fastai.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "swiftai.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "rLh2R_u4Dfmf"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "swift",
      "display_name": "Swift"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zeionara/fastai-text-classification/blob/master/fastai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LEU2QCP3j71L",
        "colab_type": "text"
      },
      "source": [
        "## Set up environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Joz_1ZJXitdQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import Foundation\n",
        "\n",
        "func shell(_ command: String) -> String {\n",
        "    let task = Process()\n",
        "    task.launchPath = \"/bin/bash\"\n",
        "    task.arguments = [\"-c\", command]\n",
        "\n",
        "    let pipe = Pipe()\n",
        "    task.standardOutput = pipe\n",
        "    task.launch()\n",
        "\n",
        "    let data = pipe.fileHandleForReading.readDataToEndOfFile()\n",
        "    let output: String = NSString(data: data, encoding: String.Encoding.utf8.rawValue)! as String\n",
        "\n",
        "    return output\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsM6tYwyiWnN",
        "colab_type": "code",
        "outputId": "d8216ea2-20c2-48c7-c25b-0d06a5b8e8b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        }
      },
      "source": [
        "shell(\"git clone https://zeionara:access-key-here@github.com/zeionara/fastai-text-classification.git\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'fastai-text-classification'...\n",
            "remote: Enumerating objects: 55, done.        \n",
            "remote: Counting objects: 100% (55/55), done.        \n",
            "remote: Compressing objects: 100% (43/43), done.        \n",
            "remote: Total 55 (delta 13), reused 52 (delta 10), pack-reused 0        \n",
            "Unpacking objects: 100% (55/55), done.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\"\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnsYznVujeX9",
        "colab_type": "code",
        "outputId": "d606e13a-598b-45cb-fd07-0b1c8c27c0b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "shell(\"pip install pandas numpy fastai seaborn\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (1.0.3)\\nRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.18.3)\\nRequirement already satisfied: fastai in /usr/local/lib/python3.6/dist-packages (1.0.60)\\nRequirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (0.10.1)\\nRequirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.1)\\nRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\\nRequirement already satisfied: spacy>=2.0.18 in /usr/local/lib/python3.6/dist-packages (from fastai) (2.2.4)\\nRequirement already satisfied: fastprogress>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from fastai) (0.2.3)\\nRequirement already satisfied: dataclasses; python_version < \\\"3.7\\\" in /usr/local/lib/python3.6/dist-packages (from fastai) (0.7)\\nRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from fastai) (3.2.1)\\nRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from fastai) (2.23.0)\\nRequirement already satisfied: nvidia-ml-py3 in /usr/local/lib/python3.6/dist-packages (from fastai) (7.352.0)\\nRequirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from fastai) (20.3)\\nRequirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from fastai) (7.0.0)\\nRequirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from fastai) (1.5.0+cu101)\\nRequirement already satisfied: bottleneck in /usr/local/lib/python3.6/dist-packages (from fastai) (1.3.2)\\nRequirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from fastai) (1.4.1)\\nRequirement already satisfied: numexpr in /usr/local/lib/python3.6/dist-packages (from fastai) (2.7.1)\\nRequirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.6/dist-packages (from fastai) (4.6.3)\\nRequirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from fastai) (3.13)\\nRequirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (from fastai) (0.6.0+cu101)\\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\\nRequirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (0.6.0)\\nRequirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (2.0.3)\\nRequirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (1.1.3)\\nRequirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (7.4.0)\\nRequirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (4.38.0)\\nRequirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (3.0.2)\\nRequirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (1.0.2)\\nRequirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (46.1.3)\\nRequirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (0.4.1)\\nRequirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (1.0.2)\\nRequirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (1.0.0)\\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (1.2.0)\\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (0.10.0)\\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (2.4.7)\\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (2020.4.5.1)\\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (1.24.3)\\nRequirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (3.0.4)\\nRequirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (2.9)\\nRequirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.0.0->fastai) (0.16.0)\\nRequirement already satisfied: importlib-metadata>=0.20; python_version < \\\"3.8\\\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.0.18->fastai) (1.6.0)\\nRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \\\"3.8\\\"->catalogue<1.1.0,>=0.0.7->spacy>=2.0.18->fastai) (3.1.0)\\n\"\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QabbGanul_eu",
        "colab_type": "code",
        "outputId": "6437c197-c1fc-4ed3-a108-5abfb37c3dd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "// This cell is here to display plots inside a Jupyter Notebook.\n",
        "// Do not copy it into another environment.\n",
        "%include \"EnableIPythonDisplay.swift\"\n",
        "IPythonDisplay.shell.enable_matplotlib(\"inline\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('inline', 'module://ipykernel.pylab.backend_inline')\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVp6yI3kkDmn",
        "colab_type": "text"
      },
      "source": [
        "## Create base classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqcTGo77tzJW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let plt = Python.import(\"matplotlib.pyplot\")\n",
        "\n",
        "func draw_plot(ys: PythonObject, xs: PythonObject?, x_label: String, y_label: String, title: String, xscale: String? = nil, figsize: Array<Int> = [25, 10]){\n",
        "  plt.figure(figsize: figsize)\n",
        "\n",
        "  if let xs = xs{\n",
        "    plt.plot(xs, ys)\n",
        "  } else {\n",
        "    plt.plot(ys)\n",
        "  }\n",
        "\n",
        "  if let xscale = xscale{\n",
        "    plt.xscale(xscale)\n",
        "  }\n",
        "\n",
        "  plt.xlabel(x_label)\n",
        "  plt.ylabel(y_label)\n",
        "  plt.title(title)\n",
        "\n",
        "  plt.show()\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fj-bsokSkseu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "// comment so that Colab does not interpret `#if ...` as a comment\n",
        "#if canImport(PythonKit)\n",
        "    import PythonKit\n",
        "#else\n",
        "    import Python\n",
        "#endif\n",
        "\n",
        "let pd = Python.import(\"pandas\")\n",
        "\n",
        "class DataWrangler {\n",
        "    let csv_path: String\n",
        "    let df: PythonObject\n",
        "\n",
        "    init(csv_path: String) {\n",
        "        self.csv_path = csv_path\n",
        "        df = pd.read_csv(csv_path, header: Python.None).rename(\n",
        "            columns: [0: \"mark\", 1: \"id\", 2: \"time\", 3: \"device\", 4: \"user\", 5: \"text\"]\n",
        "        )\n",
        "    }\n",
        "\n",
        "    func drop_redundant_columns(columns_to_retain: String...) -> PythonObject{\n",
        "        return df[columns_to_retain]\n",
        "    }\n",
        "\n",
        "    func describe(top_n: Int = 10, visualize: Bool = false) {\n",
        "        Python.print(#\"Data is read from file \"\\#(csv_path)\"\"#)\n",
        "        Python.print(\"Number of records: \\(df.shape[0])\")\n",
        "        Python.print(\"The first \\(top_n) records from the collection:\")\n",
        "        Python.print(df.head(top_n))\n",
        "        Python.print(\"The first \\(top_n) records after removing redundant columns:\")\n",
        "        let concise_df = drop_redundant_columns(columns_to_retain: \"mark\", \"text\")\n",
        "        Python.print(concise_df.head(top_n))\n",
        "        Python.print(\"Some additional info about datates:\")\n",
        "        Python.print(concise_df.info())\n",
        "        Python.print(\"Some statistics about marks:\")\n",
        "        Python.print(concise_df.mark.describe())\n",
        "        Python.print(\"Distribution of tweet lengths:\")\n",
        "        let lengths = concise_df.text.apply(Python.len).sort_values().reset_index(drop: true)\n",
        "        Python.print(lengths.describe())\n",
        "        if visualize{\n",
        "            draw_plot(ys: lengths, xs: nil, x_label: \"Tweet id\", y_label: \"Tweet length\", title: \"Tweet lengths\")\n",
        "        }\n",
        "    }\n",
        "\n",
        "    func save_csv(path: String) {\n",
        "        df.to_csv(path)\n",
        "    }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5vU5XN3rwS6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "// comment so that Colab does not interpret `#if ...` as a comment\n",
        "#if canImport(PythonKit)\n",
        "    import PythonKit\n",
        "#else\n",
        "    import Python\n",
        "#endif\n",
        "\n",
        "let ft = Python.import(\"fastai.text\")\n",
        "let np = Python.import(\"numpy\")\n",
        "\n",
        "class ModelWrapper {\n",
        "    let csv_path: String\n",
        "    let learner_kind: PythonObject\n",
        "    let batch_size: Int \n",
        "    let drop_mult: Double\n",
        "\n",
        "    var language_model_databunch: PythonObject?\n",
        "    var classifier_databunch: PythonObject?\n",
        "    var learner: PythonObject?\n",
        "    var recorder: PythonObject?\n",
        "    \n",
        "    init(csv_path: String, batch_size: Int = 48, learner_kind: PythonObject = ft.AWD_LSTM, drop_mult: Double = 0.3) {\n",
        "        self.csv_path = csv_path\n",
        "        self.batch_size = batch_size\n",
        "        self.learner_kind = learner_kind\n",
        "        self.drop_mult = drop_mult\n",
        "    }\n",
        "\n",
        "    func find_learning_rate_with_steepest_loss() -> Double? {\n",
        "        if let learner = learner {\n",
        "            learner.lr_find()\n",
        "            Python.print(\"Tested \\(Python.len(learner.recorder.lrs)) learning rates\")\n",
        "            let losses = Python.list(Python.map(Python.float, learner.recorder.losses))\n",
        "            draw_plot(\n",
        "              ys: losses,\n",
        "              xs: learner.recorder.lrs,\n",
        "              x_label: \"Learning rates\",\n",
        "              y_label: \"Losses\",\n",
        "              title: \"Tested learning rates with respective losses\",\n",
        "              xscale: \"log\"\n",
        "            )\n",
        "            return Double(learner.recorder.lrs[np.argmax(np.subtract(losses[0..<losses.endIndex - 1], losses[1...]))])\n",
        "            // for lr in learner.recorder.lrs {\n",
        "            //     Python.print(lr)\n",
        "            // }\n",
        "            //learner.recorder.plot()\n",
        "        } else {\n",
        "            Python.print(\"Learner is not yet initialized\")\n",
        "        }\n",
        "        return nil\n",
        "    }\n",
        "\n",
        "    func plot_learning_rates() {\n",
        "      draw_plot(\n",
        "        ys: learner!.recorder.losses,\n",
        "        xs: learner!.recorder.lrs,\n",
        "        x_label: \"Learning rates\",\n",
        "        y_label: \"Losses\",\n",
        "        title: \"Tested learning rates with respective losses\"\n",
        "      )\n",
        "    }\n",
        "\n",
        "    func freeze(n_layers: Int) {\n",
        "        if let learner = learner {\n",
        "            if n_layers == 0 {\n",
        "                learner.unfreeze()\n",
        "            } else {\n",
        "                learner.freeze_to(n_layers)\n",
        "            }\n",
        "        } else {\n",
        "            Python.print(\"Learner is not yet initialized\")\n",
        "        }\n",
        "    }\n",
        "\n",
        "    func fit(n_cycles: Int = 5, learning_rate: Double = 0.1, momentums: [Double] = [0.8, 0.7]) {\n",
        "        learner!.fit_one_cycle(n_cycles, learning_rate, moms: momentums)\n",
        "    }\n",
        "\n",
        "    func init_language_model() {\n",
        "        language_model_databunch = ft.TextList.from_csv(\".\", csv_path, cols: \"text\").split_by_rand_pct(seed: 17).label_for_lm().databunch()\n",
        "        learner = ft.language_model_learner(language_model_databunch, learner_kind, drop_mult: drop_mult)\n",
        "    }\n",
        "\n",
        "    func init_classifier() {\n",
        "        classifier_databunch = ft.TextList.from_csv(\".\", csv_path, cols: \"text\", vocab: language_model_databunch!.vocab).split_by_rand_pct(seed: 17)\n",
        "        .label_from_df(cols: \"mark\").databunch(bs: batch_size)\n",
        "        learner = ft.text_classifier_learner(classifier_databunch!, learner_kind, drop_mult: drop_mult)\n",
        "    }\n",
        "\n",
        "    func save(path: String, just_encoder: Bool = false) {\n",
        "        if let learner = learner {\n",
        "            if just_encoder {\n",
        "                learner.save_encoder(path)\n",
        "            } else {\n",
        "                learner.save(path)\n",
        "            }\n",
        "        } else {\n",
        "            Python.print(\"Learner is not yet initialized\")\n",
        "        }\n",
        "    }\n",
        "\n",
        "    func load(path: String, just_encoder: Bool = false) {\n",
        "        if let learner = learner {\n",
        "            if just_encoder {\n",
        "                learner.load_encoder(path)\n",
        "            } else {\n",
        "                learner.load(path)\n",
        "            }\n",
        "        } else {\n",
        "            Python.print(\"Learner is not yet initialized\")\n",
        "        }\n",
        "    }\n",
        "\n",
        "    func interpret(visualize: Bool = false) {\n",
        "        let interpretation = ft.ClassificationInterpretation.from_learner(learner)\n",
        "        if visualize {\n",
        "          interpretation.plot_confusion_matrix()\n",
        "        } else {\n",
        "          Python.print(\"Confusion matrix:\")\n",
        "          Python.print(interpretation.confusion_matrix())\n",
        "          Python.print(\"Most confused classes:\")\n",
        "          Python.print(interpretation.most_confused())\n",
        "        }\n",
        "        for sentence in [\"This movie is really nice!!!!! haven't seen anything like this before :3\", \"OMG WHAT WAS THAT???\"]{\n",
        "            Python.print(#\"Prediction on sentence \"\\#(sentence)\":\"#)\n",
        "            Python.print(learner!.predict(sentence))\n",
        "        }\n",
        "    }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "St_MK63ck0DP",
        "colab_type": "text"
      },
      "source": [
        "## Experiment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUfa-iwpk62d",
        "colab_type": "text"
      },
      "source": [
        "### Play around with input data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_hII1GQk_oQ",
        "colab_type": "code",
        "outputId": "b16b00c7-4fed-4db3-967b-e74ca793957e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "// A smaller dataset\n",
        "let wrangler = DataWrangler(csv_path: \"fastai-text-classification/Resources/data.csv\")\n",
        "wrangler.describe(top_n: 20, visualize: true)\n",
        "//wrangler.save_csv(path: \"fastai-text-classification/Resources/data-with-header.csv\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data is read from file \"fastai-text-classification/Resources/data.csv\"\r\n",
            "Number of records: 498\r\n",
            "The first 20 records from the collection:\r\n",
            "    mark  ...                                               text\r\n",
            "0      4  ...  @stellargirl I loooooooovvvvvveee my Kindle2. ...\r\n",
            "1      4  ...  Reading my kindle2...  Love it... Lee childs i...\r\n",
            "2      4  ...  Ok, first assesment of the #kindle2 ...it fuck...\r\n",
            "3      4  ...  @kenburbary You'll love your Kindle2. I've had...\r\n",
            "4      4  ...  @mikefish  Fair enough. But i have the Kindle2...\r\n",
            "5      4  ...  @richardebaker no. it is too big. I'm quite ha...\r\n",
            "6      0  ...  Fuck this economy. I hate aig and their non lo...\r\n",
            "7      4  ...                      Jquery is my new best friend.\r\n",
            "8      4  ...                                      Loves twitter\r\n",
            "9      4  ...  how can you not love Obama? he makes jokes abo...\r\n",
            "10     2  ...  Check this video out -- President Obama at the...\r\n",
            "11     0  ...  @Karoli I firmly believe that Obama/Pelosi hav...\r\n",
            "12     4  ...  House Correspondents dinner was last night who...\r\n",
            "13     4  ...  Watchin Espn..Jus seen this new Nike Commerica...\r\n",
            "14     0  ...  dear nike, stop with the flywire. that shit is...\r\n",
            "15     4  ...  #lebron best athlete of our generation, if not...\r\n",
            "16     0  ...  I was talking to this guy last night and he wa...\r\n",
            "17     4  ...                 i love lebron. http://bit.ly/PdHur\r\n",
            "18     0  ...  @ludajuice Lebron is a Beast, but I'm still ch...\r\n",
            "19     4  ...                        @Pmillzz lebron IS THE BOSS\r\n",
            "\r\n",
            "[20 rows x 6 columns]\r\n",
            "The first 20 records after removing redundant columns:\r\n",
            "    mark                                               text\r\n",
            "0      4  @stellargirl I loooooooovvvvvveee my Kindle2. ...\r\n",
            "1      4  Reading my kindle2...  Love it... Lee childs i...\r\n",
            "2      4  Ok, first assesment of the #kindle2 ...it fuck...\r\n",
            "3      4  @kenburbary You'll love your Kindle2. I've had...\r\n",
            "4      4  @mikefish  Fair enough. But i have the Kindle2...\r\n",
            "5      4  @richardebaker no. it is too big. I'm quite ha...\r\n",
            "6      0  Fuck this economy. I hate aig and their non lo...\r\n",
            "7      4                      Jquery is my new best friend.\r\n",
            "8      4                                      Loves twitter\r\n",
            "9      4  how can you not love Obama? he makes jokes abo...\r\n",
            "10     2  Check this video out -- President Obama at the...\r\n",
            "11     0  @Karoli I firmly believe that Obama/Pelosi hav...\r\n",
            "12     4  House Correspondents dinner was last night who...\r\n",
            "13     4  Watchin Espn..Jus seen this new Nike Commerica...\r\n",
            "14     0  dear nike, stop with the flywire. that shit is...\r\n",
            "15     4  #lebron best athlete of our generation, if not...\r\n",
            "16     0  I was talking to this guy last night and he wa...\r\n",
            "17     4                 i love lebron. http://bit.ly/PdHur\r\n",
            "18     0  @ludajuice Lebron is a Beast, but I'm still ch...\r\n",
            "19     4                        @Pmillzz lebron IS THE BOSS\r\n",
            "Some additional info about datates:\r\n",
            "<class 'pandas.core.frame.DataFrame'>\r\n",
            "RangeIndex: 498 entries, 0 to 497\r\n",
            "Data columns (total 2 columns):\r\n",
            " #   Column  Non-Null Count  Dtype \r\n",
            "---  ------  --------------  ----- \r\n",
            " 0   mark    498 non-null    int64 \r\n",
            " 1   text    498 non-null    object\r\n",
            "dtypes: int64(1), object(1)\r\n",
            "memory usage: 7.9+ KB\r\n",
            "None\r\n",
            "Some statistics about marks:\r\n",
            "count    498.000000\r\n",
            "mean       2.020080\r\n",
            "std        1.699686\r\n",
            "min        0.000000\r\n",
            "25%        0.000000\r\n",
            "50%        2.000000\r\n",
            "75%        4.000000\r\n",
            "max        4.000000\r\n",
            "Name: mark, dtype: float64\r\n",
            "Distribution of tweet lengths:\n",
            "count    498.000000\n",
            "mean      80.411647\n",
            "std       35.790319\n",
            "min       11.000000\n",
            "25%       53.250000\n",
            "50%       75.000000\n",
            "75%      108.000000\n",
            "max      144.000000\n",
            "Name: text, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "delWcxOaq8nk",
        "colab_type": "code",
        "outputId": "1e4f58cd-dd36-44bd-9c64-3ad861b58a64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "// A larger dataset\n",
        "let wrangler = DataWrangler(csv_path: \"fastai-text-classification/Resources/corpus.csv\")\n",
        "wrangler.describe(top_n: 20, visualize: true)\n",
        "wrangler.save_csv(path: \"fastai-text-classification/Resources/corpus-with-header.csv\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data is read from file \"fastai-text-classification/Resources/corpus.csv\"\r\n",
            "Number of records: 10000\r\n",
            "The first 20 records from the collection:\r\n",
            "    mark  ...                                               text\r\n",
            "0      0  ...  My nail polish fell and broke  now I have to g...\r\n",
            "1      0  ...  @missmei ooh sounds pretty bloody fab though. ...\r\n",
            "2      4  ...  I am going to go watch an epic sunrise and be ...\r\n",
            "3      0  ...  So work is over... nothing particularly intere...\r\n",
            "4      4  ...  @RachelHawley Both VB.Net and C# have compiler...\r\n",
            "5      4  ...  i just got twitter like 2 days ago and 21 peop...\r\n",
            "6      4  ...  psyching myself up for the next few days...oh ...\r\n",
            "7      4  ...  will watch Season 5 of CSI:NY later.  WHOO. La...\r\n",
            "8      0  ...                         Not feeling soo gooodd... \r\n",
            "9      0  ...  jailbreaking.. again. my stupid visual voicema...\r\n",
            "10     0  ...  Mudah2an malam ini bisa tidur tenang....  good...\r\n",
            "11     0  ...  @fab4_girl hi how are you? i've been watching ...\r\n",
            "12     0  ...      @princessnatt: sorry we can't go...  Ily tho!\r\n",
            "13     4  ...                    watching my favorite t.v. show \r\n",
            "14     0  ...  Wish I felt better, would really love an orien...\r\n",
            "15     0  ...       @dellibedaboss lol, iKno  whats goin on tho?\r\n",
            "16     4  ...               @rastin right on, I'll refrain then \r\n",
            "17     4  ...  @stef13191 haha. no. i dont have a stinkin dri...\r\n",
            "18     4  ...  @MissNickyHilton Follow me ;) and then maybe P...\r\n",
            "19     4  ...  @JustineDevine Beauty is Pain my girl - embrac...\r\n",
            "\r\n",
            "[20 rows x 6 columns]\r\n",
            "The first 20 records after removing redundant columns:\n",
            "    mark                                               text\n",
            "0      0  My nail polish fell and broke  now I have to g...\n",
            "1      0  @missmei ooh sounds pretty bloody fab though. ...\n",
            "2      4  I am going to go watch an epic sunrise and be ...\n",
            "3      0  So work is over... nothing particularly intere...\n",
            "4      4  @RachelHawley Both VB.Net and C# have compiler...\n",
            "5      4  i just got twitter like 2 days ago and 21 peop...\n",
            "6      4  psyching myself up for the next few days...oh ...\n",
            "7      4  will watch Season 5 of CSI:NY later.  WHOO. La...\n",
            "8      0                         Not feeling soo gooodd... \n",
            "9      0  jailbreaking.. again. my stupid visual voicema...\n",
            "10     0  Mudah2an malam ini bisa tidur tenang....  good...\n",
            "11     0  @fab4_girl hi how are you? i've been watching ...\n",
            "12     0      @princessnatt: sorry we can't go...  Ily tho!\n",
            "13     4                    watching my favorite t.v. show \n",
            "14     0  Wish I felt better, would really love an orien...\n",
            "15     0       @dellibedaboss lol, iKno  whats goin on tho?\n",
            "16     4               @rastin right on, I'll refrain then \n",
            "17     4  @stef13191 haha. no. i dont have a stinkin dri...\n",
            "18     4  @MissNickyHilton Follow me ;) and then maybe P...\n",
            "19     4  @JustineDevine Beauty is Pain my girl - embrac...\n",
            "Some additional info about datates:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   mark    10000 non-null  int64 \n",
            " 1   text    10000 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 156.4+ KB\n",
            "None\n",
            "Some statistics about marks:\n",
            "count    10000.000000\n",
            "mean         1.981200\n",
            "std          2.000012\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          4.000000\n",
            "max          4.000000\n",
            "Name: mark, dtype: float64\n",
            "Distribution of tweet lengths:\n",
            "count    10000.000000\n",
            "mean        73.728100\n",
            "std         36.284912\n",
            "min          6.000000\n",
            "25%         43.000000\n",
            "50%         69.000000\n",
            "75%        103.000000\n",
            "max        158.000000\n",
            "Name: text, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1-UzVSusz6P",
        "colab_type": "text"
      },
      "source": [
        "### Train a language model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g58UEI1DtV3l",
        "colab_type": "text"
      },
      "source": [
        "#### On a smaller corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ubR_m8PtBKD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let wrapper = ModelWrapper(csv_path: \"fastai-text-classification/Resources/data-with-header.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1Tls5xSs44a",
        "colab_type": "code",
        "outputId": "fa5ce822-a33a-47d7-81f4-edde3db3dc80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "wrapper.init_language_model()\n",
        "let learning_rate = wrapper.find_learning_rate_with_steepest_loss()!\n",
        "Python.print(\"Selected learning rate \\(learning_rate) for training language model\")"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\r\n",
            "Tested 100 learning rates\n",
            "Selected learning rate 0.36307805477010097 for training language model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='33' class='' max='34' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      97.06% [33/34 00:10<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.501871</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.514199</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.518678</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>4.515645</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>4.508852</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>4.514826</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>4.516809</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>4.517469</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>4.516786</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>4.516479</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>4.515363</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>4.516335</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>4.516406</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>4.513081</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>4.508955</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>4.501793</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>4.492317</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>4.477534</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>4.450293</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>4.410485</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>4.354885</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>4.279982</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>4.191551</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>4.092557</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>3.985260</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>3.872742</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>3.758009</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>3.642882</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>3.530791</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>3.457858</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>3.597595</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>4.268612</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>6.176699</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnwiLdoL9oId",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "14eaadb5-d418-4af8-c358-ea6ed1e7a3cd"
      },
      "source": [
        "wrapper.fit(n_cycles: 10, learning_rate: 0.36)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.144583</td>\n",
              "      <td>3.397795</td>\n",
              "      <td>0.333929</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.699426</td>\n",
              "      <td>3.996247</td>\n",
              "      <td>0.233259</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.606233</td>\n",
              "      <td>4.720909</td>\n",
              "      <td>0.223884</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.627211</td>\n",
              "      <td>4.294983</td>\n",
              "      <td>0.275446</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>3.485215</td>\n",
              "      <td>3.941498</td>\n",
              "      <td>0.298214</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>3.279298</td>\n",
              "      <td>3.734086</td>\n",
              "      <td>0.330804</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>3.073257</td>\n",
              "      <td>3.789089</td>\n",
              "      <td>0.322768</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.883824</td>\n",
              "      <td>3.792331</td>\n",
              "      <td>0.327679</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.716589</td>\n",
              "      <td>3.812927</td>\n",
              "      <td>0.327009</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.578379</td>\n",
              "      <td>3.822594</td>\n",
              "      <td>0.327232</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qk99ur0e-q2T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wrapper.save(path: \"data-language-model\", just_encoder: true)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aw5MVtNi_DWi",
        "colab_type": "text"
      },
      "source": [
        "#### On a larger corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1f9TfsFx--0z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let wrapper = ModelWrapper(csv_path: \"fastai-text-classification/Resources/corpus-with-header.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUuOXu7l_YIl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "outputId": "59e10fa4-1ca8-43f9-9bdc-efc2ac15533e"
      },
      "source": [
        "wrapper.init_language_model()\n",
        "let learning_rate = wrapper.find_learning_rate_with_steepest_loss()!\n",
        "Python.print(\"Selected learning rate \\(learning_rate) for training language model\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\r\n",
            "Tested 100 learning rates\n",
            "Selected learning rate 2.51188643150958e-07 for training language model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='2' class='' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      66.67% [2/3 00:07<00:03]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>5.678978</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>5.468406</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='31' class='' max='34' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      91.18% [31/34 00:03<00:00 9.0942]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HAWDNxe_yW1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "91c6ae4a-1be9-49c1-a947-7cefe33d13fc"
      },
      "source": [
        "wrapper.fit(n_cycles: 10, learning_rate: 0.05)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>4.927578</td>\n",
              "      <td>4.199533</td>\n",
              "      <td>0.215575</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.415164</td>\n",
              "      <td>4.065075</td>\n",
              "      <td>0.237525</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.171614</td>\n",
              "      <td>4.118848</td>\n",
              "      <td>0.236533</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.974279</td>\n",
              "      <td>4.154665</td>\n",
              "      <td>0.235764</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>3.771830</td>\n",
              "      <td>4.185728</td>\n",
              "      <td>0.236062</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>3.566675</td>\n",
              "      <td>4.221416</td>\n",
              "      <td>0.233061</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>3.372713</td>\n",
              "      <td>4.243018</td>\n",
              "      <td>0.233978</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>3.175062</td>\n",
              "      <td>4.260698</td>\n",
              "      <td>0.236458</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>3.014325</td>\n",
              "      <td>4.270618</td>\n",
              "      <td>0.236409</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.892120</td>\n",
              "      <td>4.271450</td>\n",
              "      <td>0.235813</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edjsEYn2_83u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wrapper.save(path: \"corpus-language-model\", just_encoder: true)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3r8l2yl-1D_",
        "colab_type": "text"
      },
      "source": [
        "### Train classifier\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWGR2CwXANUW",
        "colab_type": "text"
      },
      "source": [
        "#### On a smaller corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Om9k3yzTAC30",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let wrapper = ModelWrapper(csv_path: \"fastai-text-classification/Resources/data-with-header.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yzfy3OAMAX7F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b7834dbf-89ab-46e5-86c5-24dc6ef1d4c9"
      },
      "source": [
        "wrapper.init_language_model()\n",
        "wrapper.load(path: \"data-language-model\", just_encoder: true)\n",
        "wrapper.init_classifier()\n",
        "let learning_rate = wrapper.find_learning_rate_with_steepest_loss()!\n",
        "Python.print(\"Selected learning rate \\(learning_rate) for training classifier\")"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\r\n",
            "Tested 96 learning rates\n",
            "Selected learning rate 9.120108393559096e-07 for training classifier\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='11' class='' max='13' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      84.62% [11/13 00:04<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.215271</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.196770</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.196812</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.199955</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.200572</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.188349</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.165854</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.154242</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.135986</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.137666</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.187384</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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              "      62.50% [5/8 00:00<00:00 1.7991]\n",
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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Du0dtVwECdFX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "48f84509-3eef-48c6-f875-aa3fbe8e0673"
      },
      "source": [
        "wrapper.fit(n_cycles: 10, learning_rate: 9.120108393559096e-07)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.199314</td>\n",
              "      <td>1.101734</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.178170</td>\n",
              "      <td>1.107426</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.185542</td>\n",
              "      <td>1.118300</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.180994</td>\n",
              "      <td>1.133054</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.186087</td>\n",
              "      <td>1.142980</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.180737</td>\n",
              "      <td>1.151524</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.179826</td>\n",
              "      <td>1.153175</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.184779</td>\n",
              "      <td>1.150231</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.182848</td>\n",
              "      <td>1.141754</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.192356</td>\n",
              "      <td>1.132261</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gucatgzBCypj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "a66ce271-27eb-486b-8fea-c75e97ffd3dd"
      },
      "source": [
        "wrapper.freeze(n_layers: -3)\n",
        "wrapper.fit(n_cycles: 10, learning_rate: 9.120108393559096e-07)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.191897</td>\n",
              "      <td>1.120426</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.187998</td>\n",
              "      <td>1.110014</td>\n",
              "      <td>0.323232</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.191339</td>\n",
              "      <td>1.104063</td>\n",
              "      <td>0.343434</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.175472</td>\n",
              "      <td>1.090252</td>\n",
              "      <td>0.353535</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.175689</td>\n",
              "      <td>1.087884</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.185048</td>\n",
              "      <td>1.084683</td>\n",
              "      <td>0.353535</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.185108</td>\n",
              "      <td>1.092690</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.185077</td>\n",
              "      <td>1.089775</td>\n",
              "      <td>0.353535</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.196153</td>\n",
              "      <td>1.083273</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.186669</td>\n",
              "      <td>1.077133</td>\n",
              "      <td>0.393939</td>\n",
              "      <td>00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4K2EG6zC80j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "ba8d8b17-81e2-41bc-d0c1-0298c3698d96"
      },
      "source": [
        "wrapper.interpret(visualize: true)\n",
        "// necessary for displaying confusion matrix because of bugs in colab or swift\n",
        "draw_plot(ys: [0,1], xs: [0,1], x_label: \"\", y_label: \"\", title: \"\", figsize: [1,1])"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction on sentence \"This movie is really nice!!!!! haven't seen anything like this before :3\":\n",
            "(Category 4, tensor(2), tensor([0.2942, 0.2285, 0.4773]))\n",
            "Prediction on sentence \"OMG WHAT WAS THAT???\":\n",
            "(Category 4, tensor(2), tensor([0.2924, 0.2723, 0.4353]))\n",
            "[0, 1]\n",
            "2\n",
            "[0, 1]\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LiASnYhGR4FQsIhKcikVEglOxiEhwKhYRCU7FIiLBqVhEJDgVi4gEp2IRkeBULCISnIpFRIJTsYhIcCoWEQlOxSIiwalYRCQ4FYuIBKdiEZHgVCwiEpyKRUSCU7GISHAqFhEJTsUiIsGpWEQkOBWLiASnYhGR4FQsIhKcikVEglOxiEhwKhYRCU7FIiLBZccdINO0blaP4Vd8j5aN6+I4z0xdyqjJS7hlcDcu7HcoGzZvA+CXL81j8rw1MafNbFu3bmXIwBPYvn0bO4qLOW3IWdx2931xx0qEpO+7tBeLmWUB+cBKdz8t3dvbU8Ulzv0vzOWjFV/SICebiT89kanz1wIwYtJiHp+4KOaEyZGTk8NL4yfSoGFDioqKGHzyAE44aSB5PXvFHS3jJX3f7Y0jlhuBBUDjvbCtPbZu41bWbdwKwJZtxSxevYkDm9aLOVUymRkNGjYEoKioiOLiIsws5lTJkPR9l9YxFjNrC5wKjErndtLl4Ob16X5wU2Yv/RyAK47vwJv3fZ+HLz2aJvVrx5wuGXbs2MEJffM4rEMb+h9/Ikd/r2fckRIjyfsu3YO3jwC3ASUVLWBmQ80s38zyS7Z+leY4VVc/J4tR1x7DvePmsHlrMX+c8g963fU6Jz44ibUbt3L/2UfEHTERsrKyePOdfOYsWMrsWfksmD8v7kiJkeR9l7ZiMbPTgHXuPquy5dx9hLvnuXterbqZcbaUnWWMvvYYXpqxgtc+XAVAwaZtlDi4w3PTlnLkobkxp0yWJk2bcmy//rw1aWLcURInifsunUcsfYHTzWwZMBY4wcyeTeP2gnn40jwWr97Ek/+3eOe0Vk3q7nw+6Mg2fLIyc46uMlVBwXo2fvklAIWFhbz91mQ6duoSc6pkSPq+S9vgrbvfCdwJYGYDgFvc/aJ0bS+Unh2bc/YxhzD/sy+ZdO/3gdSl5TN6Hkz3g5viOJ8WfM2tz86OOWnmW7tmNTcMu5IdO3ZQUlLCkDN/wMmDTo07ViIkfd/pPpZdfLBkAwde/eK3puueleo7rPsRTJ4+M+4YiZT0fbdXisXdpwBT9sa2RCR+uqVfRIJTsYhIcCoWEQlOxSIiwalYRCQ4FYuIBKdiEZHgVCwiEpyKRUSCU7GISHAqFhEJTsUiIsGpWEQkOBWLiASnYhGR4FQsIhKcikVEglOxiEhwKhYRCU7FIiLBqVhEJDgVi4gEp2IRkeBULCISnIpFRIJTsYhIcCoWEQlOxSIiwalYRCQ4FYuIBKdiEZHgVCwiEpyKRUSCM3ePO8NOZrYeWB53jgq0AAriDpFQ2nc1l+n77hB3b7nrxIwqlkxmZvnunhd3jiTSvqu5pO47nQqJSHAqFhEJTsVSdSPiDpBg2nc1l8h9pzEWEQlORywiEpyKRUSCU7GISHDZcQfIVGbWFRgCtIkmrQRecfcF8aWS/Y2ZPe3ul8Sdo7pULOUws9uB84GxwAfR5LbAGDMb6+4PxRYuAaJSbgPMcPfNZaYPdPcJ8SXLbGb2yq6TgOPNrCmAu5++91PVjK4KlcPMFgGHuXvRLtPrAB+7e6d4kmU+M7sB+CGwAOgB3OjuL0fzZrv7UXHmy2RmNhuYD4wCnFSxjAHOA3D3t+NLVz0aYylfCdC6nOkHRfOkYlcDR7v7GcAA4KdmdmM0z2JLlQx5wCzgbmCju08BCt397SSVCuhUqCI3AZPNbDHwaTStHdAR+FFsqZKhVunpj7svM7MBwItmdggqlkq5ewnwsJm9EP25loR+RxMZOt3cfYKZdQZ68s3B25nuviO+ZImw1sx6uPscAHffbGanAU8Bh8cbLRnc/TPgbDM7Ffgq7jw1oTEWCcrM2gLF7r6mnHl93f2dGGLJXqZiEZHgNHgrIsGpWEQkOBWL7GRmO8xsjpnNM7MXzKz+Hqzrj2b2g+j5KDPrVsmyA8ysTw22sczMWtQ0o6SPikXKKnT3Hu7eHdgODCs708xqdBXR3a9y9/mVLDIAqHaxSOZSsUhFpgEdo6OJadHt5vPNLMvM/tvMZprZXDO7BsBSHjOzhWY2CWhVuiIzm2JmedHzgWY228z+bmaTzaw9qQK7OTpa6mdmLc3sL9E2ZppZ3+i9zc1sopl9bGaj0H0xGUv3sci3REcmg4DSn+s5Cuju7kvNbCipu0K/Z2Y5wDtmNhE4EugCdAMOIHVr+lO7rLclMBI4LlpXrrt/bmZPAJvd/dfRcs8DD7v7dDNrB7wBfBe4D5ju7g9G93hcmdYdITWmYpGy6pnZnOj5NGA0qVOUD9x9aTT9ZOCI0vEToAnQCTgOGBPdQLjKzN4sZ/29gaml63L3zyvI8X2gm9nOA5LGZtYw2sZZ0XtfNbMvavg5Jc1ULFJWobv3KDsh+nJvKTsJuN7d39hluVMC5qgF9Hb3reVkkQTQGItU1xvAtWZWG8DMOptZA2AqcG40BnMQcHw5730fOM7MDo3emxtN3wQ0KrPcROD60hdmVlp2U4ELommDgGbBPpUEpWKR6hpFavxktpnNA54kdeT7v8DiaN7TwHu7vtHd1wNDgZfM7O/AuGjW34AzSwdvgRuAvGhweD7/ujr1AKli+pjUKdGKNH1G2UO6pV9EgtMRi4gEp2IRkeBULCISnIpFRIJTsYhIcCoWEQlOxSIiwf0/7NDDnmgd/qcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXrsHxPxDo5F",
        "colab_type": "text"
      },
      "source": [
        "#### On a larger corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qk-vpDa6DoKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let wrapper = ModelWrapper(csv_path: \"fastai-text-classification/Resources/corpus-with-header.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzhowWLED0k6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "outputId": "5c196249-6f38-47fe-c45c-1625fd8761dd"
      },
      "source": [
        "wrapper.init_language_model()\n",
        "wrapper.load(path: \"corpus-language-model\", just_encoder: true)\n",
        "wrapper.init_classifier()\n",
        "let learning_rate = wrapper.find_learning_rate_with_steepest_loss()!\n",
        "Python.print(\"Selected learning rate \\(learning_rate) for training classifier\")"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\r\n",
            "Tested 96 learning rates\n",
            "Selected learning rate 1.7378008287493754e-07 for training classifier\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='91' class='' max='166' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      54.82% [91/166 00:03<00:02 0.9702]\n",
              "    </div>\n",
              "    "
            ],
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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeJmdIHsEOKC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "488e4006-9ca7-4dc2-c14b-175268ea7a8f"
      },
      "source": [
        "wrapper.freeze(n_layers: -3)\n",
        "wrapper.fit(n_cycles: 10, learning_rate: 1e-06)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.763038</td>\n",
              "      <td>0.761330</td>\n",
              "      <td>0.498000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.761139</td>\n",
              "      <td>0.750523</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.757604</td>\n",
              "      <td>0.746111</td>\n",
              "      <td>0.502500</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.748107</td>\n",
              "      <td>0.736105</td>\n",
              "      <td>0.509500</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.734433</td>\n",
              "      <td>0.721503</td>\n",
              "      <td>0.537500</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.732961</td>\n",
              "      <td>0.726952</td>\n",
              "      <td>0.519000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.746296</td>\n",
              "      <td>0.715582</td>\n",
              "      <td>0.548000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.734742</td>\n",
              "      <td>0.721908</td>\n",
              "      <td>0.523000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.727972</td>\n",
              "      <td>0.724821</td>\n",
              "      <td>0.523000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.722635</td>\n",
              "      <td>0.718254</td>\n",
              "      <td>0.530000</td>\n",
              "      <td>00:07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-27GQ-huExvn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "outputId": "f578b5c1-01e5-4bb3-b982-a897ed3661ae"
      },
      "source": [
        "let learning_rate = wrapper.find_learning_rate_with_steepest_loss()!\n",
        "Python.print(\"Selected learning rate \\(learning_rate) for training classifier\")"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\r\n",
            "Tested 90 learning rates\n",
            "Selected learning rate 1.202264434617413e-07 for training classifier\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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              "      51.20% [85/166 00:03<00:03 0.8045]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
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        },
        {
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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gq6bEJryErW1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "f61d51f9-7914-41ef-c28f-0547cae5a094"
      },
      "source": [
        "wrapper.freeze(n_layers: 0)\n",
        "wrapper.fit(n_cycles: 10, learning_rate: 1e-06)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.730555</td>\n",
              "      <td>0.711624</td>\n",
              "      <td>0.539000</td>\n",
              "      <td>00:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.733788</td>\n",
              "      <td>0.711065</td>\n",
              "      <td>0.547000</td>\n",
              "      <td>00:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.721625</td>\n",
              "      <td>0.715742</td>\n",
              "      <td>0.546000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.723468</td>\n",
              "      <td>0.705626</td>\n",
              "      <td>0.546500</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.729393</td>\n",
              "      <td>0.703172</td>\n",
              "      <td>0.554500</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.724003</td>\n",
              "      <td>0.701030</td>\n",
              "      <td>0.558000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.723258</td>\n",
              "      <td>0.705008</td>\n",
              "      <td>0.553000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.712999</td>\n",
              "      <td>0.700587</td>\n",
              "      <td>0.555500</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.706452</td>\n",
              "      <td>0.708733</td>\n",
              "      <td>0.554000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.711668</td>\n",
              "      <td>0.702412</td>\n",
              "      <td>0.557000</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSuy7RszEp5d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "d92ee63f-e5ca-4c47-d897-17cbb9f82cff"
      },
      "source": [
        "wrapper.interpret(visualize: true)\n",
        "// necessary for displaying confusion matrix because of bugs in colab or swift\n",
        "draw_plot(ys: [0,1], xs: [0,1], x_label: \"\", y_label: \"\", title: \"\", figsize: [1,1])"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction on sentence \"This movie is really nice!!!!! haven't seen anything like this before :3\":\n",
            "(Category 0, tensor(0), tensor([0.6048, 0.3952]))\n",
            "Prediction on sentence \"OMG WHAT WAS THAT???\":\n",
            "(Category 0, tensor(0), tensor([0.6278, 0.3722]))\n",
            "[0, 1]\n",
            "2\n",
            "[0, 1]\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLh2R_u4Dfmf",
        "colab_type": "text"
      },
      "source": [
        "## Some rubbish\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5P803SdwTQC",
        "colab_type": "code",
        "outputId": "0b2f1bcc-ef73-41c1-a031-d2a0e4e61ec1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "// var ys = Python.list(Python.map(Python.float, wrapper.learner!.recorder.losses))\n",
        "// var xs = wrapper.learner!.recorder.lrs\n",
        "\n",
        "// print(Python.list(Python.map(Python.float, wrapper.learner!.recorder.losses)))\n",
        "\n",
        "// print(ys)\n",
        "// print(xs)\n",
        "\n",
        "// wrapper.learner!.recorder.plot()\n",
        "\n",
        "// print(Python.type(wrapper.learner!.recorder))\n",
        "\n",
        "// //ys = wrapper.learner!.recorder._split_list(ys, 10, 5)\n",
        "// //xs = wrapper.learner!.recorder._split_list(xs, 10, 5)\n",
        "\n",
        "// var lrs = wrapper.learner!.recorder._split_list(wrapper.learner!.recorder.lrs, 10, 5)\n",
        "// var losses = wrapper.learner!.recorder._split_list(wrapper.learner!.recorder.losses, 10, 5)\n",
        "// var lossess = Python.list(Python.map(Python.float, losses))\n",
        "\n",
        "// plt.figure(figsize: [25, 10])\n",
        "\n",
        "// plt.plot(xs, ys)\n",
        "// plt.xscale(\"log\")\n",
        "// plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJ10uQ8vzN3m",
        "colab_type": "code",
        "outputId": "c4ec1121-76d2-44c7-86dc-d35670ad1785",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(Python.len(wrapper.learner!.recorder.losses))\n",
        "print(Python.len(wrapper.learner!.recorder.lrs))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\r\n",
            "100\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0izRMkautS4",
        "colab_type": "code",
        "outputId": "301bc3ec-ae62-4e47-af57-a2f970e5fbdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        }
      },
      "source": [
        "draw_plot(\n",
        "              ys: Python.list([1, 2, 3]),\n",
        "              xs: nil,\n",
        "              x_label: \"Learning rates\",\n",
        "              y_label: \"Losses\",\n",
        "              title: \"Tested learning rates with respective losses\"\n",
        "            )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UUB9j/F4I4UtAQ4zxYeCbwL+EEDYDe4EVADHGjSGEeuBloAv4vfz0IpIkSZIkSZLUL7zcfIBMdhsPrWum9Wgn1eOG86n3zub9i6uYMrq80PEGpDCYnkRZU1MTGxoaCh1DkiRJkiRJ0iB1oK2TR9Y3k8kmvNjYSmlxipvmT2FFbZp3nT+eVCoUOuKAEEJYHWOsOX75OZnDWpIkSZIkSZIGqhgjDW/uo+6FhEc3NNPWmWPOlAq+cPs87lxUyZjhpYWOOGhYWEuSJEmSJEnSCew62M6DaxrJNCRs2XWYkWXF3LWoihW1aS6qGk0IjqY+2yysJUmSJEmSJCmvOxd55rVdZF5IeHLTDrpykZrzxvLRpRdw60VTGV5qpdqXvLqSJEmSJEmShrxk7xFWNiSsXN1IS2sb40eU8htXTGd5bZqZkyoKHW/IsLCWJEmSJEmSNCS1d3Xz+MYd1DckPLt5NwBXz5rI52+bx3VzJ1NanCpwwqHHwlqSJEmSJEnSkPLq9oNksgmr1jay70gnlWOG8fHrLmRpTRWVY4YVOt6QZmEtSZIkSZIkadA71N7F99Y3U5dNWJfsp6QocOO8KSyvTXPFzAkUpXyAYn9gYS1JkiRJkiRpUIoxsmbbfuqzCY+82MyRjm5mTRrJn9w6l7sWVTJ+ZFmhI+o4FtaSJEmSJEmSBpW9hzt4cE0jmWzCazsPMby0iNsvmsay2jSLq8cQgqOp+ysLa0mSJEmSJEkDXi4XeXbzbjLZhMdf3k5nd+SS9Bj+8u6F3HbxNEaWWYUOBL5LkiRJkiRJkgas5v1HWdnQSH1DQtP+o4wZXsKH3zWd5bVpZk+pKHQ8nSELa0mSJEmSJEkDSkdXjh9t2kFdNuGZ13YRI1w1awKfuXkON86fTFlxUaEj6m2ysJYkSZIkSZI0IGzeeZBMNuHBNU3sOdzBlFHl/P6SmdxTkyY9bnih4+kssLCWJEmSJEmS1G8d6eji0RdbyGQTGt7cR3EqcP3cySy/LM3VsyZSlPIBioOJhbUkSZIkSZKkfiXGyIuNrdRlEx5Z38yh9i7OnziCz948h7sXVzGxoqzQEdVHLKwlSZIkSZIk9Qv7j3Swam0TmWzCK9sPUl6S4taF01hxWZqa88YSgqOpBzsLa0mSJEmSJEkFk8tFntuyh7pswg82bqejK8dFVaP58l0LuP3iaYwqLyl0RJ1DFtaSJEmSJEmSzrntrW3cvzoh05CQ7D3KqPJiPlCbZlltmvnTRhc6ngrEwlqSJEmSJEnSOdHZneOpV3aSySY89epOchHeff54PnnjbN47fwrlJUWFjqgCs7CWJEmSJEmS1Ke27j5MJptw/+pGdh9qZ1JFGR+95gKW1aQ5b/yIQsdTP2JhLUmSJEmSJOmsO9rRzfdfaiGTTXh+616KUoElsyexojbNNbMnUlyUKnRE9UMW1pIkSZIkSZLOmpeaWslkEx5a18TBti7OGz+cT980m6WLq5g0qrzQ8dTPWVhLkiRJkiRJekdaj3by8Lom6rIJG5sPUFac4uYFU1heW83lM8aRSoVCR9QAYWEtSZIkSZIk6YzFGHl+617qswmPbmihvSvH3Kmj+NL75vO+iysZPbyk0BE1AFlYS5IkSZIkSTptOw+28cDqJuobErbuPkxFWTH31FSxvKaaBZWjCMHR1Hr7LKwlSZIkSZIknVJXd46nf76LumzCv7+yk+5c5LLp4/jYkpncsnAqw0qLCh1Rg4SFtSRJkiRJkqQT2rbnCPUNCStXJ+w40M6EkaV85KoZLKtJc8HEkYWOp0HIwlqSJEmSJEnSf2rr7OaHG7eTySb89PU9pAJcM3sSX7wjzXVzJ1FSlCp0RA1iFtaSJEmSJEmS2NRygEw2YdXaJlqPdlI1dhifuOFCltZUMXX0sELH0xBhYS1JkiRJkiQNUQfbOnlkfQuZ7DbWN7ZSWpTivQumsKI2zbvPH08q5QMUdW5ZWEuSJEmSJElDSIyR1W/uoy6b8OiLLRzt7Gb25Ao+f9s87lpUydgRpYWOqCHMwlqSJEmSJEkaAnYfaufBNY1ksgmv7zrMiNIi7lw0jeW11VxcNZoQHE2twrOwliRJkiRJkgap7lzkJ6/tIpNNeOLlHXTlIpeeN5avLb2AWxdOZUSZ9aD6F+9ISZIkSZIkaZBJ9h5h5epG7m9IaG5tY9yIUv7br0xneW2aWZMrCh1POikLa0mSJEmSJGkQaO/q5omXd5DJJjy7eTcAV82ayJ/cNo/r506mtDhV4ITSW7OwliRJkiRJkgawn+84SCab8OCaRvYd6aRyzDD+8LpZLL20iqqxwwsdTzojFtaSJEmSJEnSAHO4vYvvvdhMXTZh7bb9lBQFbpg3meW11Vw5cwJFKR+gqIHJwlqSJEmSJEkaAGKMrE32U59NeGR9M4c7upk5aSR/cutc7lpUyfiRZYWOKL1jFtaSJEmSJElSP7b3cAer1jaRyW7j5zsOMaykiNsvnsry2jSLq8cSgqOpNXhYWEuSJEmSJEn9TC4X+Y/Xd1OXTXhi4w46unNcnB7DV+9eyG0XTaWivKTQEaU+YWEtSZIkSZIk9RMtrUdZ2dBIfUNC476jjBlewofeVc3y2jRzpowqdDypz1lYS5IkSZIkSQXU2Z3jR5t2kMkmPP3zXeQiXDlzAp++aQ43zptMeUlRoSNK54yFtSRJkiRJklQAr+86RH024YE1jew+1MHkUWX83pKZ3HNpmurxwwsdTyoIC2tJkiRJkiTpHDnS0cVjG7aTyW4j+8Y+ilOBa+dMYsVlaa6eNZHiolShI0oFZWEtSZIkSZIk9aEYIxuaWslkEx5e18zB9i5mTBjBZ26ew92LK5lUUV7oiFK/YWEtSZIkSZIk9YHWI508tK6JumzCppYDlJekuGXhVJbXpLlsxjhCCIWOKPU7FtaSJEmSJEnSWZLLRZ7buodMNuH7L22noyvHgspR/PmdC7jj4mmMHlZS6IhSv2ZhLUmSJEmSJL1DOw60cf/qRuobEt7cc4SK8mJW1KZZVpNmQeXoQseTBgwLa0mSJEmSJOlt6OrO8dSru8hkt/HUq7vozkXedf44Pn79LG5eMJXykqJCR5QGHAtrSZIkSZIk6Qy8sfswmYaEB1Y3svNgOxMryrj36vNZVpNmxoQRhY4nDWgW1pIkSZIkSdJbaOvs5gcvbacuu43ntuwlFeDaOZNYXlvNNbMnUlKUKnREaVCwsJYkSZIkSZJOYmNzK5lswkNrmzjQ1kX1uOF86r2zef/iKqaMLi90PGnQsbCWJEmSJEmSejnQ1sl31zVTn03Y0NRKaXGKmxdMYXltmnfNGE8qFQodURq0+qywDiGkgX8GJgMRuC/G+I3jtvkU8KFeWeYCE2OMe0MIbwAHgW6gK8ZY01dZJUmSJEmSNLTFGMm+sY+67DYe29BCW2eOOVMq+OId83nfJdMYM7y00BGlIaEvR1h3AZ+IMa4JIVQAq0MIT8QYXz62QYzxfwD/AyCEcDvwf8UY9/Y6xpIY4+4+zChJkiRJkqQhbNfBdh5Y00h9NmHL7sOMLCvm7sVVrKhNs7ByNCE4mlo6l/qssI4xtgAt+a8PhhA2AZXAyyfZ5QPAv/VVHkmSJEmSJAmgqzvHM6/tIpNN+NGmnXTlIrXTx/K7S2Zyy8IpDC91Fl2pUM7Jpy+EMB1YBDx/kvXDgZuAj/VaHIHHQwgR+IcY430n2fde4F6A6urqsxdakiRJkiRJg0qy9wj1DQkrGxrZfqCN8SNK+a0rZ3BPTZqZk0YWOp4kzkFhHUIYCTwAfDzGeOAkm90O/Mdx04FcGWNsCiFMAp4IIbwSY3zm+B3zRfZ9ADU1NfEsx5ckSZIkSdIA1tbZzeMv7yCT3cZ/bN5DKsDVF07kC3fM49o5kyktThU6oqRe+rSwDiGU0FNWfzvG+OApNl3BcdOBxBib8r/vDCGsAi4DfqmwliRJkiRJko73yvYDZLIJq9Y2sf9IJ5VjhvHfb7iQpZdWMW3MsELHk3QSfVZYh54Z6b8JbIoxfv0U240G3gP8aq9lI4BUfu7rEcCNwJf6KqskSZIkSZIGvkPtXTyyvpm6bML6ZD+lRSlunD+Z5bVprrhgAqmUD1CU+ru+HGF9BfBhYEMIYV1+2eeAaoAY49/nl90FPB5jPNxr38nAqvxTWIuB78QYf9CHWSVJkiRJkjQAxRhZs20fdS8kPLqhhSMd3Vw4eSR/ets87lpUybgRpYWOKOkM9FlhHWN8FnjLH1vFGL8FfOu4ZVuAi/skmCRJkiRJkga8PYfaWbW2ibpswuadhxheWsQdF09jeW2aS9JjyA+ElDTA9PlDFyVJkiRJkqSzoTsXeXbzbjLZbTzx8g46uyOLq8fwV+9fyK0XTWNkmVWXNND5KZYkSZIkSVK/1rT/KCsbElY2NNK0/yhjh5fwa++ezvLaNBdOrih0PElnkYW1JEmSJEmS+p2OrhxPbtpBXTbhJ6/tAuDKmRP43C1zuX7eJMqKiwqcUFJfsLCWJEmSJElSv/HajoNksgkPrm1i7+EOpo4u5/evncU9l1aRHje80PEk9TELa0mSJEmSJBXU4fYuHt3QQiabsPrNfRSnAjfMm8zy2jRXzZpIUcoHKEpDhYW1JEmSJEmSzrkYI+sbW8lkt/HwumYOd3RzwcQRfO6WOdy9uIoJI8sKHVFSAVhYS5IkSZIk6ZzZd7iDVWubqG9IeGX7QYaVFHHrRVNZUZvm0vPGEoKjqaWhzMJakiRJkiRJfSqXi/xsyx7qsgk/fGk7Hd05Lq4azVfuWsjtF0+loryk0BEl9RMW1pIkSZIkSeoTLa1Hub+hkfrVCcneo4weVsIHL69mWU2aedNGFTqepH7IwlqSJEmSJElnTWd3jh9t2kl9Q8KPX91JLsKvXDCeT944m/fOn0J5SVGhI0rqxyysJUmSJEmS9I5t2XWITEPCA6ub2H2oncmjyvjda2ZyT00V540fUeh4kgYIC2tJkiRJkiS9LUc7unlsQwuZbMILb+ylKBW4ds4kVtSmec+FEykuShU6oqQBxsJakiRJkiRJZ+Slplbqstv47tpmDrZ3MX38cP7opjm8f3Elk0aVFzqepAHMwlqSJEmSJElvqfVIJ99d30TdCwkvtxygrDjFrQunsqw2zeUzxhFCKHRESYOAhbUkSZIkSZJOKMbIc1v2Ut+Q8NiGFtq7csyfNoo/f9987rikktHDSgodUdIgY2EtSZIkSZKkX7DzQBv3r2mkPpvwxp4jVJQXs6wmzfLaNAsqRxc6nqRBzMJakiRJkiRJdHXn+PGru6jLJjz16k66c5HLZozjD66bxc0LpjKstKjQESUNARbWkiRJkiRJQ9ibew5T35CwsqGRnQfbmTCyjN++6nyW1VRx/sSRhY4naYixsJYkSZIkSRpi2jq7+eHG7dS9kPCzLXtIBVgyexLLatNcO2cSJUWpQkeUNERZWEuSJEmSJA0RLzcfIJPdxkPrmmk92kl63DA+eeOFLL00zZTR5YWOJ0kW1pIkSZIkSYPZgbZOHlnfTCab8GJjK6VFKW5aMIUVtWnedf54UqlQ6IiS9J8srCVJkiRJkgaZGCMNb+6j7oWERzc009aZY86UCv7s9nnceUklY0eUFjqiJJ2QhbUkSZIkSdIgsetgOw+uaSTTkLBl12FGlhVz16IqVtSmuahqNCE4mlpS/2ZhLUmSJEmSNIB15yLPvLaLzAsJT27aQVcuUnPeWD669AJuvWgqw0utfyQNHP6JJUmSJEmSNAAle4+wsiFh5epGWlrbGDeilN+4YjrLa9PMnFRR6HiS9LZYWEuSJEmSJA0Q7V3dPPHyDjLZhGc37wbg6lkT+fxt87hu7mRKi1MFTihJ74yFtSRJkiRJUj/36vaDZLIJq9Y2su9IJ5VjhvGH183inpo0lWOGFTqeJJ01FtaSJEmSJEn90KH2Lr63vplMQ8LabfspKQrcOG8Ky2vTXDFzAkUpH6AoafCxsJYkSZIkSeonYoysTfaTeSHhkRebOdLRzaxJI/mTW+dy16JKxo8sK3RESepTFtaSJEmSJEkFtvdwBw+uaSSTTXht5yGGlxZx20VTWV5bzeLqMYTgaGpJQ4OFtSRJkiRJUgHkcpFnN9ltRN0AACAASURBVO8m05Dw+MbtdHZHLkmP4S/vXshtF09jZJm1jaShxz/5JEmSJEmSzqHm/UdZ2dBIfUNC0/6jjBlewoffNZ3ltWlmT6kodDxJKigLa0mSJEmSpD7W0ZXjR5t2UJdNeOa1XcQIV86cwGdunsON8ydTVlxU6IiS1C9YWEuSJEmSJPWRzTsPUd+Q8MDqRvYc7mDKqHJ+f8lM7qlJkx43vNDxJKnfsbCWJEmSJEk6i450dPHoiy1ksgkNb+6jOBW4fu5kltemufrCiRSlfICiJJ2MhbUkSZIkSdI7FGPkxcZW6rIJj6xv5lB7F+dPGMFnb57D3YurmFhRVuiIkjQgWFhLkiRJkiS9TfuPdPDQ2ibqsgmvbD9IeUmKWxdOY8VlaWrOG0sIjqaWpDNhYS1JkiRJknQGcrnIc1v2UJdN+MHG7XR05bioajR/cecC7rhkGqPKSwodUZIGLAtrSZIkSZKk07C9tY37VyfUNzSybe8RRpUX84HaNMtq08yfNrrQ8SRpULCwliRJkiRJOonO7hxPvbKTTDbhqVd3kovw7vPH84kbL+S986dQXlJU6IiSNKhYWEuSJEmSJB1n6+7DZLIJ969uZPehdiZVlPE777mAZTVppk8YUeh4kjRoWVhLkiRJkiQBRzu6+f5LLWSyCc9v3UtRKrBk9iRW1Ka5ZvZEiotShY4oSYOehbUkSZIkSRrSXmpqJZNNeGhdEwfbujhv/HA+fdNsli6uYtKo8kLHk6QhxcJakiRJkiQNOa1HO3l4XRN12YSNzQcoK05x84IpLK+t5vIZ40ilQqEjStKQZGEtSZIkSZKGhBgjz2/dS3024dENLbR35Zg7dRRfet983ndxJaOHlxQ6oiQNeRbWkiRJkiRpUNt5sI0HVjdR35CwdfdhKsqKWXppFStqq1lQOYoQHE0tSf2FhbUkSZIkSRp0urpzPP3zXdRlE/79lZ105yKXTR/Hx5bM5JaFUxlWWlToiJKkE7CwliRJkiRJg8a2PUeob0hYuTphx4F2Jows5SNXzmBZbZoLJo4sdDxJ0luwsJYkSZIkSQNaW2c3P9y4nUw24aev7yEV4D0XTuSLd1Rz3dxJlBSlCh1RknSaLKwlSZIkSdKAtKnlAJlswqq1TbQe7aRq7DA+ccOFLK2pYuroYYWOJ0l6GyysJUmSJEnSgHGwrZNH1reQyW5jfWMrpUUpbpw/mRW11fzKBeNJpXyAoiQNZBbWkiRJkiSpX4sxsvrNfWSyCd97sYWjnd3MnlzB52+bx12LKhk7orTQESVJZ0mfFdYhhDTwz8BkIAL3xRi/cdw21wDfBbbmFz0YY/xSft1NwDeAIuAfY4x/2VdZJUmSJElS/7P7UDur1jRRl93G67sOM6K0iDsXTWNZTZpL0mMIwdHUkjTY9OUI6y7gEzHGNSGECmB1COGJGOPLx233kxjjbb0XhBCKgL8FbgAagWwI4eET7CtJkiRJkgaR7lzkJ6/tIpNNeHLTDjq7I4urx/C191/ErRdNZUSZ/1hckgazPvtTPsbYArTkvz4YQtgEVAKnUzpfBmyOMW4BCCHUAe87zX0lSZIkSdIA07jvCCsbGlnZkNDc2sa4EaX8+runs7w2zazJFYWOJ0k6R87JjyVDCNOBRcDzJ1j97hDCeqAZ+GSMcSM9xXbSa5tG4PKTHPte4F6A6urqsxdakiRJkiT1qfaubp58eSd12W08u3k3AFfNmsgf3zqP6+dNoqy4qMAJJUnnWp8X1iGEkcADwMdjjAeOW70GOC/GeCiEcAvwEDDrTI4fY7wPuA+gpqYmnoXIkiRJkiSpD/18x0Ey2YRVa5vYe7iDaaPL+YNrZ3FPTRVVY4cXOp4kqYD6tLAOIZTQU1Z/O8b44PHrexfYMcbHQgh/F0KYADQB6V6bVuWXSZIkSZKkAehwexePvthCXXYba7btp6QocMO8ySyvrebKmRMoSvkARUlSHxbWoedRvd8ENsUYv36SbaYAO2KMMYRwGZAC9gD7gVkhhBn0FNUrgA/2VVZJkiRJknT2xRhZl+wnk014ZH0zhzu6mTlpJH98y1zuWlzJhJFlhY4oSepn+nKE9RXAh4ENIYR1+WWfA6oBYox/DywFPhpC6AKOAitijBHoCiF8DPghUAT8f/m5rSVJkiRJUj+393AHq9Y2UZ9NeHXHQYaVFHHbRVNZcVmaxdVj6RnjJknSLws9/fDgUFNTExsaGgodQ5IkSZKkISeXi/z09T3UZbfx+MYddHTnuDg9hhW1aW67aCoV5SWFjihJ6kdCCKtjjDXHL+/zhy5KkiRJkqTBq6X1KCsbGqlvSGjcd5Qxw0v44OXVLK9NM3fqqELHkyQNMBbWkiRJkiTpjHR25/jRph1ksglP/3wXuQhXzBzPp2+aw43zJlNeUlToiJKkAcrCWpIkSZIknZbXdx2iPpvwwJpGdh/qYPKoMn5vyUzuuTRN9fjhhY4nSRoELKwlSZIkSdJJHeno4rEN28lkt5F9Yx/FqcC1cyax4rI0V8+aSHFRqtARJUmDiIW1JEmSJEn6BTFGNjS1kskmPLyumYPtXcyYMILP3DyHuxdXMqmivNARJUmDlIW1JEmSJEkCoPVIJw+ta6Ium7Cp5QBlxSluXTiV5bVpLpsxjhBCoSNKkgY5C2tJkiRJkoawXC7y3NY9ZLIJ339pOx1dORZUjuLP71zAHRdPY/SwkkJHlCQNIRbWkiRJkiQNQTsOtHH/6kbqGxLe3HOEivJiVtSmWVaTZkHl6ELHkyQNURbWkiRJkiQNEV3dOZ56dReZ7DaeenUX3bnI5TPG8fHrZ3HzgqmUlxQVOqIkaYizsJYkSZIkaZB7Y/dhMg0JD6xuZOfBdiZWlHHv1eezrCbNjAkjCh1PkqT/ZGEtSZIkSdIg1NbZzQ9e2k5ddhvPbdlLKsC1cyaxrCbNkjmTKClKFTqiJEm/xMJakiRJkqRBZGNzK5lswkNrmzjQ1kX1uOF86r2zef/iKqaMLi90PEmSTsnCWpIkSZKkAe5AWycPr2smk03Y0NRKaXGKmxdMYXltmnfNGE8qFQodUZKk02JhLUmSJEnSABRjJPvGPuqy23hsQwttnTnmTKngC7fP485FlYwZXlroiJIknTELa0mSJEmSBpBdB9t5YE0j9dmELbsPM7KsmLsXV7GiNs3CytGE4GhqSdLAZWEtSZIkSVI/152LPPPzXdRlt/GjTTvpykVqp4/ld5fM5JaFUxhe6l/vJUmDg/9FkyRJkiSpn0r2HqG+IWFlQyPbD7QxfkQpv3nlDJbVpJk5aWSh40mSdNZZWEuSJEmS1I+0dXbz+Ms7qM8mPLt5NyHAey6cyBfumMe1cyZTWpwqdERJkvqMhbUkSZIkSf3AK9sPkMkmrFrbxP4jnVSOGcZ/v+FCll5axbQxwwodT5Kkc8LCWpIkSZKkAjnU3sUj65upyyasT/ZTWpTihvmTWVGb5ooLJpBK+QBFSdLQYmEtSZIkSdI5FGNkzbZ9ZLIJ33uxhSMd3Vw4eSR/ets87lpUybgRpYWOKElSwVhYS5IkSZJ0Duw51M6qtU3UZRM27zzE8NIi7rh4Gstq0yxKjyEER1NLkmRhLUmSJElSH+nORZ7dvJtMdhtPvLyDzu7Iouox/NX7F3LrRdMYWeZfyyVJ6s3/MkqSJEmSdJY17T/KyoaElQ2NNO0/ytjhJfzau6ezvDbNhZMrCh1PkqR+y8JakiRJkqSzoKMrx5ObdlCXTfjJa7sAuHLmBD57yxxumDeZsuKiAieUJKn/s7CWJEmSJOkdeG3HQTLZhAfXNrH3cAdTR5fz+9fO4p5Lq0iPG17oeJIkDSgW1pIkSZIknaHD7V08uqGFTDZh9Zv7KE4Frp87meWXpbl61kSKUj5AUZKkt8PCWpIkSZKk0xBjZH1jK5nsNh5e18zhjm7OnziCz90yh7sWVTGxoqzQESVJGvAsrCVJkiRJOoV9hztYtbaJ+oaEV7YfpLwkxW0XTWN5bZqa88YSgqOpJUk6WyysJUmSJEk6Ti4X+dmWPdRlE3740nY6unNcVDWaL9+1gNsvnsao8pJCR5QkaVCysJYkSZIkKa+l9Sj3NzRSvzoh2XuUUeXFfPDyapbVpJk3bVSh40mSNOhZWEuSJEmShrTO7hw/2rST+oaEH7+6k1yEX7lgPJ+8cTbvnT+F8pKiQkeUJGnIsLCWJEmSJA1JW3YdItOQ8MDqJnYfamdSRRkfveYCltWkOW/8iELHkyRpSLKwliRJkiQNGUc7uvn+Sy3UZRNe2LqXolTg2jmTWFGb5j0XTqS4KFXoiJIkDWkW1pIkSZKkQe+lplbqstv47tpmDrZ3MX38cD5902yWLq5i0qjyQseTJEl5FtaSJEmSpEGp9Ugn313fRCabsLH5AGXFKW5ZOJXltWkunzGOEEKhI0qSpONYWEuSJEmSBo0YI89v3Usmm/DYhhbau3LMmzqKP3/ffO64pJLRw0oKHVGSJJ2ChbUkSZIkacDbeaCN+9c0Up9NeGPPESrKi7mnpooVtdUsqBxd6HiSJOk0WVhLkiRJkgakru4cP351F5mGhH9/ZSfduchlM8bxB9fN4uYFUxlWWlToiJIk6QxZWEuSJEmSBpQ39xymviFhZUMjOw+2M2FkGb991fksq6ni/IkjCx1PkiS9AxbWkiRJkqR+r62zmx9u3E7dCwk/27KHVIBrZk9ieW2aa+dMoqQoVeiIkiTpLLCwliRJkiT1Wy83H6C+IWHV2iZaj3aSHjeMT954IUsvTTNldHmh40mSpLPMwlqSJEmS1K8cbOvk4fXNZLIJLza2UlqU4qYFU1hem+bd548nlQqFjihJkvqIhbUkSZIkqeBijDS8uY+6FxIe29DC0c5u5kyp4M9un8edl1QydkRpoSNKkqRzwMJakiRJklQwuw+18+CaRuqyCVt2HWZEaRF3LqpkRW2ai6pGE4KjqSVJGkosrCVJkiRJ51R3LvLMa7vIvJDw5KYddOUiNeeN5XeWXsCtC6cyosy/qkqSNFT5fwGSJEmSpHMi2XuElQ0JK1c30tLaxrgRpfzGFdNZXptm5qSKQseTJEn9gIW1JEmSJKnPtHd188TLO8hkE57dvBuAq2ZN5E9vm8f1cydTWpwqcEJJktSfWFhLkiRJks66V7cfJJNNWLW2kX1HOqkcM4w/vG4W99SkqRwzrNDxJElSP2VhLUmSJEk6Kw61d/G99c1kGhLWbttPSVHgxnlTWFab5sqZEyhK+QBFSZJ0ahbWkiRJkqS3LcbI2mQ/mRcSHnmxmSMd3cycNJI/uXUudy2qZPzIskJHlCRJA4iFtSRJkiTpjO093MGDaxrJZBNe23mIYSVF3H7xVJbXVrO4egwhOJpakiSdOQtrSZIkSdJpyeUiz27eTaYh4fGN2+nsjlySHsNX717IbRdNpaK8pNARJUnSANdnhXUIIQ38MzAZiMB9McZvHLfNh4A/AgJwEPhojHF9ft0b+WXdQFeMsaavskqSJEmSTq55/1FWNjRS35DQtP8oY4aX8KvvOo/ltWnmTBlV6HiSJGkQ6csR1l3AJ2KMa0IIFcDqEMITMcaXe22zFXhPjHFfCOFm4D7g8l7rl8QYd/dhRkmSJEnSCXR05fjRph3UZROeeW0XMcKVMyfwmZvncMO8yZSXFBU6oiRJGoT6rLCOMbYALfmvD4YQNgGVwMu9tvlpr12eA6r6Ko8kSZIk6a1t3nmI+oaEB1Y3sudwB1NGlfOxJTNZVpMmPW54oeNJkqRB7pzMYR1CmA4sAp4/xWa/BXy/1/cReDyEEIF/iDHed5Jj3wvcC1BdXX024kqSJEnSkHKko4tHX2whk01oeHMfxanAdXMnsaK2mqsvnEhRygcoSpKkc6PPC+sQwkjgAeDjMcYDJ9lmCT2F9ZW9Fl8ZY2wKIUwCngghvBJjfOb4ffNF9n0ANTU18ayfgCRJkiQNQjFGNjS1UpdNeHhdM4fauzh/wgg+e/Mc7l5cxcSKskJHlCRJQ1CfFtYhhBJ6yupvxxgfPMk2FwH/CNwcY9xzbHmMsSn/+84QwirgMuCXCmtJkiRJ0unbf6SDh9Y2UZdNeGX7QcpLUtyycCoraqupnT6WEBxNLUmSCqfPCuvQ83853wQ2xRi/fpJtqoEHgQ/HGH/ea/kIIJWf+3oEcCPwpb7KKkmSJEmDWS4XeW7LHjINCd9/aTsdXTkWVo7mL+5cwB2XTGNUeUmhI0qSJAF9O8L6CuDDwIYQwrr8ss8B1QAxxr8HPg+MB/4u/1P8rhhjDTAZWJVfVgx8J8b4gz7MKkmSJEmDzo4Dbdy/upFMNmHb3iOMKi/mA7VpltWmmT9tdKHjSZIk/ZI+K6xjjM8Cp/y3ZDHGjwAfOcHyLcDFfRRNkiRJkgatzu4cT72yk0w24alXd5KL8K7zx/Hfb7iQmxZMobykqNARJUmSTqrPH7ooSZIkSep7W3cfpr4h4f7Vjew62M7EijJ+5z0XsKwmzfQJIwodT5Ik6bRYWEuSJEnSANXW2c33X2qh7oWE57fupSgVWDJ7Estr0yyZPZHiolShI0qSJJ0RC2tJkiRJGmBeamolk014aF0TB9u6OG/8cD713tksvbSKyaPKCx1PkiTpbbOwliRJkqQBoPVoJw+vbyaT3cZLTQcoLU5xy4IpLK+t5vIZ40ilTvkIIUmSpAHBwlqSJEmS+qkYIy9s3Usmm/Dohhbau3LMnTqKL94xnzsvqWT08JJCR5QkSTqrLKwlSZIkqZ/ZebCNB1Y3Ud+QsHX3YSrK/n/27jy6zvqw1/3z2xo9yPIoWZa2sMHzBBjJkDKEeTIQIGC56c1pe9rmtj3p3LRpmyZN2tPxrLtuz+o9bXPPzenNurfVlgEnEEgCSWgoTRO2PIGZZ70abMmTLEvWuH/3D9xz3RSIAMuvhuezFkvWu/cWX/+B2ftZr9+3mHsuqWNHYz0ba+cRgmdTS5Kk6clgLUmSJEmTwOhYgSde7qH5qYRvv9DNWCGydflCPnnNSm7dVMOs0qK0J0qSJE04g7UkSZIkpajtyAAtrQk7dyccOjHE4rml/OwVK7i3IcvKqrlpz5MkSTqnDNaSJEmSdI4NjozxzWcP0tKa8M+vHCET4MOrl/D5O+q5bl0VJUWZtCdKkiSlwmAtSZIkSefI810nyOUTdu3toPfUCHULZvHrN6zmnkvqWDZ/VtrzJEmSUmewliRJkqQJ1Dc4wkP7u8jl29jf3ktpUYYbN1Szo7GeH7tgEZmMN1CUJEn6VwZrSZIkSTrLYozsfvMYuXzC157u4tTIGKur5/LZ29Zz18W1LJhTmvZESZKkSclgLUmSJElnyeGTQ+za00Fzvo1Xe/qZU1rERy5aRlNjlouy8wnBs6klSZLejcFakiRJkj6AsULkn17uIZdP+NbzhxgZi2ypn8+ff3Qz2zbXMKfMj12SJEnj5TsnSZIkSXof2o8NsLO1nZ2tCZ29gyyYXcJPfmg5TY1ZVlVXpD1PkiRpSjJYS5IkSdI4DY2O8a3numnOt/HkK4cBuGLlYn5v23quX19FWXFRygslSZKmNoO1JEmSJP0ILx3qI5dP2LW3g6P9wyyrLOeXr13FvQ111C2YnfY8SZKkacNgLUmSJElvo39olIef7qI538aetuOUFAVuWF/N9oYsV65aQlHGGyhKkiSdbQZrSZIkSTotxsi+5Di5fMJD+zvpHx7jgiVz+L1b13HXlloWzy1Le6IkSdK0ZrCWJEmSNOMd6x9m194OcvmEFw/1MaukiNs217Bja5Yt9QsIwbOpJUmSzgWDtSRJkqQZqVCIfO/VIzTn23j02UMMjxW4MDufP75rE7dfWENFeUnaEyVJkmYcg7UkSZKkGaWr9xQ7W9tpaU1oP3aKylklfOzSepoas6yrmZf2PEmSpBnNYC1JkiRp2hsZK/Dt57vJ5dv47ks9FCJcvnIRv3XzWm5cX015SVHaEyVJkoTBWpIkSdI09mrPSVryCffvaefwyWGq55Xxi1evZHtDlvpFs9OeJ0mSpB9isJYkSZI0rZwaHuPhZ7poySc89cZRijKB69ZWsWNrlqtWLaG4KJP2REmSJL0Dg7UkSZKkKS/GyIGOEzTn23hwXyd9Q6OsWDyHT9+ylru31FJVUZ72REmSJI2DwVqSJEnSlNU7MMJX9nXQnE94vusEZcUZtm2qoakxy9YVCwkhpD1RkiRJ74HBWpIkSdKUUihEvv/6EVryCY8cOMjwaIGNtfP4wzs3cseFy6icVZL2REmSJL1PBmtJkiRJU8KhE4Pct7udltaEN48MUFFeTFNDlqbGLBtrK9OeJ0mSpLPAYC1JkiRp0hodK/D4iz3k8m08/mIPY4XIpSsW8qvXr+LmDTXMKi1Ke6IkSZLOIoO1JEmSpEnnjcP9tLQm3Le7ne6+IRbPLePnrjyf7Q11nL9kbtrzJEmSNEEM1pIkSZImhcGRMb5x4CDN+Ta+/9pRMgGuWVNFU2OWa9ZWUVKUSXuiJEmSJpjBWpIkSVKqnu3sJZdP+MreDk4MjlK/cDafumkNH91Sx9LK8rTnSZIk6RwyWEuSJEk6504MjvDgvk5y+YRnOnopLc5w84al7GjMctn5i8hkQtoTJUmSlAKDtSRJkqRzIsZI/o1jNOfbeOSZLgZHCqxdWsEf3L6eOy+uZf7s0rQnSpIkKWUGa0mSJEkTqqdviPv3tNOST3jtcD9zy4q5e0sdTQ1ZNtdVEoJnU0uSJOktBmtJkiRJZ91YIfLESz0059v49vPdjBYiDect4BeuvoBtm2uYXepHEUmSJP17vkuUJEmSdNYkRwdoaU3Y2drOwRODLJpTyn+8YgXbG7KsrJqb9jxJkiRNcgZrSZIkSR/I4MgYjz53iJZ8wpOvHCYE+PDqJXzu9vVct66a0uJM2hMlSZI0RRisJUmSJL0vLxw8QS6fsGtvB8cHRqidP4tfu3419zTUUTt/VtrzJEmSNAUZrCVJkiSN28mhUR7a30lzPmF/cpySosCNG5ayozHL5RcsJpPxBoqSJEl6/wzWkiRJkt5VjJE9bcfJ5dv42tNdDAyPsapqLp/Zto67t9SxcE5p2hMlSZI0TRisJUmSJL2tIyeH2LW3g1w+4eXuk8wuLeL2zcto2prl4ux8QvBsakmSJJ1d4wrWIYR7gW/EGPtCCJ8BtgB/FGPcM6HrJEmSJJ1ThULkyVcOk8snPPrcQUbGIhfXz+fPPrqJbZuXMbfMc14kSZI0ccb7bvP3Y4w7QwhXANcDfwH8NXDphC2TJEmSdM50HD/FztaEna3tdBw/xYLZJXz8suU0NWZZs7Qi7XmSJEmaIcYbrMdOf90GfDHG+HAI4Y8maJMkSZKkc2B4tMC3nj9ELp/wxMs9xAhXrlrM79y6lhvWV1NWXJT2REmSJM0w4w3WHSGEvwVuAP4shFAGZCZuliRJkqSJ8kp3H7l8wgN7OjjSP0xNZTm/dO0q7r2kjuzC2WnPkyRJ0gw23mC9HbgZ+C8xxuMhhBrgUxM3S5IkSdLZNDA8ytee7iKXT9j95jGKM4Hr11XTtDXLVauWUJTxBoqSJElK37iCdYxxIITQDVwBvAyMnv4qSZIkaZKKMbK/vZdcPuGh/Z2cHBrl/CVz+N1b13LXxXUsqShLe6IkSZL0b4wrWIcQPgc0AGuA/wGUAP8PcPnETZMkSZL0fhwfGGbX3g5y+YQXDvZRXpJh26Zl7NiapeG8BYTg2dSSJEmanMZ7SZC7gIuBPQAxxs4QgrcKlyRJkiaJQiHyL68doTmf8M1nDzI8WmBzXSX/+a6N3H7hMuaVl6Q9UZIkSfqRxhush2OMMYQQAUIIcyZwkyRJkqRxOtg7yH27E3KtCcnRU8wrL+bHG7Nsb8yyYVll2vMkSZKk92S8wbolhPC3wPwQws8B/xH4PyduliRJkqR3MjJW4DsvdJPLJ/zji90UInzo/EX85o1ruGnDUspLitKeKEmSJL0v473p4n8JIdwAnOCt61h/Nsb42Lu9JoSQBb4MVAMR+GKM8S9/6DkB+EvgVmAA+KkY457Tj/0k8JnTT/2jGOP/Pe7flSRJkjQNvdZzklxrwv27Ozh8coiqijJ+4eoL2N6Q5bxF/iVISZIkTX3jveniHOA7McbHQghrgDUhhJIY48i7vGwU+I0Y457T17veHUJ4LMb43BnPuQVYdfqfS4G/Bi4NISwE/vVGj/H0ax+MMR57z79DSZIkaQo7NTzG1w900ZxPeOr1oxRlAtesqWJHY5ar1yyhuCiT9kRJkiTprBnvJUGeAK4MISwAvgG0Ak3AT7zTC2KMXUDX6V/3hRCeB2qBM4P1R4Avxxgj8P0QwvwQQg1wNfBYjPEoQAjhMeBm4B/ew+9NkiRJmrIOdPTSnG/jq/s66Rsc5bxFs/mtm9dwz5Y6quaVpz1PkiRJmhDjDdYhxjgQQvgZ4K9jjH8eQtg33n9JCGE5cDHwgx96qBZIzvi+/fSxdzr+dj/7E8AnAOrr68c7SZIkSZp0egdG+Or+DnL5hGc7T1BWnOGWjUtpaqznsvMX8tYV9SRJkqTpa9zBOoTwId46o/pnTh8b151cQghzgfuBX40xnnjvE99djPGLwBcBGhoa4tn++ZIkSdJEijHyg9ePkssnPPJMF0OjBdbXzOMLH9nARy6spXJ2SdoTJUmSpHNmvMH6V4HfAXbFGJ8NIZwPPP6jXhRCKOGtWP3/xhgfeJundADZM76vO32sg7cuC3Lm8X8c51ZJkiRp0us+Mch9e9ppySe8cWSAirJi7m2oY0djPRtrK9OeJ0mSJKViXME6xvhd4LsAIYQMcDjG+Mvv9prw1t9X/L+A52OM/9s7PO1B4JMhhGbeuulib4yxK4TwTeCPT18zG+BG3grmkiRJ0pQ1OlbgH1/sIdea8J0XuhkrRLauWMgvXbuKWzfVMKt0XH+JUZIkjmZ+eQAAIABJREFUSZq2xhWsQwh/D/w8MAbkgXkhhL+MMf7Fu7zscuDjwDNnXO/6d4F6gBjj3wCPALcCrwADwE+ffuxoCOEPT/+7AL7wrzdglCRJkqaaN4/009KasLO1ne6+IRbPLeVnr1zB9oYsFyyZm/Y8SZIkadIY7yVB1scYT4QQfgL4OvBpYDfwjsE6xvgk8K53hYkxRuA/vcNjXwK+NM59kiRJ0qQyODLGN589SPNTCf/y2hEyAa5eU0VTY5Zr11ZRUpRJe6IkSZI06Yw3WJecvh71ncBfxRhHQgje4FCSJEn6Ic91nqClNWHX3g56T42QXTiL37hhNfc01FFTOSvteZIkSdKkNt5g/bfAG8B+4IkQwnnAiYkaJUmSJE0lfYMjPLi/k1w+4en2XkqLMty0cSk7GrN86PxFZDLv+hcPJUmSJJ023psu/lfgv55x6M0QwjUTM0mSJEma/GKM7H7zGM35hIef7uLUyBhrqiv43O3rufOiWhbMKU17oiRJkjTljPemi5XA54CrTh/6LvAFoHeCdkmSJEmT0uGTQzywp53mfMJrPf3MKS3izouX0dRYz4V1lYTg2dSSJEnS+zXeS4J8CTgAbD/9/ceB/wHcPRGjJEmSpMlkrBB54uUeWvIJjz13iNFC5JLzFvDn91zAtk01zCkb79tqSZIkSe9mvO+sL4gxfvSM7z8fQtg3EYMkSZKkySI5OsDO3e3sbE3o6h1k4ZxSfvry5TQ1ZllZVZH2PEmSJGnaGW+wPhVCuCLG+CRACOFy4NTEzZIkSZLSMTQ6xmPPHSKXT3jylcMAXLlqCb9/23quX1dNaXEm5YWSJEnS9DXeYP3zwJdPX8sa4BjwkxMzSZIkSTr3XjrURy6f8MCedo4NjFA7fxa/ct0q7m3IUjt/VtrzJEmSpBlhXME6xrgfuDCEMO/09ydCCL8KPD2R4yRJkqSJ1D80ytee7qQ5n7C37TglRYEb1lfT1FjPFSsXU5TxBoqSJEnSufSe7g4TYzxxxre/DvzvZ3eOJEmSNLFijOxNjpN7KuFrT3fSPzzGyqq5fGbbOu66uJZFc8vSnihJkiTNWB/kduaebiJJkqQp42j/MLv2dpDLt/HSoZPMKini9gtraGrMsqV+ASH49laSJElK2wcJ1vGsrZAkSZImQKEQ+edXD9OcT3js2UMMjxW4MDufP7l7E7dtrqGivCTtiZIkSZLO8K7BOoTQx9uH6QB45xlJkiRNSp3HT7GztZ2W1oSO46eYP7uEn7isnqbGLGuXzkt7niRJkqR38K7BOsZYca6GSJIkSR/E8GiB77xwiOZ8wndf6iFGuGLlYn77lrXcuL6a8pKitCdKkiRJ+hE+yCVBJEmSpNS90n2SltaE+3e3c6R/mKXzyvnkNSu595Is9Ytmpz1PkiRJ0ntgsJYkSdKUMzA8ysNPd9HSmpB/4xjFmcB166rY0VjPVauXUJTxBoqSJEnSVGSwliRJ0pQQY+SZjl6a8wkP7uvk5NAo5y+ew6dvWcvdW2qpqihPe6IkSZKkD8hgLUmSpEnt+MAwX9nbQXM+4YWDfZSXZLh1Uw07GutpXL6AEDybWpIkSZouDNaSJEmadAqFyPdfO0KuNeHrBw4yPFpgU20lf3TnRu64aBnzykvSnihJkiRpAhisJUmSNGkcOjHIfbvbyeUT2o4OMK+8mB2NWbY3ZNlYW5n2PEmSJEkTzGAtSZKkVI2MFXj8hW5y+YTHX+ymEOGy8xfy6zes5uaNSykvKUp7oiRJkqRzxGAtSZKkVLx+uJ+W1oT7drfT0zfEkooyfv7DF7C9IcvyxXPSnidJkiQpBQZrSZIknTODI2N8/UAXzU8l/OD1oxRlAtesWUJTYz3XrFlCcVEm7YmSJEmSUmSwliRJ0oQ70NFLS2vCrr0d9A2Oct6i2XzqpjXcc0kd1fPK054nSZIkaZIwWEuSJGlC9J4a4cH9neTybRzoOEFpcYZbNy5le2OWy1YsIpMJaU+UJEmSNMkYrCVJknTWxBh56vWj5PIJjxzoYnCkwLqaeXz+jg3ceVEtlbNL0p4oSZIkaRIzWEuSJOkD6+4b5IE9HbTkE1473E9FWTEf3VLHjsZ6NtbOIwTPppYkSZL0oxmsJUmS9L6MjhV44uUemp9K+PYL3YwVIluXL+QXr1nJrZuWMrvUt5qSJEmS3hs/RUiSJOk9aTsyQEtrwn272zl4YpBFc0r52StWcG9DlpVVc9OeJ0mSJGkKM1hLkiTpRxocGePR5w6Ry7fxz68cIRPgqtVL+IM71nPt2mpKizNpT5QkSZI0DRisJUmS9I5eOHiC5qcSdu3toPfUCLXzZ/HrN6zmnkvqWDZ/VtrzJEmSJE0zBmtJkiT9G32DIzy0v4tca8L+5DilRRlu3FBNU2OWyy9YTCbjDRQlSZIkTQyDtSRJkogxsqftGM1PJXzt6S5OjYyxunouv3/beu66uJaFc0rTnihJkiRpBjBYS5IkzWBHTg7xwJ4OmvNtvNrTz+zSIj5y0TKaGrNclJ1PCJ5NLUmSJOncMVhLkiTNMGOFyD+93ENLa8Jjzx1iZCyypX4+f/bRTWzbvIy5Zb5FlCRJkpQOP41IkiTNEO3HBtjZ2s7O1oTO3kEWzC7hP3xoOU2NWVZXV6Q9T5IkSZIM1pIkSdPZ0OgY33qum+Z8G0++chiAK1Yu5ve2ref69VWUFRelvFCSJEmS/n8Ga0mSpGno5UN95PIJD+zt4Gj/MMsqy/mla1dx7yV1ZBfOTnueJEmSJL0tg7UkSdI00T80ysNPd9Gcb2NP23GKM4Eb1lfT1JjlylVLKMp4A0VJkiRJk5vBWpIkaQqLMbIvOU4un/DQ/k76h8e4YMkcfu/Wddy1pZbFc8vSnihJkiRJ42awliRJmoKO9Q+za28HuXzCi4f6mFVSxLbNNexozHLJeQsIwbOpJUmSJE09BmtJkqQpolCIfO/VIzTn23j02UMMjxW4sK6SP75rE7dfWENFeUnaEyVJkiTpAzFYS5IkTXJdvafY2dpOS2tC+7FTVM4q4WOX1tPUmGVdzby050mSJEnSWWOwliRJmoRGxgp8+/lucvk2vvtSD4UIP3bBIj510xpu2rCU8pKitCdKkiRJ0llnsJYkSZpEXus5Sa414f7d7Rw+OUz1vDJ+8eqVbG/IUr9odtrzJEmSJGlCGawlSZJSdmp4jEee6SKXT3jqjaMUZQLXra2iqTHLh1cvobgok/ZESZIkSTonDNaSJEkpiDFyoOMEzfk2HtzXSd/QKMsXzea3b17LRy+ppaqiPO2JkiRJknTOGawlSZLOod6BEb66v4PmpxKe6zpBWXGGbZtqaGrMsnXFQkIIaU+UJEmSpNQYrCVJkiZYjJHvv3aUXL6Nrx84yNBogY218/jDj2zgjotqqZxVkvZESZIkSZoUDNaSJEkTpPvEIDt3t7OzNeGNIwNUlBezvSFLU2OWjbWVac+TJEmSpEnHYC1JknQWjY4V+McXe2jOJzz+YjdjhcilKxbyK9ev4uYNNcwqLUp7oiRJkiRNWgZrSZKks+CNw/20tCbct7ud7r4hFs8t4+euPJ/tDXWcv2Ru2vMkSZIkaUowWEuSJL1PgyNjfOPAQXL5hH957QiZANesqWJ7Y5Zr11ZRUpRJe6IkSZIkTSkTFqxDCF8CbgO6Y4wb3+bxTwE/ccaOdcCSGOPREMIbQB8wBozGGBsmaqckSdJ79VznCXL5Nnbt7eDE4CjZhbP4zRtXc88lWZZWlqc9T5IkSZKmrIk8w/rvgL8Cvvx2D8YY/wL4C4AQwu3Ar8UYj57xlGtijIcncJ8kSdK4nRgc4cF9neTyCc909FJalOHmjUvZ0ZjlsvMXkcmEtCdKkiRJ0pQ3YcE6xvhECGH5OJ/+48A/TNQWSZKk9yPGSP6NY+TyCQ8/08ngSIG1Syv43O3rufOiWhbMKU17oiRJkiRNK6lfwzqEMBu4GfjkGYcj8GgIIQJ/G2P84ru8/hPAJwDq6+sncqokSZohevqGeGBPO7l8wmuH+5lbVsxdF9exozHL5rpKQvBsakmSJEmaCKkHa+B24J9/6HIgV8QYO0IIVcBjIYQXYoxPvN2LT8fsLwI0NDTEiZ8rSZKmo7FC5ImXemjOt/Ht57sZLUQazlvAL1x9Ads21zC7dDK8bZIkSZKk6W0yfPLawQ9dDiTG2HH6a3cIYRewFXjbYC1JkvRBJEcH2NmasHN3O129gyyaU8pPX76cpsYsK6sq0p4nSZIkSTNKqsE6hFAJfBj4X844NgfIxBj7Tv/6RuALKU2UJEnT0NDoGI8+e4hcPuHJVw4TAly1agmfvW09162rprQ4k/ZESZIkSZqRJixYhxD+AbgaWBxCaAc+B5QAxBj/5vTT7gIejTH2n/HSamDX6WtDFgN/H2P8xkTtlCRJM8eLB/tozrexa28HxwdGqJ0/i1+7fjX3NNRRO39W2vMkSZIkacabsGAdY/zxcTzn74C/+6FjrwEXTswqSZI005wcGuVr+ztpzifsS45TUhS4cf1SmhqzXL5yMUUZb6AoSZIkSZPFZLiGtSRJ0lkVY2RP23Fy+Ta+9nQXA8NjrKqay2e2reOui2tZNLcs7YmSJEmSpLdhsJYkSdPGkZND7NrbQS6f8HL3SWaXFnH75mVsb8yypX4+py85JkmSJEmapAzWkiRpSisUIk++cphcPuHR5w4yMha5KDufP717E7dduIy5Zb7dkSRJkqSpwk9wkiRpSuo4foqdrQk7W9vpOH6K+bNL+Phly2lqzLJmaUXa8yRJkiRJ74PBWpIkTRnDowW+/fwhmvMJT7zcQ4xw5arFfPqWtdy4oZqy4qK0J0qSJEmSPgCDtSRJmvRe6e4jl094YE8HR/qHWTqvnF+6ZiX3NmTJLpyd9jxJkiRJ0llisJYkSZPSwPAoDz/dRS6f0PrmMYozgevXVdO0NctVq5ZQlPEGipIkSZI03RisJUnSpBFj5On2XprzCQ/t7+Tk0CjnL5nD79yylru31LGkoiztiZIkSZKkCWSwliRJqTs+MMyuvR3k8gkvHOyjvCTDtk3L2LE1S8N5CwjBs6klSZIkaSYwWEuSpFQUCpHvv3aE5nzCN549yPBogc11lfznuzZy+4XLmFdekvZESZIkSdI5ZrCWJEnn1MHeQe7bnZBrTUiOnmJeeTE/3phle2OWDcsq054nSZIkSUqRwVqSJE24kbEC33mhm5Z8wuMvdlOI8KHzF/GbN67hpg1LKS8pSnuiJEmSJGkSMFhLkqQJ8/rhfnL5hPt2t3P45BBVFWX8/IcvYHtDluWL56Q9T5IkSZI0yRisJUnSWXVqeIyvH+iiOZ/w1OtHKcoErllTxY7GLFevWUJxUSbtiZIkSZKkScpgLUmSzooDHb3k8glf2ddB3+Ao5y2azW/dvIZ7ttRRNa887XmSJEmSpCnAYC1Jkt633lMjPLivg+Z8wrOdJygrznDLxqU0NdZz6YqFZDIh7YmSJEmSpCnEYC1Jkt6TGCM/eP0ouXzCI890MTRaYF3NPL7wkQ185MJaKmeXpD1RkiRJkjRFGawlSdK4dPcNcv/uDlpaE14/3E9FWTH3NtTR1FDPxtp5hODZ1JIkSZKkD8ZgLUmS3tHoWIHvvtRDcz7hOy90M1aIbF2+kE9es5JbN9Uwq7Qo7YmSJEmSpGnEYC1Jkv6dN4/009KacN/udg6dGGLx3FJ+9soVbG/IcsGSuWnPkyRJkiRNUwZrSZIEwODIGN989iC5fML3Xj1CJsDVa6r4/B1ZrltXRUlRJu2JkiRJkqRpzmAtSdIM93zXCXL5hF17O+g9NULdgln8xg2ruaehjprKWWnPkyRJkiTNIAZrSZJmoL7BER7c30lLPmF/ey+lRRlu2riUHY1ZPnT+IjIZb6AoSZIkSTr3DNaSJM0QMUZ2v3mM5nzCw093cWpkjDXVFXz2tvXcdXEtC+aUpj1RkiRJkjTDGawlSZrmDp8c4oE97TTnE17r6WdOaRF3XryMpsZ6LqyrJATPppYkSZIkTQ4Ga0mSpqGxQuSfXu4hl0947LlDjBYil5y3gD+/5wK2baphTplvASRJkiRJk4+fViVJmkaSowPs3N3Ofa0Jnb2DLJxTyk/92HKaGrOsqq5Ie54kSZIkSe/KYC1J0hQ3NDrGY88dIpdPePKVwwBcuWoJn7ltPdevq6a0OJPyQkmSJEmSxsdgLUnSFPXSoT5y+YQH9rRzbGCE2vmz+JXrVnHPJXXULZid9jxJkiRJkt4zg7UkSVNI/9AoX3u6k+Z8wt6245QUBW5YX01TYz1XrFxMUcYbKEqSJEmSpi6DtSRJk1yMkb3JcVryCQ/t76R/eIyVVXP5zLZ13HVxLYvmlqU9UZIkSZKks8JgLUnSJHW0f5hdezvI5dt46dBJZpUUcdvmGnZszbKlfgEheDa1JEmSJGl6MVhLkjSJFAqRf371MM35hMeePcTwWIELs/P5k7s3cdvmGirKS9KeKEmSJEnShDFYS5I0CXQeP8V9u9tpaU1oP3aK+bNL+Nil9TQ1ZllXMy/teZIkSZIknRMGa0mSUjI8WuA7LxyiOZ/wxEs9FCJcvnIRv3XzWm5cX015SVHaEyVJkiRJOqcM1pIknWOvdJ+kpTXhgT3tHD45TPW8Mv7TNSu595Is9Ytmpz1PkiRJkqTUGKwlSToHBoZHeeSZg+TybeTfOEZxJnDt2ip2bM1y1aolFBdl0p4oSZIkSVLqDNaSJE2QGCPPdPTSnE94cF8nJ4dGWbF4Dp++ZS13b6mlqqI87YmSJEmSJE0qBmtJks6y4wPDfGVvB7nWdp7vOkF5SYZbN9XQ1JBl64qFhBDSnihJkiRJ0qRksJYk6SwoFCLff/0IuXzC1w8cZHi0wMbaefzhnRu548JlVM4qSXuiJEmSJEmTnsFakqQP4NCJQe7b3U4un9B2dICK8mJ2NGbZ3pBlY21l2vMkSZIkSZpSDNaSJL1HI2MFHn+hm5bWhO+80E0hwmXnL+TXbljFLRtrKC8pSnuiJEmSJElTksFakqRxev1wPy2tCfftbqenb4glFWX8rx++gO0NWVYsnpP2PEmSJEmSpjyDtSRJ72JwZIyvH+ii+amEH7x+lEyAa9dW0dRYz9VrllBSlEl7oiRJkiRJ04bBWpKkt3Ggo5eW1oRdezvoGxylfuFsPnXTGj66pY6lleVpz5MkSZIkaVoyWEuSdNqJwRG+uq+TXL6NAx0nKC3OcMvGpTQ1ZrlsxSIymZD2REmSJEmSpjWDtSRpRosxkn/jGM35Nh55povBkQJrl1bw+Ts28JGLljF/dmnaEyVJkiRJmjEM1pKkGamnb4j797TTkk947XA/c8uKuXtLHTsas2yqrSQEz6aWJEmSJOlcM1hLkmaM0bECT7zcQy6f8O3nuxktRBqXL+AXr1nJrZuWMrvU/y1KkiRJkpQmP5lLkqa95OgALa0JO1vbOXhikEVzSvmZK1Zwb0OWlVVz054nSZIkSZJOM1hLkqalwZExHn3uELl8G//8yhEyAa5avYQ/uGM9166tprQ4k/ZESZIkSZL0QwzWkqRp5YWDJ8jlE3bt7eD4wAi182fx6zes5p5L6lg2f1ba8yRJkiRJ0rswWEuSpryTQ6M8tL+T5nzC/uQ4pUUZbthQzY7GLJdfsJhMxhsoSpIkSZI0FUxYsA4hfAm4DeiOMW58m8evBr4KvH760AMxxi+cfuxm4C+BIuC/xxj/dKJ2SpKmphgje9qO0fxUwsPPdDEwPMbq6rn8/m3rueviWhbOKU17oiRJkiRJeo8m8gzrvwP+Cvjyuzznn2KMt515IIRQBPwfwA1AO5APITwYY3xuooZKkqaOIyeH2LW3g+Z8wivdJ5ldWsQdFy5je2OWi7PzCcGzqSVJkiRJmqomLFjHGJ8IISx/Hy/dCrwSY3wNIITQDHwEMFhL0gw1Vog8+cphcvk2HnvuECNjkYvr5/NnH93Ets3LmFvmFa4kSZIkSZoO0v6E/6EQwn6gE/jNGOOzQC2QnPGcduDSd/oBIYRPAJ8AqK+vn8CpkqRzrf3YADtb27lvdzsdx0+xYHYJ/+FDy2lqzLK6uiLteZIkSZIk6SxLM1jvAc6LMZ4MIdwKfAVY9V5/SIzxi8AXARoaGuLZnShJOteGRwt86/lDNOcT/unlHgCuWLmY37l1LTesr6asuCjlhZIkSZIkaaKkFqxjjCfO+PUjIYT/FkJYDHQA2TOeWnf6mCRpGnv5UB+5fMIDezs42j9MTWU5v3TtKu69pI7swtlpz5MkSZIkSedAasE6hLAUOBRjjCGErUAGOAIcB1aFEFbwVqjeAXwsrZ2SpInTPzTKw093kWtN2P3mMYozgRvWV9PUmOXKVUsoyngDRUmSJEmSZpIJC9YhhH8ArgYWhxDagc8BJQAxxr8B7gF+IYQwCpwCdsQYIzAaQvgk8E2gCPjS6WtbS5KmgRgj+9t7yeXbeHBfJ/3DY1ywZA6/e+ta7t5Sx+K5ZWlPlCRJkiRJKQlvNeLpoaGhIba2tqY9Q5L0No71D7Nrbwe5fMKLh/qYVVLEts017GjMcsl5CwjBs6klSZIkSZopQgi7Y4wNP3w8zZsuSpKmuUIh8r1Xj5BrTfjmgYMMjxW4sK6SP75rE7dfWENFeUnaEyVJkiRJ0iRisJYknXVdvae4r7WdXGtC+7FTVM4q4WOX1rO9Icv6ZfPSnidJkiRJkiYpg7Uk6awYGSvw7ee7yeXb+O5LPRQi/NgFi/jUTWu4acNSykuK0p4oSZIkSZImOYO1JOkDea3nJLnWhPt3t3P45DDV88r4xatXcm9DHectmpP2PEmSJEmSNIUYrCVJ79mp4TEeeaaLXD7hqTeOUpQJXLu2ih2NWT68egnFRZm0J0qSJEmSpCnIYC1JGrcDHb0059v46t5O+oZGWb5oNr9981o+uqWWqnnlac+TJEmSJElTnMFakvSuegdG+Or+DpqfSniu6wRlxRm2baphe2OWS1csJISQ9kRJkiRJkjRNGKwlSf9OjJHvv3aUltaER57pYmi0wIZl8/jDj2zgjotqqZxVkvZESZIkSZI0DRmsJUn/U/eJQe7b005LPuGNIwNUlBezvSFLU2OWjbWVac+TJEmSJEnTnMFakma40bEC//hiD835hMdf7GasENm6YiG/fN0qbtlYw6zSorQnSpIkSZKkGcJgLUkz1JtH+mlpTdjZ2k533xCL55bxc1eez/aGOs5fMjfteZIkSZIkaQYyWEvSDDI4MsY3nz1I81MJ//LaETIBrl5TRVNjlmvXVlFSlEl7oiRJkiRJmsEM1pI0AzzXeYJcvo2v7Ouk99QI2YWz+M0bV3PPJVmWVpanPU+SJEmSJAkwWEvStHVicISH9neSyyc83d5LaVGGmzcupakxy4fOX0QmE9KeKEmSJEmS9G8YrCVpGokx0vrmMZqfSnj4mU4GRwqsXVrB525fz50X1bJgTmnaEyVJkiRJkt6RwVqSpoGeviEe2NNOrjXhtZ5+5pQWcdfFdexozLK5rpIQPJtakiRJkiRNfgZrSZqixgqRJ17uIfdUwreeP8RoIdJw3gJ+/p4L2Laphjll/hEvSZIkSZKmFmuGJE0xydEBdrYm7NzdTlfvIAvnlPLTly+nqTHLyqqKtOdJkiRJkiS9bwZrSZoChkbHePTZQ7S0Jjz5ymEArlq1hM/etp7r1lVTWpxJeaEkSZIkSdIHZ7CWpEnsxYN95PIJu/a2c2xghNr5s/iV61Zxb0OW2vmz0p4nSZIkSZJ0VhmsJWmSOTk0ytf2d9KcT9iXHKekKHDj+qU0NWa5fOViijLeQFGSJEmSJE1PBmtJmgRijOxpO05LPuGhpzsZGB5jVdVcPrNtHXddXMuiuWVpT5QkSZIkSZpwBmtJStHR/mEe2NNOLp/wcvdJZpcWcdvmGpoa69lSP58QPJtakiRJkiTNHAZrSTrHCoXIk68cJpdPePS5g4yMRS7KzudP797EbRcuY26ZfzRLkiRJkqSZySoiSedI5/FT7Gxtp6U1oeP4KebPLuHjly2nqTHLmqUVac+TJEmSJElKncFakibQ8GiBbz9/iOZ8whMv9xAjXLFyMZ++ZS03bqimrLgo7YmSJEmSJEmThsFakibAK90naWlNuH93O0f6h1k6r5xfumYl9zZkyS6cnfY8SZIkSZKkSclgLUlnycDwKA8/3UUun9D65jGKM4Hr11XT1JjlqtVLKMp4A0VJkiRJkqR3Y7CWpA8gxsjT7b005xMe2t/JyaFRzl88h9+5ZS13b6ljSUVZ2hMlSZIkSZKmDIO1JL0PxweG+creDprzCS8c7KO8JMO2TcvYsTVLw3kLCMGzqSVJkiRJkt4rg7UkjVOhEPn+a0dozid849mDDI8W2FxXyR/duZE7LlrGvPKStCdKkiRJkiRNaQZrSfoRDvYOct/uhJbWdtqODjCvvJgfb8yyvTHLhmWVac+TJEmSJEmaNgzWkvQ2RsYKPP5CN7l8wuMvdlOIcNn5C/n1G1Zz88allJcUpT1RkiRJkiRp2jFYS9IZXj/cTy6fcN/udg6fHGJJRRk//+EL2N6QZfniOWnPkyRJkiRJmtYM1pJmvFPDY3z9QBe5fMIPXj9KUSZwzZoqmhqzXLNmCcVFmbQnSpIkSZIkzQgGa0kz1oGOXnL5hK/s66BvcJTzFs3mUzet4Z5L6qieV572PEmSJEmSpBnHYC1pRuk9NcKD+zpozic823mC0uIMt25cSlNjPZeuWEgmE9KeKEmSJEmSNGMZrCVNezFGfvD6UVryCQ8/08XQaIF1NfP4/B0buPOiWipnl6Q9UZIkSZIkSRisJU1j3X2D3L+7g5bWhNcP91NRVsw9l9Sxo7GejbXzCMGzqSVJkiRJkiYTg7WkaWV0rMB3X+qhOZ/wnRe6GStEti5fyCevWcmtm2r5HbL6AAAb/0lEQVSYVVqU9kRJkiRJkiS9A4O1pGmh7cgALa0JO3cnHDoxxOK5pfzsFSvY3pjlgiVz054nSZIkSZKkcTBYS5qyBkfG+OazB8nlE7736hEyAT68egmfv6Oe69ZVUVKUSXuiJEmSJEmS3gODtaQp5/muE+TyCbv2dtB7aoS6BbP4jRtWc09DHTWVs9KeJ0mSJEmSpPfJYC1pSugbHOGh/V3k8m3sb++ltCjDjRuq2dFYz49dsIhMxhsoSpIkSZIkTXUGa0mTVoyR3W8eozmf8PDTXZwaGWNNdQWfvW09d11cy4I5pWlPlCRJkiRJ0llksJY06Rw+OcQDe9rJ5RNe7elnTmkRd168jO0NWS7KzicEz6aWJEmSJEmajgzWkiaFsULkn17uIZdPeOy5Q4wWIlvq5/PnH93Mts01zCnzjytJkiRJkqTpzgIkKVXtxwbY2drOztaEzt5BFs4p5ad+bDlNjVlWVVekPU+SJEmSJEnnkMFa0jk3NDrGt57rpjnfxpOvHAbgylVL+L1t67l+fRVlxUUpL5QkSZIkSVIaDNaSzpmXDvWRyyfs2tvB0f5hllWW88vXruLehjrqFsxOe54kSZIkSZJSZrCWNKH6h0Z5+OkumvNt7Gk7TklR4Ib11TQ11nPFysUUZbyBoiRJkiRJkt5isJZ01sUY2ZccJ5dPeGh/J/3DY6ysmsvv3bqOu7bUsnhuWdoTJUmSJEmSNAlNWLAOIXwJuA3ojjFufJvHfwL4bSAAfcAvxBj3n37sjdPHxoDRGGPDRO2UdPYc7R9m194OWvIJLx7qY1ZJEbdtrmHH1ixb6hcQgmdTS5IkSZIk6Z1N5BnWfwf8FfDld3j8deDDMcZjIYRbgC8Cl57x+DUxxsMTuE/SWVAoRL736hGa8208+uwhhscKXJidz5/cvYnbNtdQUV6S9kRJkiRJkiRNERMWrGOMT4QQlr/L498749vvA3UTtUXS2dfVe4qdre20tCa0HztF5awSPnZpPU2NWdbVzEt7niRJkiRJkqagyXIN658Bvn7G9xF4NIQQgb+NMX7xnV4YQvgE8AmA+vr6CR0pzXQjYwW+/fwhcvmE777UQyHC5SsX8Vs3r+XG9dWUlxSlPVGSJEmSJElTWOrBOoRwDW8F6yvOOHxFjLEjhFAFPBZCeCHG+MTbvf50zP4iQENDQ5zwwdIM9GrPSVryCffvaefwyWGq55Xxi1evZHtDlvpFs9OeJ0mSJEmSpGki1WAdQtgM/HfglhjjkX89HmPsOP21O4SwC9gKvG2wljQxBoZHeeSZg+TybeTfOEZRJnDd2ip2bM1y1aolFBdl0p4oSZIkSZKkaSa1YB1CqAceAD4eY3zpjONzgEyMse/0r28EvpDSTGlGiTHyTEcvuXzCg/s66RsaZcXiOXz6lrXcvaWWqorytCdKkiRJkiRpGpuwYB1C+AfgamBxCKEd+BxQAhBj/Bvgs8Ai4L+FEABGY4wNQDWw6/SxYuDvY4zfmKidkqB3YISv7OugOZ/wfNcJyoozbNtUQ1Njlq0rFnL6v0dJkiRJkiRpQoUYp89lnxsaGmJra2vaM6QpoVCIfP/1I+TyCV8/cJDh0QIba+fR1FjPHRcuo3JWSdoTJUmSJEmSNE2FEHafPoH530j9pouSzq1DJwa5b3c7La0Jbx4ZoKK8mB2NWbY3ZNlYW5n2PEmSJEmSJM1gBmtpBhgdK/D4iz3k8m08/mIPY4XIpSsW8qvXr+KWjTWUlxSlPVGSJEmSJEkyWEvT2RuH+8m1Jty/u53uviGWVJTxiavOZ3tDlhWL56Q9T5IkSZIkSfo3DNbSNDM4MsY3DhykOd/G9187SibAtWur2N6Q5Zq1VZQUZdKeKEmSJEmSJL0tg7U0TTzb2Usun/CVvR2cGBylfuFsPnXTGj66pY6lleVpz5MkSZIkSZJ+JIO1NIWdGBzhq/s6acknPNPRS2lxhls2LqWpMctlKxaRyYS0J0qSJEmSJEnjZrCWppgYI/k3jtGcb+ORZ7oYHCmwdmkFf3D7eu68uJb5s0vTnihJkiRJkiS9LwZraYro6Rvi/j3ttOQTXjvcz9yyYu7eUseOxiybaisJwbOpJUmSJEmSNLUZrKVJbKwQeeKlHprz/197dx5lZ13nefz9rcoGSchC9qRCWAIhrIEU0IIIKAiRtRtJnNYW7W5HbbVnHO2jPXbLqNObZ+zpnpm29UxzunUcUglCGxVFBD2KClSFAGGVDNutJBDIvpGl6jt/3AfP7SKVVJaq51bV+3VOndz7+z237rdyvvW7z/3Uc5/nJe59ah17O5PmWeP46KUnseCMKRw9zF9hSZIkSZIkDRymXVIdqmzYwZK2Ckvb2nl5y+scO3IYH7zoeG6a38RJk0aVXZ4kSZIkSZLUKwyspTrx+p4OfvTkKyxprXD/qteIgLedPJFbrp3LZXMmM2xIQ9klSpIkSZIkSb3KwFoq2dMvb6GltcKdK1azaccepo89ik9efjI3njuDaWOPKrs8SZIkSZIkqc8YWEsl2LZrL999dA2LWys8WtnEsMYGLj9tMouam7jwxAk0NHgBRUmSJEmSJA0+BtZSH8lMHn5pIy2tFb732Fp27O5g9qRR/NnVc7lh3nTGjxxWdomSJEmSJElSqQyspV62ftsu7lyxmsWtFVat28bRwxq55sxpLDyviXlNY4nwaGpJkiRJkiQJDKylXtHRmdy/6jVaWl/inidfYU9HMm/mWP76d87gXWdOY9Rwf/UkSZIkSZKkrkzNpCNo9aadLG2rsLStndWbdjLu6KG874JZLGxu4pQpo8suT5IkSZIkSaprBtbSYdq9t5MfP/UKi1sr/PzZV8mEt86ewGcXzOHyuZMZPqSx7BIlSZIkSZKkfsHAWjpEz76ylZbWCnesWM2G7buZOmYEH79sNu8+dwZN448uuzxJkiRJkiSp3zGwlg7C9l17+f7KtbS0Vlj+4kaGNATvOHUyC89r4uLZE2ls8AKKkiRJkiRJ0qEysJYOIDN5tH0zLa0vseyRNWzf3cEJE0fypwvmcMO8GUwcPbzsEiVJkiRJkqQBwcBa6sbG7bu5c8VqlrRVePrlrYwY2sDVZ05jYXMT848bR4RHU0uSJEmSJElHkoG1VKOzM/nVc+tZ3Frh7sdfZndHJ2fOGMN/veF0rjlrGseMGFp2iZIkSZIkSdKAZWAtAWs37+T2tnaWLK9Q2bCTY0YM4d+dP5Ob5jcxd9oxZZcnSZIkSZIkDQoG1hq09nR0cu9T61jSVuGnz6yjM+EtJx7Lp644hXeeNoURQxvLLlGSJEmSJEkaVAysNeg89+o2WtoqfHv5al7btotJo4fzkUtO5Kb5TRx37Miyy5MkSZIkSZIGLQNrDQo7d3dw18q1tLRWeOiFDTQ2BJfNmcSi5ibedvJEhjQ2lF2iJEmSJEmSNOgZWGtAe3z1Zha3vsR3Vqxh6669zDr2aP7kylO48ZwZTDpmRNnlSZIkSZIkSaphYK0BZ/OOPXzn0dUsfqjCk2u3MHxIAwvOmMrC5ibOP348EVF2iZIkSZIkSZL2wcBaA0Jm8uDzG2hprXDXyrXs2tvJ3KnH8MXrTuPas6cz5qihZZcoSZIkSZIk6QAMrNWvrdvyOrc/3M6S1govrN/B6BFDePf8GSxqnsnp08eUXZ4kSZIkSZKkg2BgrX5nb0cnP33mVVraKtz39Do6OpPzjh/PJ94+m6tOn8pRwxrLLlGSJEmSJEnSITCwVr/x4vrtLGmrsLStnXVbdzFh1HD+8K0ncNP8GZwwcVTZ5UmSJEmSJEk6TAbWqmuv7+ng7ideZvFDFX713HoaAi45ZRILm5u4bM4khjY2lF2iJEmSJEmSpCPEwFp16ck1W1jSVuHOFavZvHMPTeOP4lNXnMyN5zYxZcyIssuTJEmSJEmS1AsMrFU3tr6+h2WPrqGltcJj7ZsZ1tjAO0+fwqLmJn7rhGNpaIiyS5QkSZIkSZLUiwysVarMpO3FjSx+qMJdK9eyc08Hp0wezeevmcv1Z09n3MhhZZcoSZIkSZIkqY8YWKsUr23bxR0Pt7O4tcJzr25n5LBGrp83jYXNMzlrxhgiPJpakiRJkiRJGmwMrNVnOjqTnz37Ki0PVfjxU6+wtzM597hx/M2NJ/KuM6YycrjtKEmSJEmSJA1mJoTqdZUNO1jaVmHp8nbWbn6d8SOH8YELZ7GwuYmTJo0uuzxJkiRJkiRJdcLAWr1i194O7nnyFVpaK9y/6jUA3jp7In929Vzecepkhg1pKLlCSZIkSZIkSfXGwFpH1DMvb6WltcKdK9rZuGMP08cexR+/fTbvnt/E9LFHlV2eJEmSJEmSpDpmYK3Dtm3XXr736Bpa2iqseGkTQxuDK+ZO4abmJi46aQKNDV5AUZIkSZIkSdKBGVjrkGQmKyqbaHmowncfW8OO3R2cNGkUn3vXqdwwbzrHjhpedomSJEmSJEmS+hkDax2UDdt3c8fD7bS0Vnh23TaOGtrINWdNZWHzTM6ZOZYIj6aWJEmSJEmSdGgMrHVAnZ3J/ateo6Wtwo+eeJk9HcnZTWP5y98+g6vPnMroEUPLLlGSJEmSJEnSAGBgrW6t2bSTpW3tLGmrsHrTTsYePZT3XnAcC5ubmDPlmLLLkyRJkiRJkjTAGFjr39i9t5N7n3qFxa0Vfvbsq2TCRSdN4DNXzeHyuZMZMbSx7BIlSZIkSZIkDVAG1gJg1bptLGmr8O3l7azfvpspx4zgY5eexE3zm2gaf3TZ5UmSJEmSJEkaBAysB7Edu/fy/cfW0tJaoe3FjQxpCN5+6iQWNc/k4pMn0tjgBRQlSZIkSZIk9R0D60EmM1m5ejOLWysse2QN23bt5YQJI/nsVXP47XNmMHH08LJLlCRJkiRJkjRIGVgPEpt27OZfV6xmcWuFp1/eyoihDSw4YyqLmmfSPGscER5NLUmSJEmSJKlcBtYDWGdn8sBz62lpq/CDx19m995Ozpg+hi9dfzrXnj2NY0YMLbtESZIkSZIkSfqNXg2sI+JW4GpgXWaevo/5AP4OWADsAG7OzIeLufcDnys2/VJm/ktv1jqQvLLldW5f3k5La4WXNuzgmBFDeE9zEzc1N3HatDFllydJkiRJkiRJ+9TbR1j/M/A/gW90M38VMLv4Oh/4KnB+RIwHPg/MBxJYHhHLMnNjL9fbb+3p6OQnT6+jpbXCT55ZR2fCBSeM55OXn8yVp09hxNDGskuUJEmSJEmSpP3q1cA6M38WEbP2s8l1wDcyM4EHImJsREwFLgHuycwNABFxD3AlcFtv1ttffeWeX3PbQy/x6tZdTBw9nA+/7URumt/ErAkjyy5NkiRJkiRJknqs7HNYTwcqNffbi7Huxt8kIj4EfAhg5syZvVNlnXvhte2cNWMMC5tncukpExnS2FB2SZIkSZIkSZJ00MoOrA9bZn4d+DrA/Pnzs+RySvHfF55NQ0OUXYYkSZIkSZIkHZayD8VdDTTV3J9RjHU3rn0wrJYkSZIkSZI0EJQdWC8Dfi+qLgA2Z+Za4G7giogYFxHjgCuKMUmSJEmSJEnSANWrpwSJiNuoXkBxQkS0A58HhgJk5j8CdwELgFXADuADxdyGiPgi0Fp8qy+8cQFGSZIkSZIkSdLA1KuBdWa+5wDzCfxRN3O3Arf2Rl2SJEmSJEmSpPpT9ilBJEmSJEmSJEkCDKwlSZIkSZIkSXXCwFqSJEmSJEmSVBcMrCVJkiRJkiRJdcHAWpIkSZIkSZJUFwysJUmSJEmSJEl1wcBakiRJkiRJklQXDKwlSZIkSZIkSXXBwFqSJEmSJEmSVBcMrCVJkiRJkiRJdcHAWpIkSZIkSZJUFwysJUmSJEmSJEl1wcBakiRJkiRJklQXDKwlSZIkSZIkSXXBwFqSJEmSJEmSVBcMrCVJkiRJkiRJdcHAWpIkSZIkSZJUFwysJUmSJEmSJEl1wcBakiRJkiRJklQXIjPLruGIiYhXgRfLrqMkE4DXyi5Cg5K9pzLYdyqLvacy2Hcqi72nMth3Kou9pzIM9r47LjMndh0cUIH1YBYRbZk5v+w6NPjYeyqDfaey2Hsqg32nsth7KoN9p7LYeyqDfbdvnhJEkiRJkiRJklQXDKwlSZIkSZIkSXXBwHrg+HrZBWjQsvdUBvtOZbH3VAb7TmWx91QG+05lsfdUBvtuHzyHtSRJkiRJkiSpLniEtSRJkiRJkiSpLhhYS5IkSZIkSZLqgoF1PxARV0bEMxGxKiI+s4/54RHRUsw/GBGzauY+W4w/ExHv7Mu61b/1oO8+GRFPRsRjEXFvRBxXM9cREY8UX8v6tnL1dz3ovZsj4tWaHvuDmrn3R8Szxdf7+7Zy9Wc96Lu/rem5X0fEppo51zwdkoi4NSLWRcTj3cxHRPx90ZePRcQ5NXOudzpkPei93y16bmVE/DIizqqZe6EYfyQi2vquavV3Pei7SyJic81r6p/XzO33dVranx703qdr+u7xYt9ufDHnmqdDEhFNEfGTIjd5IiL+eB/buK/XDc9hXeciohH4NXA50A60Au/JzCdrtvkocGZmfjgiFgE3ZObCiJgL3AacB0wDfgycnJkdff1zqH/pYd9dCjyYmTsi4iPAJZm5sJjblpmjSihd/VwPe+9mYH5mfqzLY8cDbcB8IIHlwLmZubFvqld/1ZO+67L9x4F5mfnB4r5rng5JRFwMbAO+kZmn72N+AfBxYAFwPvB3mXm+650OVw967y3AU5m5MSKuAm7JzPOLuReovg6/1pc1q//rQd9dAnwqM6/uMn5Qr9NSVwfqvS7bXgP8x8y8rLj/Aq55OgQRMRWYmpkPR8Roqvtr13d5b+u+Xjc8wrr+nQesysznMnM3sBi4rss21wH/Uty+HXh7REQxvjgzd2Xm88Cq4vtJB3LAvsvMn2TmjuLuA8CMPq5RA1NP1rzuvBO4JzM3FC/k9wBX9lKdGlgOtu/eQ/UPwtJhycyfARv2s8l1VN9cZ2Y+AIwt3vy43umwHKj3MvOXNW+K3c/TEdGDNa87h7N/KB1s77mfpyMiM9dm5sPF7a3AU8D0Lpu5r9cNA+v6Nx2o1Nxv580N/pttMnMvsBk4toePlfblYHvn94Ef1NwfERFtEfFARFzfGwVqwOpp7/1O8ZGp2yOi6SAfK3XV496J6umPjgfuqxl2zVNv6a43Xe/Ul7ru5yXwo4hYHhEfKqkmDVy/FRGPRsQPIuK0Ysw1T30iIo6mGgp+u2bYNU+HLaqn7p0HPNhlyn29bgwpuwBJ/VtEvJfqx1TeVjN8XGaujogTgPsiYmVm/r9yKtQA9F3gtszcFRH/nuonTC4ruSYNHouA27ucXss1T9KAVJwC7veBi2qGLyrWvEnAPRHxdHH0onS4Hqb6mrqt+Jj8vwKzS65Jg8s1wC8ys/ZobNc8HZaIGEX1jyD/ITO3lF1Pf+ER1vVvNdBUc39GMbbPbSJiCDAGWN/Dx0r70qPeiYh3AP8ZuDYzd70xnpmri3+fA35K9S+JUk8csPcyc31Nv/1v4NyePlbqxsH0ziK6fEzUNU+9qLvedL1Tr4uIM6m+zl6XmevfGK9Z89YBd+IpB3WEZOaWzNxW3L4LGBoRE3DNU9/Z336ea54OWkQMpRpWfysz79jHJu7rdcPAuv61ArMj4viIGEZ1AV3WZZtlwBtXDL0RuC+rV9NcBiyKiOERcTzVv04/1Ed1q387YN9FxDzga1TD6nU14+MiYnhxewJwIeAFUdRTPem9qTV3r6V6LjCAu4Erih4cB1xRjEkH0pPXWiJiDjAO+FXNmGueetMy4PeKK8hfAGzOzLW43qmXRcRM4A7gfZn565rxkcWFo4iIkVR77/FyqtRAExFTimsxERHnUc0r1tPD12npcETEGKqfGv5OzZhrng5ZsZ79E9WLGH+lm83c1+uGpwSpc5m5NyI+RrUxG4FbM/OJiPgC0JaZy6j+AnwzIlZRvZDAouKxT0TEEqpvnPcCf9TlI8zSPvWw774MjAKWFvuVL2XmtcCpwNciopPqTuZfeQVv9VQPe+8TEXEt1XVtA3Bz8dgNEfFFqm9qAL7Q5eN80j71sO+g+vq6uPij8Btc83TIIuI24BJgQkS0A58HhgJk5j8Cd1G9avwqYAfwgWLO9U6HpQe99+dUr4nzD8V+3t7MnA9MBu4sxoYA/zczf9jnP4D6pR703Y3ARyJiL7ATWFS85u7zdbqEH0H9VA96D+AG4EeZub3moa55OhwXAu8DVkbEI8XYnwIzwX29A4l/+55LkiRJkiRJkqRyeEoQSZIkSZIkSVJdMLCWJEmSJEmSJNUFA2tJkiRJkiRJUl0wsJYkSZIkSZIk1QUDa0mSJEmSJElSXTCwliRJkrqIiG19/Hy/7OPnGxsRH+3L55QkSZJ6wsBakiRJ6mURMWR/85n5lj5+zrGAgbUkSZLqjoG1JEmS1AMRcWJE/DAilkfEzyNiTjF+TUQ8GBErIuLHETG5GL8lIr4ZEb8AvlncvzUifhoRz0XEJ2q+97bi30uK+dsj4umI+FZERDG3oBhbHhF/HxHf20eNN0fEsoi4D7g3IkZFxL0R8XBErIyI64pN/wo4MSIeiYgvF4/9dES0RsRjEfFfirGREfH9iHg0Ih6PiIW99z8sSZIkwX6P9JAkSZL0G18HPpyZz0bE+cA/AJcB9wMXZGZGxB8AfwL8p+Ixc4GLMnNnRNwCzAEuBUYDz0TEVzNzT5fnmQecBqwBfgFcGBFtwNeAizPz+Yi4bT91ngOcmZkbiqOsb8jMLRExAXggIpYBnwFOz8yzASLiCmA2cB4QwLKIuBiYCKzJzHcV2405pP85SZIkqYcMrCVJkqQDiIhRwFuApcUBzwDDi39nAC0RMRUYBjxf89Blmbmz5v73M3MXsCsi1gGTgfYuT/dQZrYXz/sIMAvYBjyXmW9879uAD3VT7j2ZueGN0oG/KMLnTmB68ZxdXVF8rSjuj6IaYP8c+G8R8dfA9zLz5908pyRJknREGFhLkiRJB9YAbHrjiOQu/gfwlcxcFhGXALfUzG3vsu2umtsd7Ht/vCfb7E/tc/4u1aOkz83MPRHxAjBiH48J4C8z82tvmog4B1gAfCki7s3MLxxkPZIkSVKPeQ5rSZIk6QAycwvwfES8GyCqziqmxwCri9vv76USngFOiIhZxf2enkt6DLCuCKsvBY4rxrdSPS3JG+4GPlgcSU5ETI+ISRExDdiRmf8H+DLV041IkiRJvcYjrCVJkqQ3Ozoiak/V8RWqRyt/NSI+BwwFFgOPUj2iemlEbATuA44/0sUU58D+KPDDiNgOtPbwod8CvhsRK4E24Oni+62PiF9ExOPADzLz0xFxKvCr4pQn24D3AicBX46ITmAP8JEj+oNJkiRJXURmll2DJEmSpAOIiFGZuS2qifL/Ap7NzL8tuy5JkiTpSPKUIJIkSVL/8IfFRRifoHqqjzedb1qSJEnq7zzCWpIkSZIkSZJUFzzCWpIkSZIkSZJUFwysJUmSJEmSJEl1wcBakiRJkiRJklQXDKwlSZIkSZIkSXXBwFqSJEmSJEmSVBf+PxkiJa2FYv/wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1800x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}