]
          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 9:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">This requires us to be fully informed in our long-term decisions that are anchored on enhancing the capabilities of our people and being mindful of our impact on the planet.</div></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Claim 10:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">The \n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Group\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " has therefore built its sustainability approach on \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    four\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">CARDINAL</span>\n",
              "</mark>\n",
              " areas of action: – the integration of climate into its strategy, because energy production and consumption are intrinsically linked to the challenge of climate change.</div></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#taking a closer look at the claims different entities\n",
        "first_10_lines = df_train[\"text\"].head(10)\n",
        "text_10 = first_10_lines.to_list()\n",
        "docs = [sp(text) for text in text_10]\n",
        "\n",
        "for i, doc in enumerate(docs):\n",
        "    text_10_num = (f\"Claim {i+1}:\")\n",
        "    print(text_10_num) \n",
        "    displacy.render(doc,style=\"ent\",jupyter=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Company: ['MW'], Count: 14\n",
            "Company: ['LNG'], Count: 11\n",
            "Company: ['Bank'], Count: 6\n",
            "Company: ['EDF'], Count: 5\n",
            "Company: ['UN'], Count: 4\n",
            "Company: ['ESG'], Count: 4\n",
            "Company: ['Airbus'], Count: 4\n",
            "Company: ['RD'], Count: 3\n",
            "Company: ['Climate'], Count: 3\n",
            "Company: ['ROIC'], Count: 3\n",
            "Company: ['Tokio Marine Group'], Count: 3\n",
            "Company: ['BBVA'], Count: 3\n",
            "Company: ['AXA'], Count: 3\n",
            "Company: ['TCFD'], Count: 3\n",
            "Company: ['NGL'], Count: 2\n",
            "Company: ['Nordea'], Count: 2\n",
            "Company: ['FENC'], Count: 2\n",
            "Company: ['Energy'], Count: 2\n",
            "Company: ['Companys'], Count: 2\n",
            "Company: ['the Paris Agreement'], Count: 2\n",
            "Company: ['NTPC'], Count: 2\n",
            "Company: ['VOC'], Count: 2\n",
            "Company: ['Hydro'], Count: 2\n",
            "Company: ['KLP'], Count: 2\n",
            "Company: ['Groups'], Count: 2\n",
            "Company: ['VERBUND'], Count: 2\n",
            "Company: ['NYSPSC'], Count: 2\n",
            "Company: ['CH4'], Count: 2\n",
            "Company: ['FPSO'], Count: 2\n",
            "Company: ['Alliance'], Count: 2\n",
            "Company: ['EV'], Count: 2\n",
            "Company: ['MTN'], Count: 2\n",
            "Company: ['the Task Force'], Count: 2\n",
            "Company: ['EU'], Count: 2\n",
            "Company: ['Eagle Ford'], Count: 2\n",
            "Company: ['EPA'], Count: 2\n",
            "Company: ['BlueScope'], Count: 2\n",
            "Company: ['CECONY'], Count: 2\n",
            "Company: ['Board'], Count: 2\n",
            "Company: ['EON'], Count: 2\n",
            "Company: ['Diesel'], Count: 2\n",
            "Company: ['Shell'], Count: 2\n",
            "Company: ['BP'], Count: 2\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 3000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#looking for multiples in terms of the company name, including all type of claims\n",
        "company_counts = df_train[\"companies\"].value_counts()\n",
        "multiple_times = company_counts[(company_counts > 1) & (company_counts < 16)] #restrict to < 16 to exclude nan's and other entities that were titled as companies, but are other acronyms like GHG\n",
        "for company, count in multiple_times.items():\n",
        "    print(f\"Company: {company}, Count: {count}\")\n",
        "\n",
        "#displaying the count of companies visually \n",
        "plt.figure(figsize=(30,6))\n",
        "multiple_times.plot(kind=\"bar\")\n",
        "plt.xlabel(\"Company\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Count of Companies, mentioned in the claims\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n",
        "\n",
        "#I Was thinking to add hue -> depending on if the label of the countries are 0 or 1 -> differentiate it by color, but I didn't figure it out how to program it "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>text</th>\n",
              "      <th>label</th>\n",
              "      <th>word_count</th>\n",
              "      <th>companies</th>\n",
              "      <th>entities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>As part of this program we are expecting to in...</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>[Airbus]</td>\n",
              "      <td>[Airbus, A320]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>904</th>\n",
              "      <td>In conjunction with the new order, we also res...</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>[Airbus]</td>\n",
              "      <td>[25, Airbus]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1785</th>\n",
              "      <td>This enables Airbus to measure its environment...</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>[Airbus]</td>\n",
              "      <td>[Airbus]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1886</th>\n",
              "      <td>In connection with our plans to launch flights...</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>[Airbus]</td>\n",
              "      <td>[2021, London, April 2019, Airbus, 13, A321 Lo...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text  label  word_count  \\\n",
              "162   As part of this program we are expecting to in...      0          18   \n",
              "904   In conjunction with the new order, we also res...      0          31   \n",
              "1785  This enables Airbus to measure its environment...      0          22   \n",
              "1886  In connection with our plans to launch flights...      0          34   \n",
              "\n",
              "     companies                                           entities  \n",
              "162   [Airbus]                                     [Airbus, A320]  \n",
              "904   [Airbus]                                       [25, Airbus]  \n",
              "1785  [Airbus]                                           [Airbus]  \n",
              "1886  [Airbus]  [2021, London, April 2019, Airbus, 13, A321 Lo...  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#exploring row that talk about a specific company for example Airbus\n",
        "company_name = \"Airbus\"\n",
        "company_Airbus = df_train[df_train[\"companies\"].apply(lambda x: company_name in x)]\n",
        "display(company_Airbus)\n",
        "\n",
        "#df_train[df_train[\"companies\"].str.contains(\"Airbus\")]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>text</th>\n",
              "      <th>label</th>\n",
              "      <th>word_count</th>\n",
              "      <th>companies</th>\n",
              "      <th>entities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The goal to reduce relative GHG emissions by 2...</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>[GHG]</td>\n",
              "      <td>[2020, GHG, 2013, 20]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.2 Significant Changes to Assets In February ...</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>[MW, the Powering Australian Renewables Fund P...</td>\n",
              "      <td>[the Powering Australian Renewables Fund PARF,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>This not only provides a clean energy solution...</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>The Group has therefore built its sustainabili...</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>[Group]</td>\n",
              "      <td>[Group, four]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Waste As part of our ambitions to move toward ...</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 text  label  word_count  \\\n",
              "3   The goal to reduce relative GHG emissions by 2...      1          15   \n",
              "5   1.2 Significant Changes to Assets In February ...      1          38   \n",
              "7   This not only provides a clean energy solution...      1          23   \n",
              "9   The Group has therefore built its sustainabili...      1          35   \n",
              "14  Waste As part of our ambitions to move toward ...      1          23   \n",
              "\n",
              "                                            companies  \\\n",
              "3                                               [GHG]   \n",
              "5   [MW, the Powering Australian Renewables Fund P...   \n",
              "7                                                  []   \n",
              "9                                             [Group]   \n",
              "14                                                 []   \n",
              "\n",
              "                                             entities  \n",
              "3                               [2020, GHG, 2013, 20]  \n",
              "5   [the Powering Australian Renewables Fund PARF,...  \n",
              "7                                                  []  \n",
              "9                                       [Group, four]  \n",
              "14                                                 []  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Company: [], Count: 305\n",
            "Company: ['Group'], Count: 11\n",
            "Company: ['Company'], Count: 9\n",
            "Company: ['GHG'], Count: 6\n",
            "Company: ['Bank'], Count: 5\n",
            "Company: ['ESG'], Count: 3\n",
            "Company: ['EDF'], Count: 3\n",
            "Company: ['UN'], Count: 3\n",
            "Company: ['VERBUND'], Count: 2\n",
            "Company: ['Tokio Marine Group'], Count: 2\n",
            "Company: ['Alliance'], Count: 2\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#the above shows that all Airbus claims where labelled as non-environmental claims. To get a better idea of companies that used such statements we focus on environmental claims only.\n",
        "df_train_env= df_train[df_train[\"label\"] ==1]\n",
        "display(df_train_env.head())\n",
        "\n",
        "#looking for multiples in terms of the company name\n",
        "company_counts = df_train_env[\"companies\"].value_counts()\n",
        "multiple_times_env = company_counts[company_counts >1]\n",
        "\n",
        "#displaying companies that have green claims\n",
        "for company, count in multiple_times_env.items():\n",
        "    print(f\"Company: {company}, Count: {count}\")\n",
        "# -> we see that most filtered \"companies\" are not actual companies .> filter accordingly\n",
        "\n",
        "#visually presenting the count of companies that are labelled with environmental claims  \n",
        "# Filter the specific companies\n",
        "selected_companies = [[\"EDF\"], [\"Tokio Marine Group\"]]\n",
        "filtered_counts = multiple_times_env[multiple_times_env.index.isin(selected_companies)]\n",
        "\n",
        "# Plot the counts of selected companies\n",
        "plt.figure(figsize=(8, 6))\n",
        "filtered_counts.plot(kind=\"bar\",color=\"green\")\n",
        "plt.xlabel(\"Company\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Count of Claims from Companies with Environmental Claims\")\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>The hydropower facilities have positive effects on both economic development and the environment, and EDF applies a proactive management policy in relation to water resources, in liaison with various water stakeholders.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Within the EpE (3), EDF also contributed to a global study on the carbon neutrality of France in 2050.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>738</th>\n",
              "      <td>All EDF group business lines are working to cut the carbon impact of the refrigerating fluids they use.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>In March 2020, EDF committed with ARVAL to facilitating access to electric mobility for both business and residential customers, based on IZI by EDF.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>949</th>\n",
              "      <td>In January 2021, the EDF group ranked first in the list of the most credible companies in terms of communication, in the utilities/energy category (1).</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1012</th>\n",
              "      <td>As part of the \"Coup de Pouce Chauffage\" (3) scheme, EDF launched in January 2019 its \"Mon chauffage durable\" offer, to finance replacing ageing heating systems by more efficient ones.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1534</th>\n",
              "      <td>Consequently, the Group has an “energy markets” risk policy for all energy commodities, applicable to EDF and entities over which it has operational control.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1556</th>\n",
              "      <td>In terms of new business lines, EDF is aiming to have a portfolio of 1 million off-grid kits by 2030 and is pursuing the development of new markets such as microgrids, smart grids, storage, hydrogen, mobility, smart cities, etc.,</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "                                                                                                                                                                                                                                       text  \\\n",
              "242             The hydropower facilities have positive effects on both economic development and the environment, and EDF applies a proactive management policy in relation to water resources, in liaison with various water stakeholders.   \n",
              "632                                                                                                                                  Within the EpE (3), EDF also contributed to a global study on the carbon neutrality of France in 2050.   \n",
              "738                                                                                                                                 All EDF group business lines are working to cut the carbon impact of the refrigerating fluids they use.   \n",
              "753                                                                                   In March 2020, EDF committed with ARVAL to facilitating access to electric mobility for both business and residential customers, based on IZI by EDF.   \n",
              "949                                                                                 In January 2021, the EDF group ranked first in the list of the most credible companies in terms of communication, in the utilities/energy category (1).   \n",
              "1012                                               As part of the \"Coup de Pouce Chauffage\" (3) scheme, EDF launched in January 2019 its \"Mon chauffage durable\" offer, to finance replacing ageing heating systems by more efficient ones.   \n",
              "1534                                                                          Consequently, the Group has an “energy markets” risk policy for all energy commodities, applicable to EDF and entities over which it has operational control.   \n",
              "1556  In terms of new business lines, EDF is aiming to have a portfolio of 1 million off-grid kits by 2030 and is pursuing the development of new markets such as microgrids, smart grids, storage, hydrogen, mobility, smart cities, etc.,   \n",
              "\n",
              "      label  \n",
              "242       1  \n",
              "632       1  \n",
              "738       1  \n",
              "753       0  \n",
              "949       0  \n",
              "1012      0  \n",
              "1534      0  \n",
              "1556      0  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#check statements with multiple company counts including green and non green claims // eg: EDF = British integrated energy company, French state-owned by EDF (Électricité de France)\n",
        "pd.set_option('display.max_colwidth', None)# setting wide column to display whole text\n",
        "company_name = \"EDF\"\n",
        "company_EDF = df_train[df_train[\"companies\"].apply(lambda x: company_name in x)]\n",
        "display(company_EDF[[\"text\",\"label\"]])\n",
        "\n",
        "pd.reset_option('display.max_colwidth') # resetting the column size to normal"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "There are several statements from some companies, while some are labelled as environmental claims others were not. "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Sentence content - year   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index([                                         [],\n",
            "                                ['1 January 2016'],\n",
            "                                       ['10 year'],\n",
            "                              ['11 years', '1978'],\n",
            "                                         ['16128'],\n",
            "                               ['1992', 'year 12'],\n",
            "                                 ['20 yearonyear'],\n",
            "                                          ['2004'],\n",
            "                              ['2005', 'annually'],\n",
            "                                          ['2007'],\n",
            "                       ['2007', '2007-12', '2009'],\n",
            "                                          ['2009'],\n",
            "                                          ['2010'],\n",
            "                                          ['2013'],\n",
            "                                        ['201314'],\n",
            "                                          ['2014'],\n",
            "                 ['2014', '2016', 'our base year'],\n",
            "                                      ['20142020'],\n",
            "                                          ['2015'],\n",
            "                          ['2015', '2016', '2007'],\n",
            "                                  ['2015', '2030'],\n",
            "                                          ['2016'],\n",
            "                                          ['2017'],\n",
            "                                        ['201718'],\n",
            "                                          ['2018'],\n",
            "                                          ['2019'],\n",
            "                         ['2019', '2018', '06891'],\n",
            "                       ['2019 93', '2018', '2019'],\n",
            "                                          ['2020'],\n",
            "                                  ['2020', '2013'],\n",
            "                                  ['2020', '2016'],\n",
            "                                  ['2020', '2019'],\n",
            "                                ['2020', 'annual'],\n",
            "                    ['2020', 'the period of 2005'],\n",
            "                                          ['2021'],\n",
            "                                  ['2021', '2015'],\n",
            "                          ['2021', '2015', '2018'],\n",
            "                        ['2021', 'the end of the'],\n",
            "                                     ['2021-2025'],\n",
            "                                          ['2025'],\n",
            "                                          ['2030'],\n",
            "                                          ['2050'],\n",
            "                               ['2050', 'October'],\n",
            "                                          ['2221'],\n",
            "                                  ['3050', '2025'],\n",
            "                           ['31 December 2015 71'],\n",
            "                      ['31 December 2020', '2017'],\n",
            "                                       ['40 2017'],\n",
            "                              ['46', '47', '2019'],\n",
            "                                    ['April 2019'],\n",
            "                                 ['February 2016'],\n",
            "                                ['January 1 2020'],\n",
            "                                     ['June 2018'],\n",
            "                                    ['March 2018'],\n",
            "                                ['September 2020'],\n",
            "                                     ['This year'],\n",
            "                                         ['Today'],\n",
            "                                        ['a year'],\n",
            "                                        ['annual'],\n",
            "                           ['annual', 'multiyear'],\n",
            "                                      ['annually'],\n",
            "                         ['between 2009 and 2020'],\n",
            "                         ['between 2013 and 2015'],\n",
            "       ['between December 2013 and December 2017'],\n",
            "                                         ['daily'],\n",
            "                                     ['each year'],\n",
            "                                   ['fiscal 2013'],\n",
            "                 ['fiscal 2019', '128873', '2019'],\n",
            "                                   ['fiscal 2030'],\n",
            "                                    ['five years'],\n",
            "                                    ['many years'],\n",
            "                         ['quarterly', 'annually'],\n",
            "                                  ['recent years'],\n",
            "                         ['the beginning of 2015'],\n",
            "                          ['the coming two years'],\n",
            "                              ['the coming years'],\n",
            "                       ['the end of 2015', '2010'],\n",
            "                       ['the end of 2015', '2014'],\n",
            "                           ['the end of the year'],\n",
            "                           ['the eve of the 21st'],\n",
            "                   ['the first quarter this year'],\n",
            "                     ['the middle of the century'],\n",
            "                       ['the next decade', '2030'],\n",
            "                           ['the next five years'],\n",
            "                            ['the past 18 months'],\n",
            "                           ['the past five years'],\n",
            "                             ['the previous year'],\n",
            "                                      ['the year'],\n",
            "                             ['the year', 'daily'],\n",
            "                                     ['the years'],\n",
            "                                     ['this year'],\n",
            "                                         ['today'],\n",
            "                    ['today', 'the past 10 years'],\n",
            "                                      ['tomorrow']],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Charlotte Ahrens\\AppData\\Local\\Temp\\ipykernel_19292\\857368648.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_train_env[\"year\"] = year\n"
          ]
        }
      ],
      "source": [
        "#check statements based on year-> did the count increase? This is difficult to show since the \n",
        "year = []\n",
        "for text in df_train_env[\"text\"]:\n",
        "    #remove punctuation\n",
        "    text = text.translate(str.maketrans(\"\",\"\", string.punctuation))\n",
        "    #change accented characters like é -> e\n",
        "    text = unidecode.unidecode(text)\n",
        "    doc = sp(text)\n",
        "    years = [ent.text for ent in doc.ents if ent.label_ == \"DATE\"]\n",
        "    #remove duplicates and empty strings\n",
        "    years = list(set([year.strip() for year in years if year.strip()]))\n",
        "    year.append(years)\n",
        "    \n",
        "df_train_env.copy()\n",
        "df_train_env[\"year\"] = year\n",
        "df_train_env\n",
        "year_counts = df_train_env[\"year\"].value_counts()\n",
        "print(year_counts.index.sort_values(ascending=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import re\n",
        "\n",
        "year_indexes = df_train_env[\"year\"].dropna().index\n",
        "\n",
        "# Extracting years starting from 1900and removing months and days\n",
        "years = [re.findall(r\"\\b(?:19|20|21)\\d{2}\\b\", str(year)) for year in df_train_env.loc[year_indexes, \"year\"]]\n",
        "filtered_years = [year[0] if year else \"\" for year in years]  # Keep the first year only as otherwise we cannot interpret the year\n",
        "\n",
        "# Assigning the filtered years back to the DataFrame\n",
        "df_train_env.loc[year_indexes, \"year\"] = filtered_years\n",
        "filtered_years\n",
        "\n",
        "# Counting the occurrences of each year\n",
        "year_counts = pd.Series(filtered_years).value_counts().sort_index()\n",
        "\n",
        "# Filtering years starting from 1900\n",
        "filtered_year_counts = year_counts[year_counts.index >= \"1900\"]\n",
        "\n",
        "# Plotting the line graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(filtered_year_counts.index, filtered_year_counts.values, marker='o')\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Count of environmental claims that state the given year\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n"
      ]
    },
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        "**The key takeaways of the Exploratory Data Analysis (EDA) are the following:**\n",
        "- the training and test set is quite unbalanced in terms of labelled=0 and labelled=1 data.\n",
        "- unlabelled data, meaning not categorized as an environmental claim doesn't mean it doesn't talk about environmental topics. \n",
        "- The most common words within the training set are as expected articles like (\"the\", \"a\"), prepositions (\"to\",\"of\",\"in\"), and various forms of the verbs (\"to be\",\" to have\"). It will be important to give those words less importance for example through the TF-IDF model. The most important word that gives a hint about the context is \"energy\", on rank 18th with a count of 290 words across all claims.\n",
        "\n",
        "- When selecting the claims that are coming from a specific company, we have four main observations:\n",
        "\n",
        "    1. within the 2400 claims the sample shows multiple claims from one company.\n",
        "    2. The company is not determining if a claim is labels as environmental or not since we find both labels within claims of the same company.\n",
        "    3. We only have the company's name for a minority of the claims. \n",
        "    4. Considering only environmental claims, we have multiple claims and the company name information only for EDF, an Energy firm in the UK, as well as for Tokio Marine Group, a multinational insurance holding company, based in Tokyo, Japan.\n",
        "- When selecting the claims that are specifying a given year, the main observations are: \n",
        "    1. 2015, 2019 and 2020 show a relative peak in environmental claims. 2015 is likely to be due to teh year of the Paris Agreement, meaning several claims where either made in that time or refer to that year within their statement. In terms of the rise in the year 2019 and 2020, it is likely to be mentioned more often due to the time period when the dataset was fed with claims.\n",
        "    2. Years that lay in the future are likely to come from claims that state information about future targets.\n",
        "    3. Due to the high amount of missing years for environmental claims, the line graph gives just an idea of behavior, but no concrete information."
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                  "<ipython-input-21-20ec121d96c5>:29: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
                  "  X_train_vect = np.array([np.array([w2v_model.wv[i] for i in ls if i in words])\n",
                  "<ipython-input-21-20ec121d96c5>:31: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
                  "  X_test_vect = np.array([np.array([w2v_model.wv[i] for i in ls if i in words])\n"
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                  " \n",
                  "The accuracy using Word2Vec and Logistic Regression as a classifier is: 79.00%\n"
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00EO57bbbct9992WvvfZKklx66aU58MADc+GFF6ahoWG96pA0AgBs5O688870798/O++8c4499ti8+OKLzdfmz5+fvn37NjeMSTJy5Mh06dIl99xzz3q/Q9IIANCBGhsb09jY2OJcfX196uvrN+h5o0ePzvjx4zNkyJA89thj+dKXvpQxY8Zk/vz56dq1axYtWpT+/fu3+Mxmm22Wfv36ZdGiRev9HkkjAFDzOnIhzNSpU9OnT58Wx9SpUze49kMPPTQf/ehHs8cee2TcuHGZNWtW7rvvvtx5551t9weKphEAoENNmTIlK1asaHFMmTKlzZ6/ww47ZKuttsqjjz6aJBk4cGBeeOGFFve8+uqrWbZs2RvOg3w9hqcBADpwIcxbGYpeH88880xefPHFDBo0KEkyYsSILF++PAsWLMiwYcOSJHfccUeampoyfPjw9X6uphEAoBNbtWpVc2qYJE888UQWLlyYfv36pV+/fjnrrLMyYcKEDBw4MI899lhOPfXU7Ljjjhk1alSSZNddd83o0aNz9NFHZ/r06Vm7dm2OP/74HHrooeu9cjrRNAIApNKJt9y5//77s//++zf/PHny5CTJxIkTc8UVV+T3v/99rrnmmixfvjwNDQ054IADcvbZZ7dIM6+77rocf/zx+dCHPpQuXbpkwoQJmTZtWqvqqKtUKq3fmryTW7v08WqXALST7g37VLsEoJ28uubZqr37xbH7dti7tvzx3A57V1uSNAIAdOKksbOwehoAgCJJIwBQ8zrznMbOQtIIAECRpBEAQNJYJGkEAKBI0ggA1DxzGsskjQAAFEkaAYCaJ2kskzQCAFAkaQQAap6ksUzSCABAkaQRAKBSV+0KOj1JIwAARZpGAACKDE8DADXPQpgySSMAAEWSRgCg5lWaLIQpkTQCAFAkaQQAap45jWWSRgAAiiSNAEDNq9jcu0jSCABAkaQRAKh55jSWSRoBACiSNAIANc8+jWWSRgAAiiSNAEDNq1SqXUHnJ2kEAKBI0ggA1DxzGsskjQAAFEkaAYCaJ2kskzQCAFCkaQQAoMjwNABQ82y5UyZpBACgSNIIANQ8C2HKJI0AABRJGgGAmlepSBpLJI0AABRJGgGAmldpqnYFnZ+kEQCAIkkjAFDzmsxpLJI0AgBQJGkEAGqe1dNlkkYAAIokjQBAzfONMGWSRgAAitYrabz11lvX+4Ef/ehHN7gYAIBqqFSqXUHnt15N47hx49brYXV1dVm3bt1bqQcAgE5ovZrGpibbpAMAmy5zGsvMaQQAoGiDVk+vXr06c+fOzVNPPZU1a9a0uPaFL3yhTQoDAOgovhGmrNVN429/+9sceOCBefnll7N69er069cvS5cuzRZbbJH+/ftrGgEANkGtHp6eNGlSxo4dmz//+c/p3r177r777vzpT3/KsGHDcuGFF7ZHjQAAVFmrm8aFCxfmpJNOSpcuXdK1a9c0NjZm2223zQUXXJAvfelL7VEjAEC7qlTqOuzYWLW6adx8883TpctfP9a/f/889dRTSZI+ffrk6aefbtvqAADoFFo9p/G9731v7rvvvrzzne/MvvvumzPOOCNLly7Nd7/73ey+++7tUSMAQLuyuXdZq5PG8847L4MGDUqSnHvuuXn729+eY489NkuWLMl///d/t3mBAABUX6uTxr322qv5n/v375/bbrutTQsCAOhottwps7k3AABFrU4ahwwZkrq6N+7GH3/88bdUEABAR9uYVzV3lFY3jSeeeGKLn9euXZvf/va3ue2223LKKae0VV0AAHQirW4av/jFL77u+csuuyz333//Wy4IAKCjWT1d1mZzGseMGZNbbrmlrR4HAEAn0uqk8Y3cfPPN6devX1s9DgCgw1g9XbZBm3v//UKYSqWSRYsWZcmSJbn88svbtDgAADqHVjeNBx98cIumsUuXLtl6662z3377ZZdddmnT4jbU5/c6rdolAO1EFgC0B6uny1rdNH71q19thzIAAOjMWr0QpmvXrnnhhRdec/7FF19M165d26QoAICO1FSp67BjY9XqprHyBmvSGxsb061bt7dcEAAAnc96D09PmzYtSVJXV5crr7wyPXv2bL62bt26zJs3r9PMaQQAaA3bNJatd9N48cUXJ/lr0jh9+vQWQ9HdunXL9ttvn+nTp7d9hQAAVN16N41PPPFEkmT//ffPD3/4w7z97W9vt6IAAOhcWr16+he/+EV71AEAUDUb8wKVjtLqhTATJkzI17/+9decv+CCC3LIIYe0SVEAAHQurW4a582blwMPPPA158eMGZN58+a1SVEAAB2pUqnrsGNj1eqmcdWqVa+7tc7mm2+elStXtklRAAB0Lq1uGvfYY4/ceOONrzn//e9/P7vttlubFAUA0JGaOvDYWLV6IcxXvvKVjB8/Po899lg++MEPJknmzJmT66+/PjfffHObFwgAQPW1umkcO3ZsZs6cmfPOOy8333xzunfvnqFDh+aOO+5Iv3792qNGAIB2VcnGO9ewo7S6aUySgw46KAcddFCSZOXKlbnhhhty8sknZ8GCBVm3bl2bFggAQPW1ek7j38ybNy8TJ05MQ0NDLrroonzwgx/M3Xff3Za1AQB0iKZKxx0bq1YljYsWLcqMGTNy1VVXZeXKlfnEJz6RxsbGzJw50yIYAIBN2HonjWPHjs3OO++c3//+97nkkkvy3HPP5dJLL23P2gAAOkRT6jrs2Fitd9L4s5/9LF/4whdy7LHH5p3vfGd71gQAQCez3knjXXfdlZdeeinDhg3L8OHD881vfjNLly5tz9oAADpEJXUddmys1rtp3HvvvfPtb387zz//fD772c/m+9//fhoaGtLU1JTZs2fnpZdeas86AQCoolavnu7Ro0eOPPLI3HXXXXnggQdy0kkn5fzzz0///v3z0Y9+tD1qBABoV74RpmyDt9xJkp133jkXXHBBnnnmmdxwww1tVRMAAJ3MBm3u/Y+6du2acePGZdy4cW3xOACADrUxzzXsKG8paQQAoDa0SdIIALAx25jnGnYUSSMAAEWaRgAAigxPAwA1z/B0maQRAIAiSSMAUPNsuVMmaQQAoEjSCADUvCZBY5GkEQCAIkkjAFDzmsxpLJI0AgBQJGkEAGpepdoFbAQkjQAAndi8efMyduzYNDQ0pK6uLjNnzmxxvVKp5IwzzsigQYPSvXv3jBw5Mo888kiLe5YtW5bDDz88vXv3Tt++fXPUUUdl1apVrapD0wgA1LymDjxaa/Xq1Rk6dGguu+yy171+wQUXZNq0aZk+fXruueee9OjRI6NGjcorr7zSfM/hhx+eBx98MLNnz86sWbMyb968HHPMMa2qw/A0AEAnNmbMmIwZM+Z1r1UqlVxyySX58pe/nIMPPjhJcu2112bAgAGZOXNmDj300Dz00EO57bbbct9992WvvfZKklx66aU58MADc+GFF6ahoWG96pA0AgA1r6mursOOtvTEE09k0aJFGTlyZPO5Pn36ZPjw4Zk/f36SZP78+enbt29zw5gkI0eOTJcuXXLPPfes97skjQAAHaixsTGNjY0tztXX16e+vr7Vz1q0aFGSZMCAAS3ODxgwoPnaokWL0r9//xbXN9tss/Tr16/5nvUhaQQAal6lA4+pU6emT58+LY6pU6d20G+64SSNAAAdaMqUKZk8eXKLcxuSMibJwIEDkySLFy/OoEGDms8vXrw473nPe5rveeGFF1p87tVXX82yZcuaP78+JI0AQM3ryNXT9fX16d27d4tjQ5vGIUOGZODAgZkzZ07zuZUrV+aee+7JiBEjkiQjRozI8uXLs2DBguZ77rjjjjQ1NWX48OHr/S5JIwBAJ7Zq1ao8+uijzT8/8cQTWbhwYfr165fBgwfnxBNPzDnnnJN3vvOdGTJkSL7yla+koaEh48aNS5LsuuuuGT16dI4++uhMnz49a9euzfHHH59DDz10vVdOJ5pGAIBO7f7778/+++/f/PPfhrYnTpyYGTNm5NRTT83q1atzzDHHZPny5Xn/+9+f2267LW9729uaP3Pdddfl+OOPz4c+9KF06dIlEyZMyLRp01pVR12lUtnkvjnn6O0PqXYJQDuZ8dyvq10C0E7Wrnm2au++oeHwDnvXYc9d12HvakvmNAIAUGR4GgCoeU1p2023N0WSRgAAiiSNAEDN2+QWeLQDSSMAAEWSRgCg5jWZ0lgkaQQAoEjSCADUvKZqF7ARkDQCAFAkaQQAap7V02WSRgAAiiSNAEDNs3q6TNIIAECRpBEAqHlWT5dJGgEAKJI0AgA1T9JYJmkEAKBI0ggA1LyK1dNFkkYAAIo0jQAAFBmeBgBqnoUwZZJGAACKJI0AQM2TNJZJGgEAKJI0AgA1r1LtAjYCkkYAAIokjQBAzWuyuXeRpBEAgCJJIwBQ86yeLpM0AgBQJGkEAGqepLFM0ggAQJGkEQCoefZpLJM0AgBQJGkEAGqefRrLJI0AABRJGgGAmmf1dJmkEQCAIk0jAABFhqcBgJpny50ySSMAAEWSRgCg5jXJGoskjQAAFEkaAYCaZ8udMkkjAABFkkYAoOaZ0VgmaQQAoEjSCADUPHMayySNAAAUSRoBgJrXVFftCjo/SSMAAEWSRgCg5vlGmDJJIwAARZJGAKDmyRnLJI0AABRJGgGAmmefxjJJIwAARZJGAKDmWT1dJmkEAKBI0wgAQJHhaQCg5hmcLpM0AgBQJGkEAGqeLXfKJI0AABRJGgGAmmfLnTJJIwAARZJGAKDmyRnLJI0AABRJGgGAmmf1dJmkEQCAIkkjAFDzKmY1FkkaAQAokjQCADXPnMYySSMAAEWSRgCg5vlGmDJJIwAARZJGAKDmyRnLJI0AABRpGgEAKDI8DQDUPAthyiSNAAAUSRrZKPQd0C8TTj88u+/33nTrXp8XnlyUGadclj898Hi6btY1404+NLvvt2e2Htw/f3np5Tx01wO55evXZcULf6526UArnXrq8fnYuDHZeecd85e/vJL5d9+fL33pvPzxj49VuzQ2YTb3LtM00ult0btHTrvl7Dw8/8F844jzsurFlek/ZGBeXrE6SdKte30Gv2uH/OTSm/P0Q39Kjz498n/O/HSOv/K0nPvR06tcPdBaH9hn71xxxTW5f8HCbLbZZjn7a6fnpz+5Pu8eul9efvkv1S4PapamkU5v9LHj8ufnXsyMUy5vPrf0mRea//kvL72ciz91dovP3HDGVfmPW89Pv4atsuy5pR1WK/DWfWTsJ1v8fNRnTszzzz2QPfd8d+66654qVcWmrmJOY5GmkU5v6Mi98uC8hfnsZZOz0/Ddsnzxstz53dvzy+/PecPPdO+1RZqamvLyytUdWCnQHvr06Z0k+fOfl1e3EKhxmkY6va0H989+nzwgs6+clZ9e/sNs/+4dc+hXj8yra1/N/Fvmvub+zeo3z4TTP5n7bv1VXlllKAs2ZnV1dbnowrPyq1/dmwcffLja5bAJM6exrFOvnn766adz5JFHvuk9jY2NWblyZYtjXWVdB1VIR6ir65I//e8T+dF/3pCnH3wyv7zh5/nlDT/Pvocf8Jp7u27WNZ/95uSkLvnel79dhWqBtnTptPPyrnftnMM/+flqlwI1r1M3jcuWLcs111zzpvdMnTo1ffr0aXEsXPGHDqqQjrDihT/n+UeeaXHu+ceeTb+GrVqc67pZ13z2ssnZ8h1b5eJPni1lhI3cNy45JwceODIfPuCQPPvs89Uuh01cpQP/s7Gq6vD0rbfe+qbXH3/88eIzpkyZksmTJ7c4d+IeR7yVsuhkHl3wcAbu0NDi3IAhg/Lis0uaf/5bw9h/+4G58LCzsnr5qo4uE2hD37jknBx88OiM/PAhefLJp6tdDpAqN43jxo1LXV1dKpU37rrr6ure9Bn19fWpr69vca5rXdc2qY/O4edXzcppt5yTAz//sdz3k/kZMnTHfOCwkfnulG8l+WvD+LkrTsrgdw3JpUedny5du6T31n2TJKuXr8q6ta9WsXqgtS6ddl4OPXRcxk84Mi+9tCoDBmydJFmx4qW88sorVa6OTZU5jWV1lTfr2NrZNttsk8svvzwHH3zw615fuHBhhg0blnXrWjdH8ejtD2mL8uhE3v3BPfOxUw/PgCEDs/TpFzL7ylnNq6e3fMfWOf+uy1/3c/956Jn5493/tyNLpZ3NeO7X1S6BdrZ2zbOve/6ooybl2u/e1MHV0JHe6L/7jjBx+wkd9q5rnrylw97VlqqaNA4bNiwLFix4w6axlEJSO35/x2/y+zt+87rXXnxmif9HATYhm3fbptolUIOa9BtFVW0aTznllKxe/cb76O244475xS9+0YEVAQDweqraNO6zzz5ver1Hjx7Zd999O6gaAKBWyRnLOvWWOwAAdA6+EQYAqHlNssYiSSMAAEWSRgCg5m3M39TSUSSNAAAUaRoBACgyPA0A1DxfI1gmaQQAoEjSCADUPFvulEkaAQAokjQCADXPljtlkkYAgE7qq1/9aurq6locu+yyS/P1V155Jccdd1y23HLL9OzZMxMmTMjixYvbpRZNIwBQ85o68Gitd73rXXn++eebj7vuuqv52qRJk/LjH/84P/jBDzJ37tw899xzGT9+/Aa8pczwNABAJ7bZZptl4MCBrzm/YsWKXHXVVbn++uvzwQ9+MEly9dVXZ9ddd83dd9+dvffeu03rkDQCADWvUql02NHY2JiVK1e2OBobG9+wtkceeSQNDQ3ZYYcdcvjhh+epp55KkixYsCBr167NyJEjm+/dZZddMnjw4MyfP7/N/0aaRgCADjR16tT06dOnxTF16tTXvXf48OGZMWNGbrvttlxxxRV54oknss8+++Sll17KokWL0q1bt/Tt27fFZwYMGJBFixa1ed2GpwGAmteR+zROmTIlkydPbnGuvr7+de8dM2ZM8z+/+93vzvDhw7PddtvlpptuSvfu3du1zn8kaQQA6ED19fXp3bt3i+ONmsZ/1Ldv3+y000559NFHM3DgwKxZsybLly9vcc/ixYtfdw7kW6VpBABqXmdePf33Vq1alcceeyyDBg3KsGHDsvnmm2fOnDnN1x9++OE89dRTGTFixFt802sZngYA6KROPvnkjB07Ntttt12ee+65nHnmmenatWsOO+yw9OnTJ0cddVQmT56cfv36pXfv3jnhhBMyYsSINl85nWgaAQA67TfCPPPMMznssMPy4osvZuutt8773//+3H333dl6662TJBdffHG6dOmSCRMmpLGxMaNGjcrll1/eLrXUVSqVzvlXeguO3v6QapcAtJMZz/262iUA7WTtmmer9u6PDD6ow94166mfdNi72pKkEQCoeR25enpjZSEMAABFmkYAAIoMTwMANW8TXOLR5iSNAAAUSRoBgJr3VjfdrgWSRgAAiiSNAEDN66ybe3cmkkYAAIokjQBAzbO5d5mkEQCAIkkjAFDz7NNYJmkEAKBI0ggA1DxzGsskjQAAFEkaAYCaZ5/GMkkjAABFkkYAoOY1WT1dJGkEAKBI0ggA1Dw5Y5mkEQCAIk0jAABFhqcBgJpnc+8ySSMAAEWSRgCg5kkayySNAAAUSRoBgJpXsbl3kaQRAIAiSSMAUPPMaSyTNAIAUCRpBABqXkXSWCRpBACgSNIIANQ8q6fLJI0AABRJGgGAmmf1dJmkEQCAIkkjAFDzzGkskzQCAFAkaQQAap45jWWSRgAAiiSNAEDN840wZZJGAACKNI0AABQZngYAal6TLXeKJI0AABRJGgGAmmchTJmkEQCAIkkjAFDzzGkskzQCAFAkaQQAap45jWWSRgAAiiSNAEDNM6exTNIIAECRpBEAqHnmNJZJGgEAKJI0AgA1z5zGMkkjAABFkkYAoOaZ01gmaQQAoEjSCADUvEqlqdoldHqSRgAAijSNAAAUGZ4GAGpek4UwRZJGAACKJI0AQM2r2Ny7SNIIAECRpBEAqHnmNJZJGgEAKJI0AgA1z5zGMkkjAABFkkYAoOY1SRqLJI0AABRJGgGAmlexerpI0ggAQJGkEQCoeVZPl0kaAQAokjQCADXPN8KUSRoBACiSNAIANc+cxjJJIwAARZJGAKDm+UaYMkkjAABFmkYAAIoMTwMANc9CmDJJIwAARZJGAKDm2dy7TNIIAECRpBEAqHnmNJZJGgEAKJI0AgA1z+beZZJGAACKJI0AQM2rWD1dJGkEAKBI0ggA1DxzGsskjQAAFEkaAYCaZ5/GMkkjAABFkkYAoOZZPV0maQQAoEjSCADUPHMayySNAAAUaRoBACgyPA0A1DzD02WSRgAAiiSNAEDNkzOWSRoBACiqqxjEZyPW2NiYqVOnZsqUKamvr692OUAb8u83dC6aRjZqK1euTJ8+fbJixYr07t272uUAbci/39C5GJ4GAKBI0wgAQJGmEQCAIk0jG7X6+vqceeaZJsnDJsi/39C5WAgDAECRpBEAgCJNIwAARZpGAACKNI0AABRpGtmoXXbZZdl+++3ztre9LcOHD8+9995b7ZKAt2jevHkZO3ZsGhoaUldXl5kzZ1a7JCCaRjZiN954YyZPnpwzzzwzv/nNbzJ06NCMGjUqL7zwQrVLA96C1atXZ+jQobnsssuqXQrwd2y5w0Zr+PDhed/73pdvfvObSZKmpqZsu+22OeGEE3L66adXuTqgLdTV1eVHP/pRxo0bV+1SoOZJGtkorVmzJgsWLMjIkSObz3Xp0iUjR47M/Pnzq1gZAGyaNI1slJYuXZp169ZlwIABLc4PGDAgixYtqlJVALDp0jQCAFCkaWSjtNVWW6Vr165ZvHhxi/OLFy/OwIEDq1QVAGy6NI1slLp165Zhw4Zlzpw5zeeampoyZ86cjBgxooqVAcCmabNqFwAbavLkyZk4cWL22muv/PM//3MuueSSrF69Op/+9KerXRrwFqxatSqPPvpo889PPPFEFi5cmH79+mXw4MFVrAxqmy132Kh985vfzH/+539m0aJFec973pNp06Zl+PDh1S4LeAvuvPPO7L///q85P3HixMyYMaPjCwKSaBoBAFgP5jQCAFCkaQQAoEjTCABAkaYRAIAiTSMAAEWaRgAAijSNAAAUaRqBTuuII47IuHHjmn/eb7/9cuKJJ3Z4HXfeeWfq6uqyfPnyDn83QGehaQRa7YgjjkhdXV3q6urSrVu37Ljjjvna176WV199tV3f+8Mf/jBnn332et2r0QNoW757Gtggo0ePztVXX53Gxsb89Kc/zXHHHZfNN988U6ZMaXHfmjVr0q1btzZ5Z79+/drkOQC0nqQR2CD19fUZOHBgtttuuxx77LEZOXJkbr311uYh5XPPPTcNDQ3ZeeedkyRPP/10PvGJT6Rv377p169fDj744Dz55JPNz1u3bl0mT56cvn37Zsstt8ypp56af/yW038cnm5sbMxpp52WbbfdNvX19dlxxx1z1VVX5cknn2z+7uK3v/3tqauryxFHHJEkaWpqytSpUzNkyJB07949Q4cOzc0339ziPT/96U+z0047pXv37tl///1b1AlQqzSNQJvo3r171qxZkySZM2dOHn744cyePTuzZs3K2rVrM2rUqPTq1Su//OUv86tf/So9e/bM6NGjmz9z0UUXZcaMGfnOd76Tu+66K8uWLcuPfvSjN33nv//7v+eGG27ItGnT8tBDD+Vb3/pWevbsmW233Ta33HJLkuThhx/O888/n2984xtJkqlTp+baa6/N9OnT8+CDD2bSpEn55Cc/mblz5yb5a3M7fvz4jB07NgsXLsxnPvOZnH766e31ZwPYaBieBt6SSqWSOXPm5Pbbb88JJ5yQJUuWpEePHrnyyiubh6W/973vpampKVdeeWXq6uqSJFdffXX69u2bO++8MwcccEAuueSSTJkyJePHj0+STJ8+PbfffvsbvvePf/xjbrrppsyePTsjR45Mkuywww7N1/82lN2/f//07ds3yV+TyfPOOy8///nPM2LEiObP3HXXXfnWt76VfffdN1dccUX+6Z/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                  "text/plain": "<Figure size 800x700 with 2 Axes>"
                },
                "metadata": {},
                "output_type": "display_data"
              }
            ]
          }
        },
        "2feaf89dba594746ba9eb76b631cb277": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
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\n",
                  "text/plain": "<Figure size 800x700 with 2 Axes>"
                },
                "metadata": {},
                "output_type": "display_data"
              }
            ]
          }
        },
        "e004131772b741d9ab0d34bb75f90bfe": {
          "model_module": "@jupyter-widgets/output",
          "model_module_version": "1.0.0",
          "model_name": "OutputModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_2feaf89dba594746ba9eb76b631cb277",
            "msg_id": "",
            "outputs": [
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.keyedvectors:sorting after vectors have been allocated is expensive & error-prone\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 0: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 1: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 2: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 3: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 4: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 5: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 6: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 7: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 8: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 9: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 10: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 11: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 12: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 13: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 14: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 15: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 16: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 17: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 18: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 19: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 20: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 21: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 22: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 23: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 24: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 25: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 26: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 27: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 28: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 29: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 30: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 31: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 32: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 33: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 34: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 35: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 36: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 37: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 38: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 39: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 40: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 41: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 42: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 43: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 44: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 45: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 46: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 47: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 48: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 49: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 50: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 51: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 52: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 53: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 54: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 55: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 56: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 57: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 58: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 59: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 60: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 61: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 62: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 63: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 64: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 65: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 66: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 67: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 68: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 69: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 70: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 71: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 72: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 73: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 74: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 75: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 76: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 77: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 78: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 79: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 80: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 81: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 82: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 83: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 84: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 85: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 86: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 87: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 88: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 89: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 90: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 91: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 92: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 93: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 94: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 95: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 96: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 97: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 98: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 99: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 100: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 101: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 102: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 103: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 104: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 105: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 106: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 107: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 108: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 109: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 110: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 111: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 112: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 113: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 114: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 115: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 116: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 117: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 118: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 119: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 120: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 121: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 122: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 123: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 124: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 125: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 126: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 127: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 128: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 129: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 130: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 131: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 132: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 133: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 134: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 135: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 136: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 137: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 138: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 139: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 140: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 141: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 142: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 143: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 144: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 145: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 146: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 147: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 148: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 149: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 150: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 151: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 152: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 153: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 154: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 155: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 156: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 157: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 158: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 159: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 160: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 161: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 162: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 163: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 164: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 165: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 166: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 167: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 168: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 169: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 170: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 171: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 172: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 173: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 174: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 175: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 176: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 177: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 178: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 179: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 180: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 181: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 182: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 183: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 184: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 185: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 186: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 187: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 188: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 189: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 190: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 191: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 192: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 193: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 194: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 195: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 196: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 197: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 198: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 199: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 200: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 201: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 202: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 203: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 204: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 205: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 206: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 207: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 208: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 209: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 210: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 211: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 212: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 213: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 214: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 215: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 216: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 217: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 218: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 219: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 220: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 221: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 222: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 223: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 224: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 225: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 226: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 227: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 228: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 229: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 230: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 231: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 232: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 233: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 234: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
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                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 235: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
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                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 236: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 237: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 238: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 239: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 240: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 241: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 242: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 243: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 244: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 245: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 246: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 247: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 248: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 249: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 250: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 251: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 252: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 253: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 254: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 255: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 256: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 257: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 258: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 259: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 260: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 261: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 262: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 263: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 264: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 265: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 266: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 267: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 268: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 269: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 270: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 271: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 272: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 273: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 274: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 275: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 276: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 277: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 278: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 279: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 280: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 281: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 282: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 283: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 284: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 285: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 286: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 287: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 288: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 289: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 290: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 291: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 292: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 293: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 294: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 295: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 296: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 297: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 298: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stderr",
                "output_type": "stream",
                "text": [
                  "WARNING:gensim.models.word2vec:EPOCH 299: supplied example count (2400) did not equal expected count (300)\n"
                ]
              },
              {
                "name": "stdout",
                "output_type": "stream",
                "text": [
                  "The accuracy is: 80.00%\n",
                  "The accuracy using Doc2Vec and Logistic Regression as a classifier is: 80.00%\n"
                ]
              },
              {
                "data": {
                  "image/png": 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                },
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