twitter_sentiment_improvements_fwd_and_ensemble_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "twitter_sentiment_improvements_fwd_and_ensemble_model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maxmatical/TMLS_NLP/blob/master/twitter_sentiment_improvements_fwd_and_ensemble_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMgx99NYbpIV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "!pip install git+https://github.com/fastai/fastai.git"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnCNplZFCBp4",
        "colab_type": "code",
        "outputId": "da70ac17-63ba-4c55-fe98-a90cbc500c9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Set up environment and download course-v3\n",
        "!curl -s https://course.fast.ai/setup/colab | bash"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updating fastai...\n",
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKq5YwwRCM5r",
        "colab_type": "code",
        "outputId": "cbd38abd-1207-4d73-ae21-f18483d4b211",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from fastai import *\n",
        "from fastai.text import *\n",
        "from fastai.utils.show_install import *\n",
        "from fastai.callbacks import *\n",
        "show_install()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "```text\n",
            "=== Software === \n",
            "python        : 3.6.8\n",
            "fastai        : 1.0.60.dev0\n",
            "fastprogress  : 0.1.21\n",
            "torch         : 1.3.0+cu100\n",
            "nvidia driver : 418.67\n",
            "torch cuda    : 10.0.130 / is available\n",
            "torch cudnn   : 7603 / is enabled\n",
            "\n",
            "=== Hardware === \n",
            "nvidia gpus   : 1\n",
            "torch devices : 1\n",
            "  - gpu0      : 11441MB | Tesla K80\n",
            "\n",
            "=== Environment === \n",
            "platform      : Linux-4.14.137+-x86_64-with-Ubuntu-18.04-bionic\n",
            "distro        : #1 SMP Thu Aug 8 02:47:02 PDT 2019\n",
            "conda env     : Unknown\n",
            "python        : /usr/bin/python3\n",
            "sys.path      : \n",
            "/env/python\n",
            "/usr/lib/python36.zip\n",
            "/usr/lib/python3.6\n",
            "/usr/lib/python3.6/lib-dynload\n",
            "/usr/local/lib/python3.6/dist-packages\n",
            "/usr/lib/python3/dist-packages\n",
            "/usr/local/lib/python3.6/dist-packages/IPython/extensions\n",
            "/root/.ipython\n",
            "```\n",
            "\n",
            "Please make sure to include opening/closing ``` when you paste into forums/github to make the reports appear formatted as code sections.\n",
            "\n",
            "Optional package(s) to enhance the diagnostics can be installed with:\n",
            "pip install distro\n",
            "Once installed, re-run this utility to get the additional information\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYky0SyQCZPU",
        "colab_type": "text"
      },
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8bWDpNtCQNY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('testset1.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLdscs8CCd--",
        "colab_type": "code",
        "outputId": "e46b877d-8ccb-4612-e1cb-fb6758efb261",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "print(len(df))\n",
        "\n",
        "df.head(5)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5585\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>date</th>\n",
              "      <th>text</th>\n",
              "      <th>location</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.130000e+18</td>\n",
              "      <td>Evolutionomics</td>\n",
              "      <td>Sun May 19 23:57:10 +0000 2019</td>\n",
              "      <td>By combining short positions on #Gold $GOLD $G...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>bearish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.130000e+18</td>\n",
              "      <td>macrohedged</td>\n",
              "      <td>Sun May 19 23:55:02 +0000 2019</td>\n",
              "      <td>Here are the #Volatility levels for the majors...</td>\n",
              "      <td>England</td>\n",
              "      <td>unrelated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.130000e+18</td>\n",
              "      <td>Evolutionomics</td>\n",
              "      <td>Sun May 19 23:54:06 +0000 2019</td>\n",
              "      <td>We have little insight into what will happen t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.130000e+18</td>\n",
              "      <td>Evolutionomics</td>\n",
              "      <td>Sun May 19 23:50:59 +0000 2019</td>\n",
              "      <td>This brings us to #Gold miners. As a group, $G...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.130000e+18</td>\n",
              "      <td>macrohedged</td>\n",
              "      <td>Sun May 19 23:50:04 +0000 2019</td>\n",
              "      <td>Good morning Asia, here are the benchmark futu...</td>\n",
              "      <td>England</td>\n",
              "      <td>unrelated</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             id            name  ... location  sentiment\n",
              "0  1.130000e+18  Evolutionomics  ...      NaN    bearish\n",
              "1  1.130000e+18     macrohedged  ...  England  unrelated\n",
              "2  1.130000e+18  Evolutionomics  ...      NaN    neutral\n",
              "3  1.130000e+18  Evolutionomics  ...      NaN    neutral\n",
              "4  1.130000e+18     macrohedged  ...  England  unrelated\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liW3zbbzCktu",
        "colab_type": "code",
        "outputId": "e5cae54a-996c-4145-d458-1873b3e0f197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        }
      },
      "source": [
        "df['sentiment'].value_counts().plot(kind='bar')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8dc422278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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51N071wOrk6xKchBwFrB5iWuQpGYt6Z5+Ve1Kcj5wDXAAcHlV3baUNUhSy5a8T7+qtgBb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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XscVftJYDTQt",
        "colab_type": "text"
      },
      "source": [
        "Unrelated and neutral sentiments dominate the data currently (although this might not be the case in the future with the full dataset). It might be better to use a stratified sampling technique for classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-GUhvxkELES",
        "colab_type": "code",
        "outputId": "7ede62c1-ba0e-43d6-8c87-416f7025bc2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
        "sss.get_n_splits(df['text'], df['sentiment'])\n",
        "\n",
        "print(sss)\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "StratifiedShuffleSplit(n_splits=5, random_state=0, test_size=0.2,\n",
            "            train_size=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THDVEb8tFFGN",
        "colab_type": "code",
        "outputId": "7b601bba-c60b-4d3b-cc60-25dd3f4c75ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_idx, val_idx = next(sss.split(df['text'], df['sentiment']))\n",
        "\n",
        "print(len(train_idx), len(val_idx))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4468 1117\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnI14U3zKskb",
        "colab_type": "text"
      },
      "source": [
        "Verifying that the stratified split preseves the distribution of the labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPqoB9P-JzhJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train = df.iloc[train_idx, :]\n",
        "df_val = df.iloc[val_idx, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loavhYCJKdmz",
        "colab_type": "code",
        "outputId": "58ee9c91-9e4f-4c96-9378-3223ee5c8764",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df_train['sentiment'].value_counts().plot(kind='bar')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8d97c1048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l6PeU5K1V9ev7atPC7GF4XFXVxVMvZoYleUFV/f3YeP3HcJz+wiX5tap629h4/ceY1QMS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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaoWfiBQKQG2",
        "colab_type": "code",
        "outputId": "297aaa5a-d3b7-44d4-ace5-12864fc612c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "df_val['sentiment'].value_counts().plot(kind='bar')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8d978b5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//vu+alsKw320a4Fbk3y6W78Q2NJjPdPsvwJnA39aVWckeR3wsz3XNI3ePLT8OPCGofUC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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrWWL-PxKiZt",
        "colab_type": "text"
      },
      "source": [
        "Looks like the distribution of the labels in train and val are the same, so we can start modelling now"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnlWeHCMCsPI",
        "colab_type": "text"
      },
      "source": [
        "# Handling Imbalanced Data: Oversampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXjuHfxDD0fw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_class_bull = df_train[df_train['sentiment'] == 'bullish']\n",
        "df_class_bear = df_train[df_train['sentiment'] == 'bearish']\n",
        "df_class_neutral = df_train[df_train['sentiment'] == 'neutral']\n",
        "df_class_unrelated = df_train[df_train['sentiment'] == 'unrelated']\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5yI-FewEDT-",
        "colab_type": "code",
        "outputId": "aef6a038-720a-48f9-98ed-f67b8ab3c74a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(df_class_unrelated))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2298\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbBCJgpZEk8O",
        "colab_type": "text"
      },
      "source": [
        "Want to oversample so that every class has 2298 counts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcT0MDt8EoSn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_class_bull_over = df_class_bull.sample(len(df_class_unrelated), replace = True)\n",
        "df_class_bear_over = df_class_bear.sample(len(df_class_unrelated), replace = True)\n",
        "df_class_neutral_over = df_class_neutral.sample(len(df_class_unrelated), replace = True)\n",
        "\n",
        "\n",
        "df_train_oversampled = pd.concat([df_class_bull_over, df_class_bear_over, df_class_neutral_over, df_class_unrelated], axis = 0)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxoV7EH5FPIY",
        "colab_type": "text"
      },
      "source": [
        "Look at distribution of the labels in the oversampled data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xA3owdg9FMWP",
        "colab_type": "code",
        "outputId": "b7dbe4de-cf59-48c6-dc1f-4e307ea3ee86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df_train_oversampled['sentiment'].value_counts().plot(kind='bar')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8d96e2a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bGq//II7Tn7skb6uqN2yvbVLYpz93U1c5buy1isVn36p613BDktf0VcwEewrwOaY/CV6A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\n6IxFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "URdbzs4tDkAv",
        "colab_type": "text"
      },
      "source": [
        "# Setting up data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F80cVObhDAwN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PATH = '/content/'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axHubSwEgG9J",
        "colab_type": "code",
        "outputId": "e98245be-9746-4e40-c3cd-a89603694215",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ls{PATH}"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;36mdata\u001b[0m@  \u001b[01;36mmodels\u001b[0m@  testset1.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWuZHdr2Dvea",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bs = 128"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzpFdPEIDx3C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# data_lm = TextLMDataBunch.from_df(path = PATH, \n",
        "#                                   train_df = df_train_oversampled,\n",
        "#                                   valid_df = df_val,\n",
        "#                                   text_cols='text',\n",
        "#                                   min_freq = 3,\n",
        "#                                   bs = bs) \n",
        "\n",
        "\"\"\"\n",
        "Original databunch, no need for oversampling since were not doing classification\n",
        "so we don't need to worry about class imbalance\n",
        "\n",
        "\"\"\"\n",
        "data_lm = TextLMDataBunch.from_csv(path = PATH, \n",
        "                                   csv_name = 'testset1.csv', \n",
        "                                   text_cols='text',\n",
        "                                   valid_pct = 0.1,\n",
        "                                   min_freq = 3,\n",
        "                                   bs = bs) \n",
        "\n",
        "\n",
        "data_lm.save('data_lm_fwd')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z90o1knEjWpF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "data_clas = TextClasDataBunch.from_df(path = PATH,\n",
        "                                        train_df = df_train_oversampled,\n",
        "                                        valid_df = df_val,\n",
        "                                        vocab = data_lm.train_ds.vocab,\n",
        "                                        text_cols = 'text',\n",
        "                                        label_cols = 'sentiment',\n",
        "                                        bs = bs)\n",
        "\n",
        "\n",
        "data_clas.save('data_clas_fwd')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "42rRI25ZXLRt"
      },
      "source": [
        "# Training forward model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3C1sDfiRXLSP"
      },
      "source": [
        "## Language Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "p2nla5LOXLSS",
        "colab": {}
      },
      "source": [
        "data_lm = load_data(PATH, 'data_lm_fwd', bs = bs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KTPILPpzXLSf"
      },
      "source": [
        "Look at what the preprocessed text looks like"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "570805b1-e684-48a6-ee25-32faa07b391f",
        "id": "pIVL3pbEXLSj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "data_lm.show_batch()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>idx</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>sentiment , then we continue down . $ gold to 1320 . https : / / t.co / tioikccioi xxbos xxmaj long $ gold $ gc https : / / t.co / xxunk xxbos xxmaj how # hedgefunds are positioned in futures . $ xxup tnx $ xxup tyx $ xxup spx $ xxup rut $ xxup ndx $ xxup vix $ xxup gld $ xxup eur $ xxup usd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>) ( xxup nyse : $ xxup trx ) https : / / t.co / xxunk xxbos $ xxup gld rose by xxunk % . xxmaj currently xxunk at xxunk . https : / / t.co / xxunk xxbos xxmaj gold xxmaj miners could start a new rally from this price point , says xxmaj joe xxmaj friday . xxmaj one of the most important support tests in years is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>/ / t.co / 5cmhudsbq6 xxbos xxmaj gold | $ xxup gold | $ xxup xau $ xxup usd \\n \\n  xxup gold xxmaj long \\n \\n  xxmaj long or short it on xxup wcx : https : / / t.co / gfnsxoenz8 https : / / t.co / xxunk xxbos xxmaj it was a pleasure speaking with @lukegromen \\n  recently . xxmaj interesting his pointing out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>is $ 1255 - 60 - this is the intersection of the 200dma and the uptrendline from the xxmaj august low https : / / t.co / ayikquvrch xxbos * xxmaj broken xxmaj record xxmaj alert * xxmaj junior xxmaj miners weekly still \" bullish \" longer - term and will soon break higher . \\n  $ xxup gdxj $ xxup gld # xxmaj gold # goldminers # preciousmetals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>like xxmaj gold should be safe now from more downside \\n \\n  # xxmaj metals $ xxup gc # xxup gc # xxup gld $ xxup gld https : / / t.co / 43das3yitl xxbos i still think xxunk is a xxunk target for $ xxmaj gold by year 's end . xxbos xxmaj read our xxup free xxmaj online xxmaj daily xxmaj newspaper , catch up with latest</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Un5HmTZaXLSq",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "Default AdamW\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "learn = language_model_learner(data_lm, \n",
        "                               arch=AWD_LSTM, \n",
        "                               loss_func = FlattenedLoss(LabelSmoothingCrossEntropy, axis=-1),\n",
        "                               drop_mult=1.).to_fp16()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "ab3e4cfe-f234-4c34-aafb-d806a56ef827",
        "id": "ZnM-pNqrXLSy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot(suggestion = True, skip_end=15)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
            "Min numerical gradient: 1.00E-01\n",
            "Min loss divided by 10: 5.25E-02\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eVjrXzJ7G8s3VPPjK5rDLSQoKEhGRfXTuzNGUjC3k5oWrqWts6fkN/ZyCRERk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\n+uT/A4Gcx0BvYQa1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqBGk1OpZFqR",
        "colab_type": "text"
      },
      "source": [
        "fitting learner\n",
        "\n",
        "fit_fc for new optimizers\n",
        "\n",
        "fit_one_cycle for adamw/lamb/LookaheadAdam"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "l9weMXktXLS5",
        "outputId": "4a2e4368-44c8-4cdb-e1ef-85575bf7d376",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "lr_init = 0.1\n",
        "lr = lr_init\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "1 cycle policy\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "learn.fit_one_cycle(10,\n",
        "                   lr,\n",
        "                   moms=(0.8,0.7),\n",
        "                   callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "                                                             name='lm_stage1')])\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "1 cycle policy without clipping momentum\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# learn.fit_one_cycle(5,\n",
        "#                    lr,\n",
        "#                    callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "#                                                              name='lm_stage1'),])\n",
        "\n",
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 29,
      "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>4.591938</td>\n",
              "      <td>3.093186</td>\n",
              "      <td>0.591674</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.534665</td>\n",
              "      <td>2.543967</td>\n",
              "      <td>0.723705</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.174967</td>\n",
              "      <td>2.528317</td>\n",
              "      <td>0.740491</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.985215</td>\n",
              "      <td>2.424199</td>\n",
              "      <td>0.762076</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.886685</td>\n",
              "      <td>2.400230</td>\n",
              "      <td>0.764130</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.774359</td>\n",
              "      <td>2.339348</td>\n",
              "      <td>0.778616</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.685539</td>\n",
              "      <td>2.289477</td>\n",
              "      <td>0.787411</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.598691</td>\n",
              "      <td>2.250554</td>\n",
              "      <td>0.793995</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.527482</td>\n",
              "      <td>2.234207</td>\n",
              "      <td>0.797723</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.473707</td>\n",
              "      <td>2.230591</td>\n",
              "      <td>0.798951</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.5916740894317627.\n",
            "Better model found at epoch 1 with accuracy value: 0.7237054109573364.\n",
            "Better model found at epoch 2 with accuracy value: 0.7404910922050476.\n",
            "Better model found at epoch 3 with accuracy value: 0.7620758414268494.\n",
            "Better model found at epoch 4 with accuracy value: 0.7641295194625854.\n",
            "Better model found at epoch 5 with accuracy value: 0.7786160707473755.\n",
            "Better model found at epoch 6 with accuracy value: 0.7874106764793396.\n",
            "Better model found at epoch 7 with accuracy value: 0.7939954996109009.\n",
            "Better model found at epoch 8 with accuracy value: 0.7977231740951538.\n",
            "Better model found at epoch 9 with accuracy value: 0.7989508509635925.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Lh9PhKKXUaRmOI/pFxpjZxpj5w7CuIXPj2ePo6Ozm2Q/3Ox2KUkqdlgElehEZLyIee/xC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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PR56NNgqUIAH",
        "colab_type": "code",
        "outputId": "04604a06-801e-4848-d7a1-7a4e7170d70d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "learn.recorder.plot_lr(show_moms=True)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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23Di4Ix8s38GanWVWx1FKOWB49xh+yi+luq7B6ihKUXSomqe+ySKjY2tuHNzJ\n6jjqGKcstI0xLwFTgS+BZOBxY8zrzg6mTm1pXin1jUbnZ7u5By9KoV1EEH/4ciM19fqDWylXN6J7\nDNV1jazeob8cK+s9MWMzVXUNPD8xXTemcUGOLIZ83hgz1xjzgDHmfmPMXBF5viXCqZP7MbuI8EA/\nzumobf3cWVigH89MSCOv6DBvLthmdRyl1CkM7NKGAF8fFuboduzKWrMz9zF7035+d34SXWN0Z2hX\n5MjUkQuO89jFzR1EnZ6jbf2GJkXj76vt0N3dyJRYJvSN560FeWzdX2F1HKXUSYQE+DGgcxsW5eim\nU8o6B4/U8scZm+kVH8Ftw7pYHUedwAkrNBG5XUQygWQR2djk2A5sbLmI6ni27j/E/opqnTbiQf54\naSoRwf78YepGGhq1R686cyKSISLTRWStfdzOFBEdt5vR8O7RZBceYl95ldVRlJd66pssDh6p5YUr\ne+OnF9xc1sn+Zj4BLgNm2j8ePc4xxtzQAtnUSRxt6zdC2/p5jDahAUwe15MNBeW8t3S71XGUe/sY\neA+4Etu4fan9o2omI7rbLnIs1qvaygILsouYtnYPt5/XldT2EVbHUSdxwkLbGFNujNlhjLnWGLMT\nqMLW4i9MRBId+eQicpGIZItInog8dJznA0XkM/vzK0SkU5Pn0kVkuYhstl+N0S2OmvhxazE920cQ\nF6F/LJ7ksvR2jE6J5aXvs9lZqhtiqDNWbIyZaYzZbozZefQ41UkOjNmvish6+5EjIgebPJcoIt+L\nyBYRyWo6nnui7nFhtI0I0jZ/qsUdrqnn0WmZJMWGceeoblbHUafgyGLIy0QkF9gOLAR2ALMdOM8X\neBPbfO5U4FoRST3mZb8Eyowx3YBXgeft5/ph233y/4wxPYHzgDrHviXPV15Vx5pdZTptxAOJCE9P\n6IWfjw8PT8vUbZ7VmXpCRP4pIteKyBVHj5Od4MiYbYy51xjTxxjTB3gDmNbk6Q+BF40xPYABgEev\nFBQRhnePZkleCfUNjVbHUV7kpTnZ7Kuo5vmJ6QT6+VodR52CI5N6ngYGATnGmM7AaOAnB84bAOQZ\nY/KNMbXAFGD8Ma8ZD3xgvz0VGC227YzGABuNMRsAjDGlxhjte2a3JLeEhkaju0F6qHatgnno4hSW\nbSvl89W7rY6j3NMtQB/gIv477e/SU5zjyJjd1LXApwD2gtzPGDMXwBhz2Bhz5Oy+Bdc3vHsM5VV1\nbCgotzqK8hLrdx/kg+U7uHFQR/olascxd+BIoV1njCkFfETExxizAMhw4Lx4oGmVUGB/7LivMcbU\nA+VAFNAdMCIyx76Y58HjfXn8Z1QAACAASURBVAERuU1EVovI6uJi73n7bkF2Ea2C/enTIdLqKMpJ\nrhuQyIDObXj62y0UVlRbHUe5n/7GmAxjzE3GmFvsx62nOMeRMRsAEekIdAbm2x/qDhwUkWkisk5E\nXrRfIT/euR4zbg/tFo2P6HbsqmXUNTTy8LRM4sKDuP/CZKvjKAc5UmgfFJEwYBHwsYi8Bjh78qgf\nMBS43v5xgoiMPvZFxph37D9MMmJivOPqbmOjra3f8O4xusrYg/n4CH++Io3a+kYmz9xsdRzlfpYd\nZ6pec5oETG3yTqMfMAy4H+gPdAFuPt6JnjRuR4YEkBbfiiV5uiBSOd+7S7azZV8FT47vSXiQv9Vx\nlIMcqdTGA0eAe4HvgG04tnp9D9Chyf0E+2PHfY19XnYroBTblZRFxpgS+9uPs4B+DnxNj5e1r4KS\nwzWc1929f0CpU+sSE8bdo5OYvWk/87IKrY6j3MsgYL19YaOj7f0cGbOPmoR92ohdAbDePu2kHvgK\nLxmzhyZFs373QSqqdRmRcp5dpUf4y7wcxqTGcWHPtlbHUafBkS3YK40xjcaYemPMB8Bfsc37O5VV\nQJKIdBaRAGwD88xjXjMTuMl+eyIw39hWf80B0kQkxF6AjwCyHPuWPNvRFe7DukdbnES1hF8P60L3\nuDAen7GJypp6q+Mo93ERkIRtvYuj7f0cGbMRkRSgNbD8mHMjReToFYBReMmYPbRbDA2Nhp+2lVod\nRXkoYwyPfpWJn48PT47vaXUcdZpOtmFNhIg8LCJ/FZExYnMnkA9cfapPbL+qcSe2onkL8LkxZrOI\nPCUi4+wvexeIEpE84D7gIfu5ZcAr2Abv9cBaY8y3Z/5teo6FOba2frHh2tbPGwT4+fDcFWnsLa/m\n5e9zrI6j3Ic5wXHiExwbs8FWgE8xTVri2KeQ3A/8YN/oTIB/NOP347L6dYwk2N9Xp48op5m5YS+L\nc0t44MJk2rUKtjqOOk1+J3nu30AZtqsWvwIewTZ4Xm6MWe/IJzfGzMI27aPpY483uV0NXHWCcz/C\n1uJP2VVU17F2Zxm3DdetVr3JOR3bcP3ARN5ftp0JfeNJS2hldSTl+r7FVlgLEIRt4WI2cNLLYaca\ns+33J5/g3LlA+hkndlOBfr4M7NKGJblaaKvmd/BILU99nUWfDpHcMKij1XHUGTjZ1JEuxpibjTFv\nY2vjlApc6GiRrZrfsrxS6hsNI3R+ttd58KIUosICeWjaRu3Zq07JGJNmjEm3f0zC1rpv+anOU2dm\naLdo8ksq2XNQt2NXzevZWVsor6rjuSvS8PURq+OoM3CyQvs/KzvsbwsW2K9AK4sszCkmLNCPfh21\nd6a3aRXsz+TLerJ5bwXvL9thdRzlZowxa4GBVufwVMOSbBc/luRqmz/VfJZvK+Xz1QX8algXerTT\nbdbd1cmmjvQWkQr7bQGC7fcFMMYY/VtvQcYYFuUUc263KPy1rZ9XGpvWllEpsbz8fQ4X9WpLQusQ\nqyMpFyUi9zW564OtA8hei+J4vO5xYcSGB7I4t4Rr+idaHUd5gOq6Bh6dnklimxDuGZ1kdRx1Fk5Y\nsRljfI0xEfYj3Bjj1+S2FtktbFux7W3J4TptxGuJCE/ZV5w/PmOzbs+uTia8yRGIbc72yXZ5VGdB\nRBjaLZpl20ppbNT/l+rsvfXjNvJLKnlmQi+CA3SbdXeml0bdxNG2fsOTtND2ZgmtQ/j9mO7M31rE\nrMz9VsdRrivLGPOk/XjGGPMxju1/oM7Q0KRoDlTWkrWv4tQvVuokcgsP8bcf85jQN/4/05KU+9JC\n200szCmma0woHdrodAFvd/OQTvSKj2Dy15spr9JNMtRxPezgY6qZDO1m29tA2/yps9HYaHhkeiah\ngX48dkkPq+OoZqCFthuormtgRX4pI7rHWh1FuQA/Xx/+fEU6pYdreP67rVbHUS5ERC4WkTeAeBF5\nvcnxPqA7HjlRbEQQyXHh2uZPnZUpq3azakcZj4ztQVRYoNVxVDPQQtsN/JRfSk19IyOS9S0kZdMr\nvhW3nNuZT1bsYvWOA1bHUa5jL7AaqAbWNDlmAhdamMsrDE2KZuWOA1TXNVgdRbmhoopqnpu9hUFd\n2nDVOQlWx1HN5JSFtogcEpGKY47dIjJdRHTnlBawMKeYQD8fBnZuY3UU5ULuu6A78ZHBPDwtk9p6\n7a2twBizwRjzAdDNGPNBk2Oafcdd5URDk6KprW9klf7yq87Ak99kUVPfyLMT0hDRntmewpEr2n8B\nHgDigQRs2+x+AkwB/uW8aOqoRTnFDOoSRZC/rjxW/xUa6MdT43uSW3SYdxZtszqOci0DRGSuiOSI\nSL6IbBeRfKtDebqBndsQ4Ouj00fUaVuwtYhvN+7jrpHd6BITZnUc1YwcKbTHGWPeNsYcMsZUGGPe\nwbZD5GeA7pziZLsPHGFbcaW29VPHNbpHHGPT2vL6/Dy2l1RaHUe5jneBV4ChQH8gw/5ROVFIgB/9\nOkayWAttdRoqa+p57KtNJMWG8ZsRXa2Oo5qZI4X2ERG5WkR87MfV2Ob/AWjDUCdbZN9pTLddVyfy\nxGU9CfT14dHpmdpbWx1VboyZbYwpMsaUHj2sDuUNhiXFkLWvgpLDNVZHUW7i1bk57DlYxXNXpBHg\np0vnPI0jf6PXA78AioBC++0bRCQYuNOJ2RSwMLuY+MhgusaEWh1Fuai4iCAevDiFZdtKmbZ2j9Vx\nlGtYICIvishgEel39LA6lDc42uZvqbb5Uw7YtKecfy3dznUDE8nopOuwPNHJtmAHwBiTz4k3OljS\nvHFUU7X1jSzbVsq4Pu11YYQ6qesHJDJ9bQFPf5vFyJRY2oQGWB1JWWug/WNGk8cMMMqCLF6lV3wr\nWgX7syS3hPF94q2Oo1xYfUMjD03bSFRYIH+4KMXqOMpJTlloi0gM8GugU9PXG2NudV4sBbB2VxmH\na+p12og6JR8f4bkr0rnk9cU8/W0Wr1zdx+pIykLGmJFWZ/BWvj7Cud2iWJJXgjFGL5KoE3p/2Q42\n7angzev60SrY3+o4ykkcmToyA2gFzAO+bXIoJ1uYU4yfjzCka5TVUZQbSG4bzm9GdGHa2j36trWX\nE5E4EXlXRGbb76eKyC+tzuUthnaLYV95NduKdYGyOr49B6t4ZW4Oo1JiGZvW1uo4yokcKbRDjDF/\nMMZ8boz58ujh9GSKRTnFnNOxNeFB+puucsxdo5LoFBXCo9MzddMM7/Y+MAdob7+fA/zOsjReZliS\nfTt2+2J2pZoyxvDHrzZhDDw1vqe+6+HhHCm0vxGRsU5Pov5H0aFqNu+t0LZ+6rQE+fvyzIQ0dpQe\n4Y35uVbHUdaJNsZ8DjQCGGPqAf3Nq4V0aBNCx6gQlug7S+o4ZmXuZ/7WIn4/pjsJrUOsjqOczJFC\n+x5sxXaVfVfIQyJS4exg3m5xjm2A1vnZ6nSd2y2aK/rF8/bCfLL3H7I6jrJGpYhEYW/BKiKDgHJr\nI3mXod2i+Sn/AHUNumur+q/yqjomf72ZXvER3Dykk9VxVAs4ZaFtjAk3xvgYY4KNMRH2+xEtEc6b\nLcwpJjoskNR2+ketTt9jl6QSHuTHw9M20tiovbW90H3ATKCriCwFPgTusjaSdxmWFM3hmnrW7z5o\ndRTlQl74biulh2t4bkI6fr7aM9sbnPBvWURS7B/7He9ouYjep6HRsDi3mOHdo/Hx0blb6vS1CQ3g\nsUtSWbvrIB+v3GV1HNXCjDFrgRHAEOA3QE9jzEZrU3mXwV2j8RF0l0j1H2t2HuDjFbu4eUhn0hJa\nWR1HtZCTtfe7D7gNePk4z2k/VifK3FNO2ZE6nTaizsoV/eKZtq6AF2ZvZUxqHHERQVZHUi1ERHyB\nsfy3LesYEcEY84qlwbxIq2B/0hMiWZJbzH0XdLc6jrJYbX0jD0/LJD4ymN+P0X8P3uSEV7SNMbfZ\nP448zqFFthMtzC5GxLaVr1JnSkR45vI0ahsamTxzs9VxVMv6GrgZiALCmxyqBQ1LimZDQTkV1XVW\nR1EW+8fifHIKD/PU+J6EBp5yCxPlQRz62xaRIfx8w5oPnZTJ6y3KLSY9vpXu7qfOWqfoUO4encSL\nc7KZm1XIBalxVkdSLSPBGJNudQhvN7RbNG/Mz2P5tlIu7Km9kr3VjpJKXvshl7FpbRndQ8dgb3PK\nmfgi8m/gJWAo0N9+ZJz0JHXGyo/UsW5XmU4bUc3m18O6kBwXzuMzNnG4pt7qOKplzBaRMVaH8HZ9\nE1sTEuDLEp2n7bWMMTz6VSaBvj48cVlPq+MoCzhyRTsDSDXGaOuCFrAkr4RGAyOStdBWzSPAz4dn\nr0hj4t+X8fL32TrYe4efgOki4gPUAQIY7RjVsgL8fBjUJUr7aXux6ev2sDSvlD9d3kvXyXgpR3rL\nbAL0Pa8WsjCniIggP3onRFodRXmQczq25vqBiXywbAcbtN2YN3gFGIxtZ19ty2qhod2i2V5SSUHZ\nEaujqBZ2oLKWp7/dQr/ESK4fkGh1HGURRwrtaCBLROaIyMyjh7ODeSNjDAtzihmWFKP9NVWze/Ci\nFKLDAnl4Wib1uomGp9sNbNJ3Iq333+3Y9aq2t3nm2y1UVNXx3BXp2qrXizkydWSys0Mom+zCQxRW\n1Oj8bOUUEUH+PDmuJ7d/vJZ/Ld3ObcO7Wh1JOU8+8KOIzAZqjj6o7f1aXrfYMNpGBLE4t4RJelXT\nayzLK+HLtQXccV5Xkttqwx9vdtJC296LdbIxZmQL5fFqP2YXAzBcC23lJBf1asv5PeJ4ZW4OF/dq\nR4c2IVZHUs6x3X4E2A9lERFhaFI0c7MKaWg0+OqVTY9XXdfAo19tomNUCHePTrI6jrLYSecnGGMa\ngEYR0S2MWsD8rUX0aBdB21a6YEI5h4jw1Pie+Irw2Feb0JkFnskY86Qx5klsG4693OS+ssDw7jGU\nV9WxsUDXR3iDNxfksb2kkmcuTyPI39fqOMpijkwEPgxkisi7IvL60cORTy4iF4lItojkichDx3k+\nUEQ+sz+/QkQ6HfN8oogcFpH7Hfl67qy8qo41O8sYlaJXs5VztY8M5vdjklmYU8zXG/dZHUc5gYj0\nEpF1wGZgs4isEZFTtptxYMx+VUTW248cETl4zPMRIlIgIn9tvu/G/Q3tFo0ILMrRedqeLrfwEH9f\nuI0JfeMZap+fr7ybI4X2NOCPwCJgTZPjpOzTTt4ELgZSgWtFJPWYl/0SKDPGdANeBZ4/5vlXgNkO\nZHR7i3OLaWg0jEyOtTqK8gI3DelEekIrnvp6MweP1FodRzW/d4D7jDEdjTEdgd8D/zjZCY6M2caY\ne40xfYwxfYA3sP18aOpP2H5WqCbahAaQFt+KxbnFVkdRTtTYaHh4WiahgX48dkkPq+MoF3HKQtsY\n88HxDgc+9wAgzxiTb4ypBaYA4495zXjg6OeaCowWEQEQkcuxzTH0ir2jF2wtJjLEn76Jra2OoryA\nr4/w3BVplB2p48+zt1odRzW/UGPMgqN3jDE/AqGnOMeRMbupa4FPj94RkXOAOOD7Mw3tyYYnxbBu\n90Hdjt2DfbRiJ6t3lvHYJalEhQVaHUe5CEd2hkwSkakikiUi+UcPBz53PLYWU0cV2B877muMMfVA\nORAlImHAH4CTzikUkdtEZLWIrC4udt8rBY2NhoU5RQxPitGFMqrF9Gzfil8N7cyUVbtZkV9qdRzV\nvPJF5I8i0sl+PIatE8nJODJmAyAiHYHOwHz7fR9s88FPOc3PU8bt0zW8ewwNjYZlunmNR9pzsIrn\nZ29lWFI0V/Y77n8b5aUcmTryHvA3oB4YCXwIfOTMUNhaCr5qjDl8shcZY94xxmQYYzJiYtx3bnPm\nnnJKDtcyKkWnjaiWdc/5SSS0Dubh6ZlU1zVYHUc1n1uBGGxTO6bZb9/ajJ9/EjDVvmAe4A5gljGm\n4FQnesq4fbr6JkYSFujHQp2n7XGMMTw6PRMDPDshDfsb80oBjhXawcaYHwAxxuw0xkwGLnHgvD1A\nhyb3E+yPHfc1IuIHtAJKgYHACyKyA/gd8IiI3OnA13RL87cWIaJt/VTLCwnw45kJaeQXV/LX+XlW\nx1HNxBhTZoy52xjTz37cY4wpO8VpjozZR02iybQRbLtQ3mkfs18CbhSRP59hfI/k7+vDkK5RLMop\n1m4/HmbG+r38mF3MAxcma8tU9TOObFhTY39bMNde7O4Bwhw4bxWQJCKd7edMAq475jUzgZuA5cBE\nYL59J7NhR18gIpOBw8YYj13F/mN2EX07RNImVNvdqpY3onsMV/SL528Lt3FxWlt6ttdunu7qVLv2\nGmPGneRpR8ZsRCQFaI1t3D76ea9v8vzNQIYx5mddS7zdsO4xfJ9VSH5JJV1jHPkxqlxd6eEanvx6\nM30TI7lxcCer4ygX5MgV7XuAEOBu4BzgBmzF8UnZ51zfCcwBtgCfG2M2i8hTInJ0sH8X25zsPOA+\nwOsG5uJDNWwoKNduI8pSj1+aSuuQAB6cupE63Z7dnQ3GdiV6MbYryy8fc5yQg2M22ArwKbq9++kb\nkWR713JxjvfMTfd0T36dRWVNAy9cma5rrNRxnfKKtjFmFYCINBpjbjmdT26MmQXMOuaxx5vcrgau\nOsXnmHw6X9PdLLQPuCN1frayUGRIAE9f3pP/+2gt7yzK57cju1kdSZ2ZtsAF2DqCXAd8C3xqjHGo\ne9Opxmz7/cmn+BzvA+87GtibJEaF0CkqhEW5Jdx8bmer46iz9MOWQmZu2Mu953cnKU63WVfH50jX\nkcEikgVstd/vLSJvOT2Zl1iwtYjY8EB6to+wOorychf1asfYtLa89kMueUUnXYesXJQxpsEY850x\n5iZgEJAH/OjJa1zczfDuMSzfVkpNvS4+dmeHqut4dPomkuPCuf28rlbHUS7MkakjfwEuxLZIEWPM\nBmC4M0N5i7qGRhblFjMyOVZXKSuX8OS4XoQE+PKHLzfS0KgzA9yRfcfdK7B1h/ot8Dow3dpU6qhh\nSTFU1TWwZsep1qYqV/bn2VspOlTN8xPTCfBzpJRS3sqhfx3GmN3HPKS/ijeDNTvLOFRdz0jddl25\niJjwQB6/NJU1O8v4cPkOq+Oo0yQiH2JbpNgPeNIY098Y8ydjzIm6h6gWNrhrFH4+wkLdJdJtrcgv\n5eMVu7jl3M706RBpdRzl4hwptHeLyBDAiIi/iNyPbaGMOksLthbh5yOc2y3a6ihK/ceEvvGclxzD\nC99ls/vAEavjqNNzA5CEbRH7MhGpsB+HRKTC4mwKCAv045yOrVms/bTdUnVdAw9Ny6RDm2B+P6a7\n1XGUG3Ck0P4/bG8/xmNr+dQH2+YE6izN3VLIoC5RhAf5Wx1Fqf8QEZ6dkIavj/DQtI3a89eNGGN8\njDHh9iOiyRFujNGFIC5iePcYsvZVUHyoxuoo6jT9ZV4u20sqeW5COiEBjnRIVt7ulIW2MabEGHO9\nMSbOGBNrjLkBuLEFsnm0bcWHyS+u5ILUOKujKPUz7SODeejiFJbmlfLZqmNnjimlzsbwo23+dPqI\nW1m3q4x3Fm3jmowODE3Sd6KVY850Bv99zZrCC83LKgRgdA9t66dc03UDEhnYuQ3PfLuF/eXVVsdR\nymP0bB9BVGjAf9q7KtdXXdfA/V9soG1EEI9e2sPqOMqNnGmhrS0yztK8LYWktosgobVu16pck4+P\n8PyV6dQ1NvLYV5k6hUSpZuLjIwzvHsOinGLt7uMmXp2bw7biSp6fmE6ETvdUp+FMC20dGc5C6eEa\n1uws02kjyuV1ig7l/jHJzNtSxLS12rhCqeYyMiWWsiN1rN990Ooo6hTW7DzAO4vzuXZAIsOStEuY\nOj0nLLSPrlI/znEIaN+CGT3OD1uLaDRooa3cwi3ndmZApzZM/nozew9WWR1HKY8wIikGXx9hwdYi\nq6Ook6iua+CBLzbSvlUwj16iU0bU6TthoX2cVetNV6/rUtuzMC+rkPatgnQ3SOUWfH2EF69Kp6HR\n8IcvtQuJUs2hVYg/5yS2ZkG2Ftqu7KU52eSXVPLCxHTCArX0UadPtzNqYdV1DSzOLeH81DjdDVK5\njY5RoTwytgeLc0v4eMUuq+Mo5RHOS4lh894KCit0sbErWrXjAO8u3c4NgxJ1vwt1xrTQbmFLckuo\nqmvg/B46bUS5l+sHJjIsKZpnZ21hZ2ml1XGUcnujUmxdp3T6iOs5UlvPA19sID4ymIcv1ikj6sxp\nod3C5m0pJCzQj0FdoqyOotRpEbF1IfH1ER74YqN2S1DqLCXHhdO+VZBOH3FBL3yXzY7SI7w4sTeh\nOmVEnQUttFtQY6Nh3pYiRiTHEOCnf/TK/bSPDGbyZT1ZueMA/1qy3eo4Srk1EeG8lFiW5JZQU99g\ndRxltzSvhPeX7eCmwR0Z3FUviqmzo9VeC1q3u4ySwzWM0W4jyo1d0S+eC1LjePH7bHILD1kdRym3\nNio5lsraBlbvKLM6igLKj9Tx+8830CUmlId0yohqBlpot6BZmfsJ8PX5z7w8pdyRiPDshDRCA3z5\n/RcbqGtotDqSUm5rSLcoAvx8mK/ztF3CYzM2UXK4hr9c04fgAF+r4ygPoIV2CzHGMDtzH8O7RxOu\nu0opNxcTHsgzE9LYWFDOG/PzrI6jlNsKCbCt2dF52tabsX4PX2/Yy+/OTyI9IdLqOMpDaKHdQjYU\nlLO3vJqLe7WzOopSzWJsWjuu6BfPX+fnsnrHAavjKOW2RiXHkF9cyY4S7eZjlT0Hq3jsq02c07E1\n/zeiq9VxlAfRQruFzM7ch7+vaFs/5VGeHNeThNYh3DNlPRXVdVbHUcotjbb/XJi3pdDiJN6psdHw\n+8/X09hoePXqPvj5ammkmo/+a2oBxhhmbdrHud2iaRWi00aU5wgP8ucvk/qwv6Kax7/aZHUcpdxS\nhzYh9GgXwfebtdC2wj+X5PNT/gGeGNeTxKgQq+MoD6OFdgvYvLeC3QeqGKvTRpQH6pfYmntGJ/HV\n+r18tW6P1XGUcktjUuNYvfMAJYdrrI7iVbbsq+ClOTlc2DOOq85JsDqO8kBaaLeAWZn78PURLtC2\nfspD/XZkN/p3as1jX21i94EjVsdRyu2M6RlHo4H5W3RRZEupqm3g7k/X0SrEn+euSEdErI6kPJAW\n2k5mjGH2pv0M6RpF69AAq+Mo5RS+PsIrV/dBgN99tp56bfmn1GlJbRdBfGQw32fttzqK13jqm83k\nFR/m1av70EZ/Pisn0ULbyTbtqWB7SaV2G1Eer0ObEJ6e0Is1O8v46wJt+afU6RARxvSMY1FuCZU1\n9VbH8Xhfb9jLpyt3c/uIrgxNirY6jvJgWmg72Yz1e/D3FcamtbU6ilJON75PPFf0jef1H3JZkV9q\ndRyl3MqY1LbU1jeyOLfY6igebVfpER6Zlkm/xEjuvaC71XGUh9NC24kaGg1fb9zLecmxRIbo21LK\nOzx1eS86RoVy95R1urBLqdPQv1NrIkP8+T5Lu484S219I3d9uhYReG1SX/y1lZ9yMv0X5kQr8ksp\nrKhhfJ/2VkdRqsWEBfrx5nX9OHikjns/s/WmVUqdmp+vD6NSYvlhS5Guc3CSl77PZkNBOc9fmU6H\nNtrKTzmfFtpONGP9XkIDfBmdot1GlHdJbR/B5HE9WZxbwls/6nxtpRw1JrUt5VV1rNTdVpvdguwi\n3lmUzw2DErk4TddNqZahhbaT1NQ3MGvTPi7s1ZbgAF+r4yjV4ib178D4Pu15ZW4OP+l8baUcMrx7\nNIF+Prp5TTMrKDvCvZ+tJ6VtOI9dkmp1HOVFnFpoi8hFIpItInki8tBxng8Ukc/sz68QkU72xy8Q\nkTUikmn/OMqZOZ3hx+xiDlXXM75PvNVRlLKEiPDMhDQ6RYVy96frKD6k87VdnQNj9qsist5+5IjI\nQfvjfURkuYhsFpGNInJNy6f3DCEBfgzvHsN3m/brtKtmUlPfwB0fr6WhwfC3G84hyF8vfqmW47RC\nW0R8gTeBi4FU4FoROfbXyF8CZcaYbsCrwPP2x0uAy4wxacBNwL+dldNZZqzfQ3RYAOd2jbI6ilKW\nCQv0483r+1FeZZuv3aCFg8tyZMw2xtxrjOljjOkDvAFMsz91BLjRGNMTuAj4i4hEtlx6z3Jpejv2\nV1SzZleZ1VE8wpNfZ7GxoJyXru5N5+hQq+MoL+PMK9oDgDxjTL4xphaYAow/5jXjgQ/st6cCo0VE\njDHrjDF77Y9vBoJFJNCJWZtVWWUt87KKuDS9PX66oll5uR7tInhyXE+W5JXw8vfZVsdRJ+bImN3U\ntcCnAMaYHGNMrv32XqAIiHFyXo81ukccgX4+fLtxn9VR3N7UNQV8smIX/zeiKxf21Da7quU5swqM\nB3Y3uV9gf+y4rzHG1APlwLGXgK8E1hpjfva+s4jcJiKrRWR1cbHr9B39av0eahsauaZ/B6ujKOUS\nJg1I5NoBibz14zZmZWrx4KIcGbMBEJGOQGdg/nGeGwAEANtOcK5LjtuuJCzQj5HJsXybuU/fBToL\nWXsreHR6JoO7RHH/GO2Xrazh0pdbRaQntukkvzne88aYd4wxGcaYjJgY17h4Yozhs1W7SU9oRY92\nEVbHUcplTB6XSt/ESO7/YgM5hYesjqPOziRgqjGmoemDItIO21S/W4wxx+1P54rjtiu6tHc7ig/V\nsHK7dh85E+VVddz+8RoiQ/x5/dq++u6ysowz/+XtAZpe0k2wP3bc14iIH9AKKLXfTwCmY5v3d9wr\nI65oY0E5W/cf4uoMvZqtVFOBfr78/YZzCAnw47YPV1NeVWd1JPW/HBmzj5qEfdrIUSISAXwLPGqM\n+ckpCb3IqJRYgv19+Wbj3lO/WP2PhkbDvZ+tZ09ZFW9d34+YcLeZeao8kDML7VVAkoh0FpEAbAPz\nzGNeMxPbYkeAicB8Y4yxL6L5FnjIGLPUiRmb3WerdxPk78M43aRGqZ+Jiwjibzf0o6Csit9NWadd\nFVyLI2M2IpICtAaWrXQHqAAAGQ5JREFUN3ksANuFkQ+NMVNbKK9HCwnwY1SPWL7btF83rzlNL8zZ\nyvytRTxxWSrndGxjdRzl5ZxWaNvnXN8JzAG2AJ8bYzaLyFMiMs7+sneBKBHJA+4DjraTuhPoBjze\npJVUrLOyNpeq2ga+Xr+XsWntiAjytzqOUi6pf6c2PHFZKguyi3l1Xo7VcZSdg2M22ArwKcaYpr8l\nXQ0MB25uMmb3abHwHuqy9HaUVtbyU75OH3HU9HUFvL0wn+sHJvKLwZ2sjqMUfs785MaYWcCsYx57\nvMntauCq45z3NPC0M7M5w6zMfRyqqecanTai1EndMKgjGwvKeWN+Hklx4Yzrre8AuYJTjdn2+5OP\nc95HwEdODff/7d15eJXlmcfx703IQkICBEgghJ0ARWRfIkWlbkWrIoUWrFbrAi51qa0zap22tra9\nnE47rW1HWxREBMEFF7RaN3SQNRCQXWQJYNgCBCIECFme+eO82JhJwpZz3jc5v8915co571meX54c\nbu68axQa0SONpLgY5qzcwfCsVn7HCbwV2w/wwOzVZHdJ5ZGrz/E7jggQ8IMh65vnc7bTuVUSQzpr\nU5VIbcyMX4/uzeBOLbj/pZXkbtP5gkWqSoiN4fJz2/LW6t0cPV5+8hdEsd1Fx7jtuVzSU+J54rqB\nxOrgRwkIfRLryOr8InK3HeD67I6Ymd9xRAIvvnEMf//+INo2S2DitGV8XnjE70gigTNmQCaHS8p4\nZ+1uv6ME1pHjZUyYtozikjKevmEwqUlxfkcS+ZIa7ToydeFWEuNi+M6gTL+jiNQbqUlxTL5xMKXl\nFdw8dSlfHNOZSEQqG9o5lXbNmzB7eb7fUQKprLyCu59fwdqdRTw+vj892iT7HUnkK9Ro14F9h0t4\nY+VOxgzI1EGQIqepW1pTnrx+IHn7ivnhjOU6w4JIJY0aGWMGZjJ/0z52FR31O06gOOd45I21fPBp\nAb+8+hwu6ZXudySR/0eNdh2YlbOd4+UV3Diso99RROqlr3drxa+v6c3HG/fx0Cur+eoJLUSi25gB\n7XAOXl1R02nNo9Pf521h+uLt3HZhF51hRAJLjfZZKikrZ9qibZyf1YpuadpkJXKmxg/pwD0XZ/FS\nbj6/e2eD33FEAqNjyyQGd2rB7Nx8/RHqmbNyJ4+9/SlX9c3ggW/29DuOSI3UaJ+l2bk7KDhUwu0X\ndvU7iki9d98lWXxvaAee/Ggzk+fn+R1HJDDGDMhk895iVuYX+R3Fdws27eP+F1cypHMqv/9OHxo1\n0gkIJLjUaJ+FsvIK/j5vM30zmzGsa0u/44jUe2bGo6N6M/KcNjz65jpe06ZyEQC+1actTWJjmJWz\n3e8ovsrddoBbn11Gl9ZJPPX9QcQ3jvE7kkit1GifhX+s3sW2/Ue4Y0Q3ndJPpI7ENDL+NL4fQzun\ncv9LK/lwQ4HfkUR8l5wQy9V9M3j9k51Re3aetTuLuOmZHNJT4pl2yxCaJerkAxJ8arTPUEWF48mP\nNtMtrSmX6UhnkTqVEBvDUzcOokebZG57Lpf5G/f5HUnEd9dnd+RoaTmv5Ebfqf427z3MDZNzaBrf\nmOm3DiUtOcHvSCKnRI32GXpj1U4+3X2Iu77RTfuHiYRBSkIs028ZSpdWSdzy7FIWblazLdHt3Mxm\n9M1sxvQl26PqoMjPC49w/dNLAHju1qFktkj0OZHIqVOjfQaOl1Xwh3c/o2ebZK7um+F3HJEGq0VS\nHNNvHUqH1ERumbqMJVv2+x1JxFfXZ3dkU8FhluQV+h0lIrbvP8L4SYspLilj2i1D6Nq6qd+RRE6L\nGu0z8MLS7WwvPMIDI3tqbbZImLVqGs/zE7LJaJ7ATVOXsmxrdDQYItW5qm8GzZrEMnXBVr+jhN3W\nfcWMm7SIwyVlPD8hm3MymvkdSeS0qdE+TYdLynj8g00M6ZzKiB6t/Y4jEhVaJ8czc0I2bVISuGFK\nDgs2aTcSiU4JsTFcN7QD76zbzbb9xX7HCZstew8zbtIijpWWM3NCNr3bqcmW+kmN9mn673c/Y39x\nCT+94ms604hIBKWlJDBrYjbtWyRy0zNLeXftbr8jifjiB8M6EduoUYM91/y6nV8wbtJiysodMydm\n0ysjxe9IImdMjfZpWLOjiKkL87huaAf6tW/udxyRqJOWksALt2XztYwU7pixnFdXRN/ZF0TSUhK4\npn8GLy77nMLi437HqVOLt+xn3N8X0biR8cJt2fRsoyZb6jc12qeovMLx8GtrSE2K4990uVcR3zRP\njGPGrUMZ2jmV+15YyVPztkTVGRhEAG49vwvHSit4btE2v6PUmX+u2c0NU3JIS4ln9h3D6JaW7Hck\nkbOmRvsUPfXxFlZ+fpCfXdmLZk10knwRPzWNb8yUHwzminPb8Ju31vOz19dQVl7hdyyRiOmenszF\nPdOYsiCvQVzA5rnF27hzRi692qbw8u3DyGjexO9IInVCjfYpWJ1fxB/e3cDlvdvodH4iAZEQG8Nf\nrx3AbRd2Yfri7dw6bRmHS8r8jiUSMT+6pDtFR0uZUo/31S4tr+Dnr6/hZ6+tYUSPNJ6fMJQWSXF+\nxxKpM2q0T6Kw+Dh3zMilZVI8vx19rg6AFAmQRo2Mhy7/Gr8dfS4fb9zH2CcXNugzMYhUdm5mM755\nTjqTP87j4JH6t6920ZFSfvBMDtMWbWPC+Z156oZBJMY19juWSJ1So12LkrJy7pyRS8GhEp68foD+\nyhYJqO8N7cAzPxjMrqJjXPWX+cz9dI/fkUQi4r5Lu3P4eBmT5m3xO8ppWb/rC655YgE5eYX8bmwf\nHv5WL2J0XQppgNRo16C0vIK7n1/B4i2F/OeYc+nfoYXfkUSkFhd0b80bdw0ns0UiN09dxh/f+4yK\nCh0kKQ1bzzYpXNkngykL8thx8KjfcU7KOcesnO1c8z8LvrwQzXcHtfc7lkjYqNGuRnFJGbc/l8u7\n6/bwy6vPYXT/TL8jicgp6NAykVfuHMaYAZk8/sFGrnt6CTvrQfMhcjYeGNkDgN/8Y53PSWpXXFLG\nj19cyYOvrGZQpxa8dc/5DO6U6ncskbBSo13F2p1FfPuJhXy4oYBHr+nNjcM6+R1JRE5DQmwMv/9O\nH343pg8r8w8y8k/zmLNyp9+xRMIms0Uid47oxlurdwf2qqk5eYVc8eePee2THdx3SXem3TyU1snx\nfscSCTs12oQ2ZX26+wsenL2KUX9dQOGR4zxz0xC+n93R72gicgbMjO8Obs/b955Pt7Sm3DNzBffM\nXMH+wyV+RxMJi4kXdKF9ahN+MWctx0rL/Y7zpaPHy/nVG+sYN2kRFc4xc0I2916Spf2xJWpE9eG9\nk+fn8Y9VO8k/cJSCQyXENW7EtUM68JPLutM8UQc+itR3HVsm8eJt5/HER5v5y9yN/O9ne3no8p58\nd1B7Guk/emlAEmJj+NWo3tz0zFL+650N/OzKXn5HYt5ne/nFnLXk7SvmhvM68sDIniTFR3XbIVEo\nqj/x5RUVJMTGMLxbKwZ3TuWSr6VrU5ZIA9M4phH3XJzFFee24aevruHBV1bzUm4+v7z6HHq3a+Z3\nPJE6840eaXw/uyOT5+cxokdrzs9q7UuObfuLefTN9by/fg8dWyby/IShDOvaypcsIn6zhnLp4kGD\nBrlly5b5HUNEAsw5x+zlO/jtW+spLD7OVX0z+Mml3enUKsnvaJhZrnNukN85Ikl1u+4dPV7OVX+d\nzxdHS3nznuGkJSdEbOwDxcf527zNPDN/K41jjLsvyuLm4Z2IbxwTsQwikXKqNTuq12iLSHQxM8YO\nzOTSXulMmreZKfO38vbqXXx3cHtuv6ArHVom+h1R5Kw0iYvhz+P7M+bJhUyclsusidkkxIa30T14\n5DiT5+cxZX4eR0rLGd2vHQ9c3pP0lMg1+SJBpTXaIhK1Cr44xl/mbmLW0u2UVzgu792Wm4d3ZkCH\n5hG/CqzWaEtd+uea3dwxI5eLeqTxxPUDwrJWeVPBIaYs2Mory/M5VlrBt/q05UcXZ5GVnlznY4kE\nzanWbDXaIhL19nxxjKkLtzJ98TYOHSuje3pTvjOwPaP6Z0Rs07sabalr0xdv4z9eW8OIHq35y7X9\nSU6IPev3LDpayturd/HKih3k5BUS37gRo/u34+bhnemuBluiSCAabTMbCTwOxABPO+ceq/J4PDAN\nGAjsB8Y557Z6jz0E3AKUA/c4596pbSwVbBE5W4dLynhj5U5eXPY5K7YfxAwGdGjBZb3SubBHa7qn\nJYftbCVBaLRPoWb/EfiGdzcRSHPONfceuxH4D++xXzvnnj3ZeKrb4TcrZzsPv7aGTi0TeXx8/9M+\nANg5R/6Bo3y4oYC5nxawcPN+jpdV0LV1EmMGZjJ+cAdSk3SWLok+vjfaZhYDfAZcCuQDS4FrnXPr\nKj3nTqCPc+52MxsPjHbOjTOzXsBMYAiQAbwPdHfO1XhyUBVsEalLG/cc4q3Vu3l33W7W7vwCgJSE\nxgzqlErvjBSy0pPpltaUjGZNSGnS+Kx3NfG70T6Vml3l+XcD/Z1zN5tZKrAMGAQ4IBcY6Jw7UNuY\nqtuRsWjzfu6euYL9xSV8u38m12d3oE9m86+cy9o5x5Hj5eQfOErevmI27z3M6vwilm8/QMGh0Pnn\nO7VM5KKe6Yzql0GfzGYR371KJEiCcDDkEGCTc26LF2gWMAqoXLRHAY94t18G/mqhf7mjgFnOuRIg\nz8w2ee+3KIx5RUS+lJWezL3pydx7SRY7Dh5l0eb9LNtayNKthXy0oYCKSuso4mIakZoUR2JcDFf2\nzeDHl3b3L/iZO5WaXdm1wC+8298E3nPOFXqvfQ8YSWiFifjsvK4t+eAnF/L4+xuZmbOd2cvzaRIb\nQ6vkOAzjWGk5B4+Ucry84iuv69gykWFdWzKgYwuGd2tFl9ZNffoJROqvcDba7YDPK93PB4bW9Bzn\nXJmZFQEtveWLq7y2XdUBzGwiMBGgQ4cOdRZcRKSyds2bMHZgJmMHZgJwrLScLXtDa/0KDpWw91AJ\nhcUlHC2tID2l3p6L/1RqNgBm1hHoDMyt5bX/r2Z7r1Xd9kGzJrH8/Kpe3HdpFu+v38Oq/CKKjpTi\nCP2h2DwplhaJcbRtlkCXVk3p1CqxTvbpFol29fr0fs65ScAkCG2C9DmOiESJhNgYemWk0Csjxe8o\nfhkPvFzb7nw1Ud32V3JCLKP7ZzK6f6bfUUSiQqMwvvcOoH2l+5nesmqfY2aNgWaEDoo8ldeKiEjd\nOZ26O56v7haimi0iUo1wNtpLgSwz62xmcYQK85wqz5kD3OjdHgvMdaGjM+cA480s3sw6A1lAThiz\niohEu1Op2ZhZT6AFXz1m5h3gMjNrYWYtgMu8ZSIiUS1su454+1zfRajYxgBTnHNrzexXwDLn3Bxg\nMvCcd7BjIaHCjve8FwkdhFMG/PBMNlGKiMipOcWaDaE6PctVOmWVc67QzB4l1KwD/OrEgZEiItFM\nF6wREQkAv0/v5wfVbRGpr061Zodz1xERERERkailRltEREREJAzUaIuIiIiIhIEabRERERGRMGgw\nB0Oa2V5g2xm8tBWwr47jnI0g5QlSFghWniBlAeWpTZCyQM15OjrnWkc6jJ/OsG7Xl9+nX4KUJ0hZ\nIFh5gpQFgpUnSFngLGt2g2m0z5SZLQvSkf5ByhOkLBCsPEHKAspTmyBlgeDlqW+CNn/KU7MgZYFg\n5QlSFghWniBlgbPPo11HRERERETCQI22iIiIiEgYqNGGSX4HqCJIeYKUBYKVJ0hZQHlqE6QsELw8\n9U3Q5k95ahakLBCsPEHKAsHKE6QscJZ5on4fbRERERGRcNAabRERERGRMFCjLSIiIiISBlHdaJvZ\nSDPbYGabzOxBH8bfamarzewTM1vmLUs1s/fMbKP3vUUYx59iZgVmtqbSsmrHt5A/e3O1yswGRCDL\nI2a2w5ufT8zsikqPPeRl2WBm36zLLN77tzezD81snZmtNbN7veURn59asvgyP2aWYGY5ZrbSy/NL\nb3lnM1vijfuCmcV5y+O9+5u8xztFIMtUM8urNDf9vOVh/RxXyhVjZivM7E3vfsTnpiFSzQ5Oza4l\nj191STW75jyBqdknyeNb3Q5rzXbOReUXEANsBroAccBKoFeEM2wFWlVZ9jvgQe/2g8B/hnH8C4AB\nwJqTjQ9cAbwNGJANLIlAlkeA+6t5bi/v9xUPdPZ+jzF1nKctMMC7nQx85o0b8fmpJYsv8+P9jE29\n27HAEu9nfhEY7y3/G3CHd/tO4G/e7fHACxHIMhUYW83zw/o5rjTOj4HngTe9+xGfm4b2hWp2TXXS\nl5pdSx6/6pJqds15AlOzT5JnKj7VbcJYs6N5jfYQYJNzbotz7jgwCxjlcyYIZXjWu/0scE24BnLO\nzQMKT3H8UcA0F7IYaG5mbcOcpSajgFnOuRLnXB6widDvs84453Y555Z7tw8B64F2+DA/tWSpSVjn\nx/sZD3t3Y70vB1wEvOwtrzo3J+bsZeBiM7MwZ6lJWD/HAGaWCXwLeNq7b/gwNw2QanaAanYteWoS\n7rqkml1znsDU7JPkqUlYP8vhrtnR3Gi3Az6vdD+f2v8hhIMD3jWzXDOb6C1Ld87t8m7vBtIjnKmm\n8f2ar7u8TUVT7F+bZCOaxds01J/QX92+zk+VLODT/Hib2T4BCoD3CK2BOeicK6tmzC/zeI8XAS3D\nlcU5d2JufuPNzR/NLL5qlmpy1pU/Af8OVHj3W+LT3DQwqtnVC1rNBp/rtmp2tTkCU7Ory+Nz3Q5r\nzY7mRjsIhjvnBgCXAz80swsqP+hC2yZ8O/+i3+MDTwJdgX7ALuAPkQ5gZk2B2cCPnHNfVH4s0vNT\nTRbf5sc5V+6c6wdkElrz0jNSY58si5n1Bh7yMg0GUoEHIpHFzK4ECpxzuZEYTyJONfvkfK3bqtnV\nC1LNri6PX3U7EjU7mhvtHUD7SvczvWUR45zb4X0vAF4l9OHfc2KTiPe9IJKZahk/4vPlnNvj/WOs\nAJ7iX5vSIpLFzGIJFckZzrlXvMW+zE91WfyeHy/DQeBD4DxCm/MaVzPml3m8x5sB+8OYZaS36dY5\n50qAZ4jc3HwduNrMthLateEi4HF8npsGQjW7eoGp2eBvXVLNPrkg1ewqefyq22Gv2dHcaC8Fsrwj\nS+MI7dQ+J1KDm1mSmSWfuA1cBqzxMtzoPe1G4PVIZfLUNP4c4Abv6N9soKjS5riwqLIP1mhC83Mi\ny3jv6N/OQBaQU8djGzAZWO+c++9KD0V8fmrK4tf8mFlrM2vu3W4CXEpoH8QPgbHe06rOzYk5GwvM\n9dYshSvLp5X+YzVC+9ZVnpuwfY6dcw855zKdc50I1ZS5zrnr8GFuGiDV7OoFpmaDr3VJNbvmPIGp\n2bXk8aVuR6Rmuzo+crM+fRE6kvUzQvsqPRzhsbsQOsp4JbD2xPiE9vX5ANgIvA+khjHDTEKbr0oJ\n7YN0S03jEzra93+8uVoNDIpAlue8sVZ5H+62lZ7/sJdlA3B5GOZmOKFNjKuAT7yvK/yYn1qy+DI/\nQB9ghTfuGuDnlT7TOYQO5HkJiPeWJ3j3N3mPd4lAlrne3KwBpvOvI9zD+jmukm0E/zqCPeJz0xC/\nUM0OTM2uJY9fdUk1u+Y8ganZJ8nja90mTDVbl2AXEREREQmDaN51REREREQkbNRoi4iIiIiEgRpt\nEREREZEwUKMtIiIiIhIGarRFRERERMJAjbY0KGZ22Pveycy+V8fv/dMq9xfW5fuLiEQb1Wxp6NRo\nS0PVCTitol3pKlA1+UrRds4NO81MIiJSvU6oZksDpEZbGqrHgPPN7BMzu8/MYszsv8xsqZmtMrPb\nAMxshJl9bGZzgHXestfMLNfM1prZRG/ZY0AT7/1meMtOrIkx773XmNlqMxtX6b0/MrOXzexTM5vh\nXfEKM3vMzNZ5WX4f8dkREQkW1WxpkE7216BIffUgcL9z7koAr/gWOecGm1k8sMDM3vWeOwDo7ZzL\n8+7f7Jwr9C4Nu9TMZjvnHjSzu5xz/aoZ69tAP6Av0Mp7zTzvsf7AOcBOYAHwdTNbT+jyuz2dc+7E\npWhFRKKYarY0SFqjLdHiMuAGM/sEWELosrxZ3mM5lQo2wD1mthJYDLSv9LyaDAdmOufKnXN7gP8F\nBld673znXAWhy/B2AoqAY8BkM/s2cOSsfzoRkYZFNVsaBDXaEi0MuNs518/76uycO7F2pPjLJ5mN\nAC4BznPO9QVWAAlnMW5JpdvlQGPnXBkwBHgZuBL451m8v4hIQ6SaLQ2CGm1pqA4ByZXuvwPcYWax\nAGbW3cySqnldM+CAc+6ImfUEsis9Vnri9VV8DIzz9ilsDVwA5NQUzMyaAs2cc28B9xHafCkiEs1U\ns6VB0j7a0lCtAsq9zYlTgccJbQJc7h3cshe4pprX/RO43dsnbwOhTZEnTAJWmdly59x1lZa/CpwH\nrAQc8O/Oud1e0a9OMvC6mSUQWmvz4zP7EUVEGgzVbGmQzDnndwYRERERkQZHu46IiIiIiISBGm0R\nERERkTBQoy0iIiIiEgZqtEVEREREwkCNtoiIiIhIGKjRFhEREREJAzXaIiIiIiJh8H/HJBFCVqle\nlgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LdoQyKfiXLTB",
        "colab": {}
      },
      "source": [
        "learn.load('lm_stage1');\n",
        "learn.unfreeze()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "3ad63d12-e148-431b-a273-2187cf1ba30e",
        "id": "DTHrAozkXLTK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "# train with smaller LR in last layer\n",
        "lr =lr_init/10\n",
        "\n",
        "learn.fit_one_cycle(10,\n",
        "                   slice(lr/(2.6**4),lr),\n",
        "                   moms=(0.8,0.7),\n",
        "                   callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "                   name='lm_stage2')])\n",
        "\n",
        "\n",
        "learn.recorder.plot_losses()\n",
        "\n"
      ],
      "execution_count": 32,
      "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>2.382976</td>\n",
              "      <td>2.176516</td>\n",
              "      <td>0.802388</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.318691</td>\n",
              "      <td>2.110914</td>\n",
              "      <td>0.811629</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.256619</td>\n",
              "      <td>2.086051</td>\n",
              "      <td>0.817679</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.160916</td>\n",
              "      <td>2.017246</td>\n",
              "      <td>0.830692</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.075545</td>\n",
              "      <td>1.981940</td>\n",
              "      <td>0.836630</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.024832</td>\n",
              "      <td>1.955146</td>\n",
              "      <td>0.841473</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.974775</td>\n",
              "      <td>1.946366</td>\n",
              "      <td>0.844955</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.937437</td>\n",
              "      <td>1.941292</td>\n",
              "      <td>0.846696</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.903300</td>\n",
              "      <td>1.935013</td>\n",
              "      <td>0.848304</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.886495</td>\n",
              "      <td>1.931892</td>\n",
              "      <td>0.848973</td>\n",
              "      <td>00:40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.8023883700370789.\n",
            "Better model found at epoch 1 with accuracy value: 0.8116294741630554.\n",
            "Better model found at epoch 2 with accuracy value: 0.8176785707473755.\n",
            "Better model found at epoch 3 with accuracy value: 0.8306919932365417.\n",
            "Better model found at epoch 4 with accuracy value: 0.8366295099258423.\n",
            "Better model found at epoch 5 with accuracy value: 0.8414732217788696.\n",
            "Better model found at epoch 6 with accuracy value: 0.844955325126648.\n",
            "Better model found at epoch 7 with accuracy value: 0.8466964960098267.\n",
            "Better model found at epoch 8 with accuracy value: 0.8483036160469055.\n",
            "Better model found at epoch 9 with accuracy value: 0.848973274230957.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dJkBr4BLgoDHmhRNt1+uvQzgV2bth+6+wbS5s+wUObLGWB0ZC634Q39+6NU2y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\ngAaCUkopmwaCUkopAP4fz0UdpGlFcd8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mrr4LM-1XLTW"
      },
      "source": [
        "Testing the performance of our language model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dHuvdF0CXLTZ",
        "colab": {}
      },
      "source": [
        "learn.load('lm_stage2');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "frUNyztpXLTh",
        "outputId": "88c90a9d-e89f-4623-ab7d-52a7a7e311ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "learn.predict('$GLD is ', 20, temperature=0.25, min_p=0.001)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"$GLD is  now starting to take moves since 2018 . i 'll show that it might exceed to $ usdu $ yinn\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HpdqMDRaXLTp"
      },
      "source": [
        "Save encoder for classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4Unx5UmUXLTs",
        "colab": {}
      },
      "source": [
        "learn.save_encoder('lm_encoder')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VHlXyIGTXLTx"
      },
      "source": [
        "## Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Jedldv9XLT0",
        "colab": {}
      },
      "source": [
        "data_clas = load_data(PATH, 'data_clas_fwd', bs = bs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JPpNb-2XXLT5"
      },
      "source": [
        "Look at the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "a2e62b70-fe8e-40a0-bd3b-b133acc08903",
        "id": "4g97BaYxXLT7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "data_clas.show_batch()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj we d ( 5 / 29 / 19 ) trading range &lt; 15 % ( xxmaj min $ 5 , xxmaj vol &gt; 750 k , xxmaj opt xxmaj vol in any strike &gt; 500 ) : $ xxup xxunk $ xxup bb $ xxup xxunk $ xxup xxunk $ xxup xxunk $ xxup stay $ xxup xxunk $ xxup xxunk $ xxup xxunk $ xxup xxunk $</td>\n",
              "      <td>unrelated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos * * * xxmaj silver and xxmaj gold are on xxunk today xxrep 4 * \\n \\n  $ xxup xlf $ xxup bac $ xxup eem $ xxup gdx $ xxup uso $ xxup efa $ xxup spy $ xxup gld $ xxup slv $ xxup ftse $ xxup dax $ xxup spy $ xxup gld $ xxup slv $ xxup ftse $ xxup btc.x $ xxup xxunk</td>\n",
              "      <td>bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos xxmaj xxunk of the year xxunk goes to xxunk . \\n  $ xxup spy $ xxup iwm $ xxup qqq $ xxup xli $ xxup xxunk $ xxup mmm $ xxup cat $ xxup ba $ xxup tlt $ xxup gld $ xxup cl_f $ xxup xle $ xxup xom $ xxup cvx $ xxup xlf $ xxup jpm $ xxup gs $ xxup xrt $ xxup xxunk</td>\n",
              "      <td>unrelated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos ' # xxmaj oil xxmaj xxunk xxmaj xxunk as xxmaj market xxmaj falls . \\n \\n  xxmaj but # xxmaj safehaven xxmaj gold &lt; xxmaj is up today nicely . \\n \\n  $ xxup xlf $ xxup bac $ xxup eem $ xxup gdx $ xxup uso $ xxup efa $ xxup spy $ xxup gld $ xxup slv $ xxup ftse $ xxup dax $ xxup</td>\n",
              "      <td>bullish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xxbos # estate asset watch - most active stocks xxmaj friday - $ xxup amd $ xxup bac $ t $ f xxup xxunk $ xxup gps $ xxup jci $ xxup eca $ xxup vale $ xxup snap $ xxup teva $ xxup baba $ s $ xxup vz $ xxup siri $ xxup rig $ xxup uber $ xxup ntnx $ xxup aapl $ xxup xxunk $ xxup</td>\n",
              "      <td>unrelated</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MQCrvakdXLUA"
      },
      "source": [
        "Making sure the vocab is the same between the language model and classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_wI_JTewXLUD",
        "colab": {}
      },
      "source": [
        "data_clas.vocab.itos = data_lm.vocab.itos"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QOcay0G-XLUH"
      },
      "source": [
        "Trying different opt_funcs\n",
        "\n",
        "- default\n",
        "- partial(optim.Adam, eps = 0.1)\n",
        "- partial(optim.SGD)\n",
        "- Radam\n",
        "- ranger\n",
        "\n",
        "Observations:\n",
        "adam with eps 0.1/0.01 and SGD with nesterov momentum seems to take much longer to train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "W5ryJPr7XLUJ",
        "colab": {}
      },
      "source": [
        "# learn = text_classifier_learner(data_clas, \n",
        "#                                 arch = AWD_LSTM,\n",
        "#                                 loss_func = FlattenedLoss(LabelSmoothingCrossEntropy, axis=-1),\n",
        "#                                 drop_mult=0.3,\n",
        "#                                 metrics = [accuracy],\n",
        "#                                 opt_func = optar,\n",
        "#                                 bn_wd = False,\n",
        "#                                 true_wd = True,).to_fp16\n",
        "\n",
        "\"\"\"\n",
        "learner with default adamw\n",
        "\n",
        "\"\"\"\n",
        "learn = text_classifier_learner(data_clas, \n",
        "                                arch = AWD_LSTM,\n",
        "                                loss_func = FlattenedLoss(LabelSmoothingCrossEntropy, axis=-1),\n",
        "                                drop_mult=0.1, # try lower drop mult (0.1-0.3)\n",
        "                                metrics = [accuracy]).to_fp16()\n",
        "\n",
        "learn.load_encoder('lm_encoder')\n",
        "learn.freeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "b3b4c517-8e4a-44f9-8065-91063745132f",
        "id": "gp3vm68IXLUQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot(suggestion = True)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
            "Min numerical gradient: 1.74E-03\n",
            "Min loss divided by 10: 2.51E-02\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h0kpyj5TT34Md1KAFQinVBAlRofzigjTmb85jYXae3XH83rrjF8hpC0Ip5Qum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\nSinllhYIpZRSbmmBUEop5db/BwIqKubzQT0xAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "cd6ce099-2372-4197-8746-fc71b0282dad",
        "id": "lsMAqTbSXLUW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "lr_init = 2e-2\n",
        "lr = lr_init\n",
        "# learn.fit_one_cycle(10, \n",
        "#                    lr, \n",
        "#                    moms=(0.8,0.7),\n",
        "#                    callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "#                                                              name='classifier_stage1')])\n",
        "learn.fit_fc(10,\n",
        "             lr,\n",
        "             moms=(0.8,0.7),\n",
        "             callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "             name='classifier_stage1')])\n",
        "\n",
        "\n",
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 41,
      "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>1.156598</td>\n",
              "      <td>0.859849</td>\n",
              "      <td>0.732319</td>\n",
              "      <td>00:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.964504</td>\n",
              "      <td>0.796207</td>\n",
              "      <td>0.774396</td>\n",
              "      <td>00:27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.860215</td>\n",
              "      <td>0.792575</td>\n",
              "      <td>0.802149</td>\n",
              "      <td>00:27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.795379</td>\n",
              "      <td>0.788059</td>\n",
              "      <td>0.795882</td>\n",
              "      <td>00:27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.754798</td>\n",
              "      <td>0.827976</td>\n",
              "      <td>0.796777</td>\n",
              "      <td>00:27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.752020</td>\n",
              "      <td>0.793733</td>\n",
              "      <td>0.820949</td>\n",
              "      <td>00:23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.691708</td>\n",
              "      <td>0.790778</td>\n",
              "      <td>0.813787</td>\n",
              "      <td>00:27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.680143</td>\n",
              "      <td>0.804619</td>\n",
              "      <td>0.821844</td>\n",
              "      <td>00:25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.628267</td>\n",
              "      <td>0.737166</td>\n",
              "      <td>0.843330</td>\n",
              "      <td>00:25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.575219</td>\n",
              "      <td>0.750447</td>\n",
              "      <td>0.837959</td>\n",
              "      <td>00:25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.7323186993598938.\n",
            "Better model found at epoch 1 with accuracy value: 0.7743957042694092.\n",
            "Better model found at epoch 2 with accuracy value: 0.8021486401557922.\n",
            "Better model found at epoch 5 with accuracy value: 0.8209489583969116.\n",
            "Better model found at epoch 7 with accuracy value: 0.821844220161438.\n",
            "Better model found at epoch 8 with accuracy value: 0.8433303236961365.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yEkLfSRDWyt0R1psxhvJKg7+vD1nHSli15wi7sgtZ8WMuB/KK2XekqPrYlPZR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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6isGG4LcXLUq"
      },
      "source": [
        "Gradual unfreezing\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uyhv3tjMXLUv",
        "colab": {}
      },
      "source": [
        "learn.load('classifier_stage1');\n",
        "learn.freeze_to(-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "k4w02QqaXLU0",
        "outputId": "23829a27-a86f-4536-c0f1-f840eec0cd78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "source": [
        "lr = lr_init\n",
        "\n",
        "learn.fit_one_cycle(5,\n",
        "                   slice(lr/(2.6**4),lr), \n",
        "                   moms=(0.8,0.7),\n",
        "                   callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "                                                             name='classifier_stage2')])\n",
        "\n",
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 43,
      "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>0.558920</td>\n",
              "      <td>0.790173</td>\n",
              "      <td>0.835273</td>\n",
              "      <td>00:27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.641712</td>\n",
              "      <td>0.774762</td>\n",
              "      <td>0.829902</td>\n",
              "      <td>00:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.635175</td>\n",
              "      <td>0.745927</td>\n",
              "      <td>0.836168</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.577434</td>\n",
              "      <td>0.742832</td>\n",
              "      <td>0.846016</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.549106</td>\n",
              "      <td>0.725227</td>\n",
              "      <td>0.845121</td>\n",
              "      <td>00:26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.835273027420044.\n",
            "Better model found at epoch 2 with accuracy value: 0.8361682891845703.\n",
            "Better model found at epoch 3 with accuracy value: 0.8460161089897156.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gs99DxgiISICwKHBGgNP+NywSnPatdp/nsdc+Z4S13+E8YSiq9fFnglgM9BSRrliJYQpw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\nHCDMzKzKAcLMzKocIMzMrOoCzYybXF8sgRwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZwfwqsMwdyV1",
        "colab": {}
      },
      "source": [
        "learn.load('classifier_stage2');\n",
        "learn.freeze_to(-2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "b2bc28f5-887a-4d6a-b78c-b7e74bb8c698",
        "id": "c1ZLoppgdyWT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "# train with smaller LR in last layer (/2)\n",
        "lr = lr_init/2\n",
        "\n",
        "learn.fit_one_cycle(5,\n",
        "                   slice(lr/(2.6**4),lr), \n",
        "                   moms=(0.8,0.7),\n",
        "                   callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "                                                             name='classifier_stage3')])\n",
        "\n",
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 45,
      "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>0.602572</td>\n",
              "      <td>0.847757</td>\n",
              "      <td>0.829902</td>\n",
              "      <td>00:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.716961</td>\n",
              "      <td>0.785842</td>\n",
              "      <td>0.830797</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.598576</td>\n",
              "      <td>0.726023</td>\n",
              "      <td>0.843330</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.533608</td>\n",
              "      <td>0.692885</td>\n",
              "      <td>0.856759</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.506438</td>\n",
              "      <td>0.707194</td>\n",
              "      <td>0.844226</td>\n",
              "      <td>00:34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.8299015164375305.\n",
            "Better model found at epoch 1 with accuracy value: 0.8307967782020569.\n",
            "Better model found at epoch 2 with accuracy value: 0.8433303236961365.\n",
            "Better model found at epoch 3 with accuracy value: 0.8567591905593872.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eGNyOXq0bcP+M1Vxag7+NiwgT+p9YnXTSyFj+OXcTby3cRn5hMX8b3kGTRE1k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\ng0iSJElKSQeRJEmSlJIOIkmSJCnlfyyKSdnQN1WdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "URQFStBJdvyd",
        "colab": {}
      },
      "source": [
        "learn.load('classifier_stage3');\n",
        "learn.unfreeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e59938a0-07a5-48ff-980a-54510cbfdeca",
        "id": "UuRiYLnsdvys",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        }
      },
      "source": [
        "# train with smaller LR in last layer /10\n",
        "lr = lr_init/10\n",
        "\n",
        "learn.fit_one_cycle(10,\n",
        "                   slice(lr/(2.6**4),lr), \n",
        "                   moms=(0.8,0.7),\n",
        "                   callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "                                                             name='classifier_final')])\n",
        "\n",
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 47,
      "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>0.481891</td>\n",
              "      <td>0.691355</td>\n",
              "      <td>0.851388</td>\n",
              "      <td>01:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.497633</td>\n",
              "      <td>0.697872</td>\n",
              "      <td>0.847807</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.486163</td>\n",
              "      <td>0.736457</td>\n",
              "      <td>0.854073</td>\n",
              "      <td>01:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.487316</td>\n",
              "      <td>0.698361</td>\n",
              "      <td>0.856759</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.479933</td>\n",
              "      <td>0.721061</td>\n",
              "      <td>0.849597</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.462450</td>\n",
              "      <td>0.673168</td>\n",
              "      <td>0.870188</td>\n",
              "      <td>01:15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.464407</td>\n",
              "      <td>0.694478</td>\n",
              "      <td>0.854969</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.455098</td>\n",
              "      <td>0.678900</td>\n",
              "      <td>0.863026</td>\n",
              "      <td>01:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.444644</td>\n",
              "      <td>0.672356</td>\n",
              "      <td>0.863921</td>\n",
              "      <td>01:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.454045</td>\n",
              "      <td>0.669108</td>\n",
              "      <td>0.862131</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Better model found at epoch 0 with accuracy value: 0.851387619972229.\n",
            "Better model found at epoch 2 with accuracy value: 0.8540734052658081.\n",
            "Better model found at epoch 3 with accuracy value: 0.8567591905593872.\n",
            "Better model found at epoch 5 with accuracy value: 0.8701879978179932.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HSt4Doz+BTbNg6ZuQfhjCB1uliybnVXS0hWqHW+0Qnw23Rq7f+DsEu+4ho5S7eUoj9Vhg\nhjHmtCbNF5FJwCSApk2buiOuqiUzBZLi4MgOa3Gfrb9Dj0lw0YvWYj1VjQj0u9e66f4wCd602xPC\nB9mJwQ0z0JaF+h1g/PfWIkxTR8GEX61SkVLlzJ13hQSgidPrxvY2V8YCdxQ7d0CxcxcVP8kY8yHw\nIVhtEGceahWSddwaD3Bkh/0zrvB1+uHC47x8Ydh/oNe/Ki7W8tL+MqtOf+XH0PXaytFLqMl5ViP5\nV1fCV2Pg+p+qVgmvFIwxSGXrKOHBzqS92Z2N1D5YjdSDsW74K4FrjDEbix3XDvgdaGGP6sNupF4F\ndLMPW43VSJ1U0vXOqUbq7HSrJOAqERw/WPTY4AZQKxxqt7R/hls/a7UA32oVE78qvS2/Wd1fm/Wx\nusNWlr/Zsf1W7yzfgDM6fefOnQQHB1O7dm1NEmXAGMORI0dITU2lRYsWRfadrJHabSUIY0yuiNwJ\nzAG8gU+NMRtFZDLW0O5Z9qFjgWnGKVMZY5JE5DmspAIw+WTJoUrKyYSjO50SwI7C6qHUfUWPDaxr\n3fhbDSmWCFpaI3RV5dX2Ymuq8+9vhm9vsAbrlecsuqdrzwr46zWr+tK/hjX6vMNoaNn/tOJu3Lgx\n8fHxJCYmujHYc0tAQACNGzc+rXPcVoIob5WyBJGbbY36LVISsBNBSjxWD19b9dpFSwAFiaBWSwio\nUVGfQJWX6Cnwy70QeQWM/tgaQe4pjLFm1f3rf1bX4mq14LybrFLEpp8hK8X69xsx0koWTfu47nas\nKkSFlCBUKXx/o/UfqEBAiJUAmvY+MRFUq5jpfpWHiJoIWceseaf8g6z1OCq66iU/D2JnWonhwHqo\n0dhq1+p2XWHJ9dLXrHE2G2ZAzDSrW3VwQ4gcBR1HQ8NuFf85VIm0BFGRts+HtMTCkoH2VFGnMv85\naw2K3nfC0Ocr5uaamwUx31jLqibFWSv99b0XOl5pdS8uSXaa1aay4QfYPhfysq3lajuMhg5jSj9l\nuipTJytBaIJQqjIxBn57BP75AAY+Dv0fLr9rZ6XCqs9g+TuQut8aVNnvfqud4XSrjDKSYfMvsOF7\niPsTTB6EtbdKFZFXWF+YVLnQBKFUVZKfD7PuhLVfwbCXoFfJy2CWibQj1iSG/3xoTWLY4gIrMbQc\nUDYlmOOJVlXVhh9gzzJrW8OuVqkichTU1DGy7qQJQqmqJi8XZky0RoaPeNuq9y9rKfHWzLarP4ec\ndKuk0O9+a0U/d0mJh40/WiWLfWusbU37WCWLiMshsM7Jz1enTROEUlVRbpY1PXvcImvxochRZfO+\nh7dZc1Wtmw4Yq22h771Q9+Qzl5a5IzusUsWGGZC4GcTb6i7bYQy0u0Q7bpQRTRBKVVXZafDlaIiP\ntkZetx5y5u+VsNrqkbTpZ/AJgG7XW9O+h3jANDYHN1qlig3fW13Dvf2g9VDocAW0Gabjfc6CJgil\nqrLMFPj8MkjcAtf+AM37nvqcAsbAzsXW4La4ReBfE3rcYrVreGJ1jjFWItvwPWz8wWos9w20BhR2\nGA2tBoOP/6nfRzloglCqqks7DFMutgan3TDLWsb0ZPLzYctsKzEkrIKgetDrdmuZ2coy8DI/32rU\n3vA9bJxprekRUNOajLFOG7v7eCtrLFG10IqO1mNpglDqXHBsH3x6kTVh48TfXLcZ5OXA+hnWmuOJ\nm61xCH3vgc7XnPG8SR4hL8fqLrthhjWaO3kvRWYiqFbLafCpPQNBwevKkhDdRBOEUueKpDj49GLr\n+Y2/W5MygjXB45qp1up5KXuhXgfod5/VM6gqTvWem2W1VRSZwsb+eazYpNIFc5mdMKnluTGXmSYI\npc4lhzbBlOHW9ODXTLcGpP39vjXde5NecP79VgPvuTrFRXZ6yRNhHj9Q9FjHbMjhRUsgoS0qd4nL\niSYIpc41CautFemyU63XrYZYiaFZn4qNy9NlpRYmi5Otp4JAzcZFq6pqh0NgmNXeUS3UmlutEkxK\nqJP1KXWuadQNrv3eGsvQfQI06FTREVUO/sHQoLP1KC4j+cSkkWSP1chMdvFmYo3VqFarMGlUL3he\ny+l1SNFtATU9pnSnCUKpqqppT+uhyka1EGjU3XoUl54ESTutUkbGUet1xlGrZ1XGUeuRlgiHt1rP\ns46VfB3xPkViCTlxW/VabllxUBOEUkqdreq1Tm825rwcq0RSkDwKEkl60onbUvdb7UoZSZB93PX7\nNewKkxaVxScpQhOEUkqVN29fCAqzHqcjN9uqzipeQvELckuYmiCUUqqy8PGDoLrWoxx4fhO7Ukqp\nCqEJQimllEuaIJRSSrmkCUIppZRLmiCUUkq5pAlCKaWUS5oglFJKueTWBCEiw0Rki4hsF5FHSzjm\nKhGJFZGNIvK10/Y8EVlrP2a5M06llFIncttAORHxBt4BhgDxwEoRmWWMiXU6pjXwGNDXGHNURJxH\nf2QYY7q4Kz6llFIn584SRA9guzEmzhiTDUwDRhY75hbgHWPMUQBjzCE3xqOUUuo0uDNBNAL2Or2O\nt7c5awO0EZGlIvK3iAxz2hcgItH29svdGKdSSikXKnouJh+gNTAAaAwsFpGOxphkoJkxJkFEWgIL\nRGS9MWaH88kiMgmYBNC0adPyjVwppao4d5YgEoAmTq8b29ucxQOzjDE5xpidwFashIExJsH+GQcs\nAroWv4Ax5kNjTJQxJios7DRnRVRKKXVS7kwQK4HWItJCRPyAsUDx3kgzsUoPiEgdrCqnOBEJFRF/\np+19gViUUkqVG7dVMRljckXkTmAO4A18aozZKCKTgWhjzCx731ARiQXygIeMMUdEpA/wgYjkYyWx\nl5x7PymllHI/McZUdAxlIioqykRHR1d0GEopVamIyCpjTJSrfTqSWimllEuaIJRSSrmkCUIppZRL\nmiCUUkq5pAlCKaWUS5oglFJKuaQJQimllEuaIJRSSrmkCUIppZRLmiCUUkq5pAlCKaWUS5oglFJK\nuaQJQimllEuaIJRSSrmkCUIppZRLmiCUUkq5pAlCKaWUS5oglFJKuaQJQimllEuaIJRSSrmkCUIp\npZRLmiCUUkq5pAlCKaWUS5oglFJKueTWBCEiw0Rki4hsF5FHSzjmKhGJFZGNIvK10/YbRGSb/bjB\nnXEqpZQ6kY+73lhEvIF3gCFAPLBSRGYZY2KdjmkNPAb0NcYcFZG69vZawNNAFGCAVfa5R90Vr1JK\nqaJKVYIQkXAR8befDxCRu0Uk5BSn9QC2G2PijDHZwDRgZLFjbgHeKbjxG2MO2dsvAuYaY5LsfXOB\nYaX7SEoppcpCaauYvgfyRKQV8CHQBPj65KfQCNjr9Dre3uasDdBGRJaKyN8iMuw0zlVKKeVGpa1i\nyjfG5IrIKOAtY8xbIrKmjK7fGhgANAYWi0jH0p4sIpOASQBNmzYtg3CUUkoVKG0JIkdExgE3AL/Y\n23xPcU4CVkmjQGN7m7N4YJYxJscYsxPYipUwSnMuxpgPjTFRxpiosLCwUn4UpZRSpVHaBDER6A28\nYIzZKSItgKmnOGcl0FpEWoiIHzAWmFXsmJlYpQdEpA5WlVMcMAcYKiKhIhIKDLW3KaWUKielqmKy\nex7dDWDfsIONMf85xTm5InIn1o3dG/jUGLNRRCYD0caYWRQmglggD3jIGHPEvs5zWEkGYLIxJun0\nP55SSqkzJcaYUx8ksggYgZVQVgGHgKXGmPvdGt1piIqKMtHR0RUdhlJKVSoissoYE+VqX2mrmGoa\nY44BVwBfGGN6AheWVYBKKaU8T2kThI+INACuorCRWimlVBVW2gQxGau9YIcxZqWItAS2uS8spZRS\nFa20jdTfAd85vY4DRrsrKKWUUhWvtFNtNBaRH0XkkP34XkQauzs4pZRSFae0VUxTsMYwNLQfP9vb\nlFJKVVGlTRBhxpgpxphc+/EZoEOXlVKqCittgjgiIteKiLf9uBY44s7AlFJKVazSJogbsbq4HgD2\nA2OACW6KSSmllAcoVYIwxuw2xowwxoQZY+oaYy5HezEppVSVdjZLjnrMNBtKKaXK3tkkCCmzKJRS\nSnmcs0kQp57lTymlVKV10pHUIpKK60QgQDW3RKSUUsojnDRBGGOCyysQpZRSnuVsqpiUUkpVYZog\nlFJKuaQJQimllEuaIJRSSrmkCUIppZRLmiCUUkq5pAlCKaWUS5oglFJKuaQJQimllEuaIJRSSrnk\n1gQhIsNEZIuIbBeRR13snyAiiSKy1n7c7LQvz2n7LHfGqZRS6kQnnYvpbIiIN/AOMASIB1aKyCxj\nTGyxQ6cbY+508RYZxpgu7oqvuMGvLqJBzWp8eXPP8rqkUkp5NLclCKAHsN0YEwcgItOAkUDxBOER\ndiSmsSMxraLDUEopj+HOKqZGwF6n1/H2tuJGi8g6EZkhIk2ctgeISLSI/C0il7sxTqWUUi5UdCP1\nz0BzY0wnYC7wudO+ZsaYKOAa4HURCS9+sohMspNIdGJiYvlErJRS5wh3JogEwLlE0Nje5mCMOWKM\nybJffgx0d9qXYP+MAxYBXYtfwBjzoTEmyhgTFRYWVrbRK6XUOc6dCWIl0FpEWoiIHzAWKNIbSUQa\nOL0cAWyyt4eKiL/9vA7Ql3Juu3h4Rgx/bDxQnpdUSimP4rYEYYzJBe4E5mDd+L81xmwUkckiMsI+\n7G4R2SgiMcDdwAR7e3sg2t6+EHjJRe8nt0nPzuXb6HgmTV1VXpdUSimP485eTBhjZgOzi217yun5\nY8BjLs5bBnR0Z2wnE6e9mZRSqsIbqT3SriNWggit7lvBkSilVMXRBOFCelYeAPmmggNRSqkKpAnC\nhTxjZYa0rNwKjkQppSrOOZ8gjmXmcMsX0UW25dpFh1wtQiilzmHnfILIzTPMjT3oeJ2Xb8jLy3e8\nnr/pIBnZeRURmlJKVahzPkF4ixR5nZOXX6TkcNPn0bR/6nd2H9GeTUqpc8s5nyC8iv0GsvPyyXNR\ntbRmT3I5RaSUUp7hnE8Q3l5FSxDZufku2x4SU7Mcz/cmpdP80V9Zsk3nf1JKVV2aIFwkiIIShPO+\nhOQMx/Pth44D8NwvHjlzuVJKlQlNEMXaIDJy8hwliB9v7+PYHne4sA3Cx9s6Z+vB4+UQoVJKVQxN\nEMVKEMnp2eTnG7y9hPo1Axzb4xILk0GOUy8nY7QrrFKqajrnE4QUK0EcTcsh104QdQL9Hdvjj2aw\nes9RALJzC5NC4vEslFKqKjrnEwSAc444mp5NXn4+Pl6CV7HSxRXvLmPV7qNFShA6sZ9SqqrSBFHM\n0fRsRwkCYEDbMMb1KFz3aN6mg5oglFLnBLdO911ZCFBQaZSUlkNevsHHThCfTewBwDf/WMtr+3gJ\n2bmFCWLLgWMApKTnUFNnf1VKVSFagijmWGZBG0TRX83kkZEA7EvOdJQg2jeowdIdR5iydCedJ//B\n3qT0co9XKaXcRRMERRuqj2fmkpdXWIIocH3v5nRvFsq+5Ayy86zyxpD2ddl+6DjP/myNh1i243D5\nBa2UUm6mCaKY1MycIm0QzhqFVCMhOcNRgrgwol6R/SviksolRqWUKg+aILDaIAocz8ol3xjHYDhn\njUKrsT8lg6ycwiqmAkMi6rE87oi7Q1VKqXKjCaKY1Mzck5YgcvIMe4+mI2I1WE+6oCWD29WlT3ht\n9qdkcvBYZgVErZRSZU97MVF0HERqZq5jHERxbesHA/Dn1kR8vb0QEf49vD0A0bus6qX18SnUiwg4\n4VyllKpstAThpLqfN8np2eTmGbzkxAQR2dCqUkpMzaJ4/ohoWAMvgfUJKeURqlJKuZ0mCCcNQ6qR\nlp3Hoi2JLtsgqvv5cH7rOgBk5uSfsC88LIgN5ZAg8vINKRk5br+OUurcpgkCELuZ+poeTQFr0aDi\n4yAKPHxRuxLfp3OTEFbtOUpuXn6Jx5SFl+dsofOzf5CenevW6yilzm2aIJzUCvRztD24aoMAaF0v\nqMTzB7atS3J6DqvdvPpcwXiLN+dvd+t1VNnbuC+FZ3/eWGS6FqU8lVsThIgME5EtIrJdRB51sX+C\niCSKyFr7cbPTvhtEZJv9uMGdcRb0c/XyEmpUs6bLcNWLCSDA15vzmofSpFa1E/b1bxuGn7cXc2MP\nuC1UgK5NQgD4YvkusnLz3HotVbY+/WsXU5bu4usVeyo6FKVOyW0JQkS8gXeAi4EIYJyIRLg4dLox\npov9+Ng+txbwNNAT6AE8LYTSlb0AACAASURBVCKh7oq1gLcIwQFWx66SShAA0yb15s8HB56wPcjf\nh17htZkbe7Bc1olIz85j1a6jbr+OKjvV/bwB+Hz5Ll1LRHk8d5YgegDbjTFxxphsYBowspTnXgTM\nNcYkGWOOAnOBYW6K08HbC0eCKKkEUbCv+FTgBYZE1GPXkXR2JLpvtbnsPEN1P298vYVvo/ee8Y3m\nps9WMumLaEd1R1pWLo//uN6xpKqnSUnP4ae1CY4lYSujZLtzQVxiGl/ZpYi3F2zjf3O3VmRYSrnk\nzgTRCNjr9Dre3lbcaBFZJyIzRKRgXu3SnlsmCm71IkJ1v1OXIE7mwvZ1AZgbe6gsQnMpJy+f0Op+\nDO/YgJlr9zHglUVkZOdhjCEnL58v/97Nd9F7Szw/P9/w09oE5m8+xB+xB3nsh/XsT8kgZm8yX63Y\nw4Wv/VlkxtqTyc83bD+UesafZX9KBpN/ji1VQr3961XcM20tv29wbxWeOyWnZxPZsAbdmobwxMwN\n/L7hAD+sSeDdRds5rItPKQ9T0Y3UPwPNjTGdsEoJn5/OySIySUSiRSQ6MTHxjIMoGPLgLUJSWjYA\nF3docEbv1aBmNVrXDWLFTvdNu5GTl4+vt/DqlZ3p16oOu4+k89rcLTz7cyydn/2DJ2Zu4KEZ6zie\ndWIvp7x8Q++X5nPPtLUAeAnMWBXPzZ9Hc8BpFPi8TQdLFctHS+K48LXFLNt+ZhMVTvtnL58u3cn7\ni3acsG9vUjpr9xY2+O9PtuL7fFnlbXtJycghLNifB4a2BeBfX64iLjGNnDzD9JUlJ3WlKoI7E0QC\n0MTpdWN7m4Mx5ogxpuBr08dA99Kea5//oTEmyhgTFRYWdtYBe3sJk0dE8tzlHbjqvCanPqEE3ZqG\nsmZPstu6u1oJwgsfby++vLknHRvV5KMlO/ls2S7SswtvnHd8tZqZaxJ4Z+F2RyyHUjM5eMz6lbeu\nG8TKxy/k5n4t2LjvGPd/GwNAjQAfpizdyb7kDF6Zs+WkN+Nf1u0H4ImZG85ouvOC8RxzNh4o0rNn\nXuxBzv/vQi5/Zymv/bGFDQkpHE23kvc/u5Jo+8TvTF2+67SvV9GOpmcTUs2Xvq3qEPPU0CL7vvp7\nd6WuPlNVjzsTxEqgtYi0EBE/YCwwy/kAEXH+mj4C2GQ/nwMMFZFQu3F6qL3Nrby8hD6t6nBdr2Zn\n9T6D2tclJSOHQa/+WUaRFZWda/D1LvzT/Wd0J+rVsNbPHtG5IQARDWrw59ZE7p2+lpfnbOGR79eT\nnZvvKCG9N74bc+/vT+0gf/49vD13DAx3vN+kC1qyctdR+ry0gLcXbuentftKjCUjx0oecYfTGPTq\nolJ330zPzsWYwgF/xzJzufqD5SzYfJD8fMPKXYUz4765YDuXvvUXR9NzmNCnOY1DrR5kT/60sVIl\nibx8w8GULOrVsKZiqVndl9l3n0/nJiE8OLQN+1IymV/KkptS5cFtczEZY3JF5E6sG7s38KkxZqOI\nTAaijTGzgLtFZASQCyQBE+xzk0TkOawkAzDZGOO2ubQLRkUX9DA5W4Pa1cXP24s9SensT8mgQc0T\nu8SejZy8fHx9ChNERMMarPj3hQAYY7ihTzOa1Q6k5//Nd3wj/X51PE1qVeO85rUAa8xHAS8v4aGL\n2pGWlcf2Q8e5fUAr5mw86Jg25MXZmxjWoT41Ak5cMS8tK5cx3RtzLCOHP2IPMmfjATbuO0aQvw+3\n9Q932Zifnp1LxFNzaN+gBnWD/YlsWIN+rerwweI4bvwsmmdHRJKUlk2DmgH8e3h73py/jW12w3l4\n3SCeGRFJ7L5jDH9zCc/9uomhkfUdN11PdsV7y8jOy6dlWKBjW0TDGvx0R19y8/L5bNluZq5NYGhk\n/QqMUqlCbm2DMMbMNsa0McaEG2NesLc9ZScHjDGPGWMijTGdjTEDjTGbnc791BjTyn5McWecBWpW\nK5slQ329vfjl7n4ALNpy5m0jJcnJy8fPxVQgYDW0d29WizpB/ky6oCUAz46IJKpZKK/P2+ZovA51\nShAFnhkRyZc398TLS5g2qRcrH7+QX+7qx9H0HJ6dFevyesezcgny9+Hd8d0IDwvkzq/X8N6iHbw8\nZwtXfrCcOBeNzwlHMwDYtP8Yf25NpGY1Xx4b3p75D/Snaa3qzFgVT1JaNqHV/bisc0Pm3t+fSzpZ\nhc2Cv1FEwxosfmgg+fmGj5fEneZvsGLE2O0pTUKrn7DPx9uLy7s0ZM7Gg2zaf6y8Q1PKpYpupPYo\nZZUgwKrfbxRSjWdmbWTUu0v5ae0JTShnLCcvHz+fU//p7hncmgeHtmFM98a8NLojYcH+zLSri0Kr\nn5ggnAX6+xAW7E+HRjW5tX9Lvl8dz8Z9ReeZMsaQZicIH28vpkzoQa1AP/q1qsPLYzqxI/E413/6\nD0fTsjmUmklmTh55+YaVxcZuHDluVXuFhwVxywUtWZ+QwvzNh4qUcl67qjMvjOrAkPaFizQ1rV2d\nQe3q8tGSnfy17TBJadnk5xv+2ZnEfdPXkp2b77guWItBrdlTceNGQu01y89rUcvl/rsGtSbAx4uX\n52whX9silAfQBOGkLBNEwVTgwQG+rNmTzD3T1vKf3zefMGbhTBols/OKtkGUJMDXmzsHtSbQ34dW\ndYOZMuE8x76Q6qX/rLcPaEWwvw///X0LiamFXTEzc/LJN1YyAeuG/edDA/jixh5cGdWEKRPO4+Cx\nTB78LoYeL8znuk9W8NB3Mfz7x/UAfHlTTwDScwp7W10d1YRGIVaV3Dan7rP+Pt6M79mMasWqAcfZ\n82dd+8kKuj03l//N28qb87fx45oEXvg1lgEvL+KKd5eRlJbNv3/cwKh3l7E/JaPUn70s+fl4cXVU\nkxL/djWr+3LnoNYs2HyIBZvd101aqdLSBOEkwLds2iAKXNKpAdFPXMiW54cxpntj3lu0g5d+2+zo\nUbTrcBrh/57NT2sTWLbjMGkuuqW6kp2bX6oEUVyHRjX56Y6+TJ/U67TOr1nNlzsGteLPrYmc98I8\n4hKPk52bz59brZtYkH/h7y04wNfR7tC1aSiPDGvHfPtmt3LXUX5YU1iSOq9FKG+N68onNxQmLj8f\nL764qQdgrQN+KgPahvHWuK6O128t2E6q/Xv8fPlu0rPziN1/jAEvL+TnGKv0dO+0tTwzayNT/97N\ntoNnPobjdKVn5TmSaUluPr8F9WsE8PKcLTpfk6pwumBQOfD38ea/ozuRmJrFB4vjqF8zgIl9WxAT\nb9VJF4xJ6NG8Ft9M6uVyFHdaVi7xRzNoWz/YboM4s9ze2Z7H6XRNOr8lB1Iy+WzZLh78Loa0rDy2\n2DfXk930bj6/JdsOHmd74nHij6Zz8FgWD13UlqER9fD38eYyu9eVs/CwIOL+b3iJo9WdiQiXdW5I\nkL8Pd32zhrTsXGL2JhPZsAZeIozt0YQuTUJ47If1rIu3qshW7Exixc7CPg+bJg87oWRyutbFJ3Pd\nJ//w5U096di45gn7jTGkZecS6H/y6/h6e/HsyEhunbqK+7+NYVyPJvQJr3NWsSl1pjRBAJd3aVjk\nhuEOXl7C29d0peMzfzBz7T7+2Znk6JlT4J9dSUQ9P5epN/WkQyPrJlMwMjp23zG+WxXPVzf3dAyU\nK09eXsIzIyJpVTeIJ2ZuKLLvVDN9/GdMJwASkjN4+qcNjOzSkMYuGmqLX+90DGxXlw3PXsT909fy\nw5oEbujTnKuiCseyzLqzH1P/3k3z2tVZtuMI2w6mMm+TVbJ5Z+F27h/Shv+bvYmwYH9u7R9e0mUA\n62afmZNfJKnExKeQkpHDZW//xStXdmZM98ZFzileHXcyQyPq0bFRTX6O2Wc97uznMuko5W5SVSYM\ni4qKMtHR0RUdxil9tnQnz/xc2CMopLovjw9vT5cmIbw2dyu/2dNITOjTHH9fL3q1rM3EKSuLHJ+c\nnsOV3Rvz8pWdyz3+3Lx8Wj3+GwDPjYwkNSuX63s3J6gUN77ykJyezferE7i+d7NTVqPl5uUz8bOV\nLNlWdBT47/eeT7v6Nfh+VTz/m7eV2fecX6SL77R/9vDC7E3Mvvt8mtSyEt3U5bt48qeNAPh5W9Vk\nvVrWdpyTmJrFeS/M47mRkVxXiqqzuMTj/LbhAO8t2kGArxd3D27NuB5Nz6hqUamTEZFVxpgoV/v0\nX1s5m9C3BV2bFlbzNKxZjSujmtC6XjAvjOpIb/um8tmyXXzwZxzT/imcFvqank05nmnVr5emF5M7\n+Hh7Me/+C1j04ACu692c2we08pjkABBS3Y+b+rUo1Y3Ux9uLj66PcjSKA9QO9OO+6TFk5ebx4m+b\niT+awWdLdxU5L+5wGqmZuTz780bHtoIR7Ase6E+9mv5c/8k/bEhIITE1i8PHsxwD+krbztUyLIg7\nBrZi5h198Pfx5qmfNvL4j+u1d5MqV5ogKsBH10fxwJA23Ni3BW+O6+LYXivQjykTzyvSvXPOxoNU\n8/Vm1p19eeHyDlzT0+q1czq9kMpaq7rBNK8TeOoDK4EAX2/+uO8CWtUN4q1xXXlhVEc27T/GwzPW\ncSzTGuX9wZ87ioznSLan/Ji36RB/brXGuaRl5yECzWsHMuuOfoRU9+XB72K4+I3FRD0/jzcXWIs7\n1XIx/uRkWtUN5vd7z+eiyHp8Gx3PYz+sP6Oeb/n5htu+XHXGI7V3H0ljxqr4Uk/iqKoGTRAVoE6Q\nP3cNbs1Tl0XQqm5wkX0Bvt6Ovv5XRTWmSa1qPH1ZBJ0ahyAiPHFJBF/c2IO7B7euiNCrpEB/H+bd\n35/LOjdkWIf63DmwFT+t3Ud2bj4PXdQWPx8vHp6xztFFOTk9h5ZhgdSr4e8YpJeelUs1X2+8vITQ\nQD9evKIjmw+kctge4wHQOLQaA9rWPe34ggN8ef/a7lzepSHTo/fy+bJdp/0eqZm5/LbhADd9Hs1v\n6/dz8+fRpZrwcMaqeN6cv43X523jwe9ieHLmBl3H4hyiCcID/WtAOI1CqvGv/uEsfmggY+2+/mBV\nLV3QJgx/n7LtkqsK/WtAuGNQ20WR9blvSBuidx/lw8VWMkjOyCEsyJ/rezdnybbDrI9PIS07zzFV\nPMDg9vUcU2pcFGkl/P5twk66zsjJiAj/u7oLPVrU4r0/d3DkNKcGdx5rcttXq5m36SA/x+w/5XmP\n/bCO1+Zu5Ue7e/L06L2Oxn1V9WmC8EAt6gSy9NFBtAwLQqR8eyspa2XAPx8eyJx7raqn8T2bMTSi\nHq/O3cqR41mkpOcQUt2X63o3o1agH8//Gku6iy6sV3a3elENjajPN7f04pGL251VXCLCU5dGcOR4\nFp8u3Xla56ZlWaWFBjUL56x67Id1vLNw+wmz8ObnG9bHp3DoWCY5eYWlheEd69OwZgAvzt5Uaadb\nV6dHE4RSLtQI8KVtfav6z9tLePCitmTn5jPm/eVsOZhKrUB/agT4ct+FrVmxM4mf1u6jWrEG6Fsv\naMmUiecxoktDeofXdjnZ4enq0KgmF7avx9cr9pR6YCVAht2I/uyISHq0qEWf8Nrk5BlenrOF0e8t\nK3Ls4m2JXPb2X/T4v/mANRgRrPaV50d1IO5wGjd/Hq0LHLlZ7L5jpGeX/m/sDpoglCqFNvWCef7y\nDvj7eNGyTiAT+zYH4Orzmjp6nu0p9k3cy0sY2LZumXdNvbV/OMkZOTz504YiU5+cTMGNJtDfh29v\n7c3Xt/Ri+qReABxKzWK10xxVCclFpyJ56tIIfrmrH3cMbMXAtnW5Z3Brlm4/fEZtIap0MnPyGP7m\nEq79eEWFxqEJQqlSurZXM36/9wIWPDiANvWs0oWfjxdTb+rB9b2bcU85dRzo3iyUuwa15ofVCZz3\nwjy+/Hv3Kc8p6IbrPKV9z5a12fjsRdQI8OHDPwtnxE1Oz3E8f25kJC3DgujQqCaB/j6ICPcNacMF\nbcL4esUejmXmkJ2b75gQUZWN/SnW6omr9yQ71kypCJoglDpLPt5eTB7Z4ZQjsMvS3YNaOUoxT8zc\nwKBXFp20OqIwQRQdsxLo72Mlvo0H6P7cXBKSM0hKy6a6nzebJg/j2hIWz3pgSFuS0rO5/cvVXPn+\nMnq9OJ/5mw46qrIKfLtyLyvi3Lf8bmWVk5fPf3/fzCGnZX4LbDmQysBXFjleP/RdTDlGVpQmCKUq\nIR9vL56+LJKNz17ErRe0JO5wGg9+F0NKuutvm9G7ralkXC2KNbFvCwJ8vTiSls2dX69mVsw+Qqv7\nUc3Pu8ROEh0b1+Rf/cP5a/thYuJTSE7P4abPo7nyg2WO0oQxhoe/X8fVH/6tJQwnf207zLSVe3l3\n0Q6u+mD5Cd2GtzhNIDn2vCb8EXuwyNrs5UkThFKVWKC/D49e3I7xPZsye/0Bbv96Fdm5+Sc0YE+x\nR4O7ShBhwf6se/oi7h7UijV7kklMzSrV5IWPDGvHk5dGAHDXoFZc16sZGxKOccW7y3hkxjoGOy25\ne+X7y6v8ILvX/tjCHV+tLnF/Zk4e93+7lms/WcGT9nxmu46kO7pPF0i1B2iO6NyQxy9pT0h1X96a\nv819gZ+EJgilKjkR4YVRHfnvmE4s3X6ENk/8xtD/LXZUOTl/ew8KcD0tip+PF/cPbcs/jw+md8va\njOraqFTXvrFvc765pRf3DG7Nc5d34LmRkcTuP8b06L3EHU5zHLc+IYV3Fm4/i09Zei/+tolbp0af\n0Yjzs/Hmgu38un5/iYtSbT2Yyg+riy4cNrBtGG/M31aks0GqPZ3Oi1d0JDjAl5v7tWD+5kOsjy+6\nYFd50AShVBVxVVQTetqr1SUkZzD551gOH89yzFR89+DWpxxgWTc4gG8m9eKOga1KdU0RoXd4bXzs\nnlrX9mp2QinlvfHdqBvszzsLt5fpyool+eDPOOZsPMi39vK65aV13SAA7v82xmV7kHO34PE9m3Lv\nha158tIIsnPzeWP+Vse+1MwcvL3E8Xu8vk9zagT4cO/0NS6X8HUnz5llTSl11j68Poo1e47y09p9\nTFu5l2krC2+S5zUPdfv1RYRFDw7gn11J9Amvw09rExgaWZ9+resw5r3l3DNtLa3qBhHZ0PX05b+u\n20+j0Gp0OcN1S4wxBPp5k5adx9OzNlInyJ8hEfVOfWIZ8PPxIsjfh11H0rjps2jeu7YbIU5L+xZM\nu7Lk4YGOWYDBmoTzqxV7mNi3BeFhQaRmWsv4FrT/1Ajw5db+4bw8ZwuXvvUXfz0y6LTn9DpTWoJQ\nqgqpWc2XAW3r8tpVnbkqquiaFB1KuCmXtbo1Ari0U0NqBfoxsW8LvL2E4ABfpt/aixoBPtw3fW2J\nPa7u+Ho1l7+zlIVbDpGcnn3a7RbHMnJJy87jjoHhtK4bxC1fRPPF8l3lMn9Udm4+F7SpwwND2rA8\n7giPO62bsnhrIg/PWAdA7aCiN/e7B1trkT/x4wZWxB3h0LEsalQr+t39tv7h3DO4NenZeTz/Syzl\nRROEUlWQiPDfMZ3Z+vzFvDmuK1ueH0ZoOX3rLElIdT/eHd+dbYeO8+B3MSe0ERTMngswccpKukye\ny+j3lhWpmlm1+6hjyV5X4pOtwYodGtZk6k09qVnNl6d+2sh0uyTlztHfWbnWSo93DmrN/UPa8Ou6\n/SzcYs1bdf2n/ziOK97VuE6QP09eGsHyuCNc/eHf/L7xAMH+RUfde3lZ40/uHtSKH9YkMOa9ZaUe\nJHk2NEEoVYX5+XgxonNDj5ncsV/rOjwyrB2z1x/gps9XOta3SMnI4Y+N1lTkt5zfwnH8+oQUop6f\nx7cr97L5wDFGv7eM53/dVOL7xx+1RoE3Cq1GrUA/fri9DyHVrSTx2dKdRD0/j2dmbXTLzTU7N9+x\nTsut/VsSHhbIIzPWsdXuttq0VnXeGNvF5bljezR19AgDGNahvsvj7hjUiuEd6xO9+yhPzFzv9qk4\ndEU5pVS5ys83DH9zCZsPpHLXoFYMaleXK95b5li6dvbd51O3hj+1A/1YszeZaz9eQXp2HmHB/o4b\n+6cTohjUrmjbQmZOHlOX7+aF2ZtY9cSF1A7yB2BvUjoDX1lErlOJpVnt6nx1c09qB/rz7x/Xc/uA\ncFrXKzr1/unq9txchnesz/OXdwQgZm8y13z0N01qVWfzgVQmj4zk+lOsJpiTl09GTt4p5+16cfYm\nPlgcR61AP/647wLq2J/1TOiKckopj+HlJfx2z/lENqzBWwu2M+rdwuRwcYf6RDSsQZ0gf0SEbk1D\nWfrIIM5rHupIDu3qB/PQd+scCzct3HKIw8ezGP3eMl6YvYkAX68ijbhNalXnmRGReIk1XuOpSyNI\nOp7NzZ9HExOfzI9rEhjyv8XEH00/IdbTkZ2bj593YUmtc5MQHr8kgs0HrBJE3eCAkk518PX2KtWk\njncPbs2t/VuSlJbNq39sOfOgT8GtvZhEZBjwBuANfGyMeamE40YDM4DzjDHRItIc2AQUfPK/jTH/\ncmesSqnyUzB2477pa9lpj5d46KK2julDnIUG+vHdv/qwIu4I6Tl51AsO4NK3lvDa3K3cMbAVE6es\nRARHkrl/SJsTRoBf26sZl3dt5Fget1agH/dOX8vYD/92HPOf37fw1riuZ/yZsnPz8fct+p17XI8m\nrN5zlBmr4mkYcuoEUVqB/j48dnF7wOrWe17zWlzRrfEpzjp9bksQIuINvAMMAeKBlSIyyxgTW+y4\nYOAeoPi0hTuMMa4r7JRSlV6XJiEsfHAAR9Oyid599JTdUXvas+YCXNerGZ8v380Xy62JCguSw3vj\nu3FxxwYuz3deO31kl4akZefy+I9WT6PbB4Tz7qIdTOzbnPA6QSzaeohhHeqXuu3GGEN2ntVI7UxE\n+L9RHbm0UwM6Nir7XmQPX9QOXy+vM1qpsDTcWYLoAWw3xsQBiMg0YCRQvI/Wc8B/gIfcGItSykOF\nBvqd9liFJy6NIDUrl9nr9zO8QwP8fb34fcMBOpdy/ISIML5nM3y9vIjdf4w7Brbiu1XxvPDrJnq2\nqMW7i3Zwba+mjvaEU8m2e1YVNFI78/Nx3w28YK0Sd3FngmgEOA9ljAd6Oh8gIt2AJsaYX0WkeIJo\nISJrgGPAE8aYJcUvICKTgEkATZs2Lb5bKVVF+Xp78dpVXXjtqsJKhv8b1fG0V2C86rwmjucPDW3L\nw9+vY9Vua6qMr1fs4dpezWhXv8Yp3yfLHq/h7yJBVGYV9mlExAt4DXjAxe79QFNjTFfgfuBrETnh\nr2SM+dAYE2WMiQoLC3NvwEopj3a2y/OO7t6YyIY18PPx4j+jrXmQRr+7jFfmbHEMtIveleRyfYaC\nAX2uShCVmTtLEAlAE6fXje1tBYKBDsAi+w9bH5glIiOMMdFAFoAxZpWI7ADaANqPVSnlFt5ewrRJ\nvcjPh5rVfWlSqzr/+W0zby/cTmigHyO7NGTM+8sB6N2yNt2bhXJ510Y8NCOGCX2aA1WvBOG2cRAi\n4gNsBQZjJYaVwDXGmI0lHL8IeNDuxRQGJBlj8kSkJbAE6GiMSSrpejoOQilV1owxjPvob1bsTKI0\nt8r/Xd2ZUV3LvjeRO51sHITbShDGmFwRuROYg9XN9VNjzEYRmQxEG2NmneT0C4DJIpID5AP/Olly\nUEopdxARXr+6K9/8s4fUzFx8fYRbzm/J7PX7ycrJ54XZ1qjuRiHVCKnuS8dGZzbJoKfSkdRKKXWG\nflwTD1DpSg3OKqQEoZRSVV1lTgylUbVaVJRSSpUZTRBKKaVc0gShlFLKJU0QSimlXNIEoZRSyiVN\nEEoppVzSBKGUUsolTRBKKaVcqjIjqUUkEdh9Fm9RBzhcRuG4W2WJtbLECRqru2is7lGWsTYzxric\nDrvKJIizJSLRJQ039zSVJdbKEidorO6isbpHecWqVUxKKaVc0gShlFLKJU0QhT6s6ABOQ2WJtbLE\nCRqru2is7lEusWobhFJKKZe0BKGUUsqlcz5BiMgwEdkiIttF5FEPiOdTETkkIhucttUSkbkiss3+\nGWpvFxF50459nYh0K+dYm4jIQhGJFZGNInKPp8YrIgEi8o+IxNixPmtvbyEiK+yYpouIn73d3369\n3d7fvLxita/vLSJrROQXD49zl4isF5G1IhJtb/O4v799/RARmSEim0Vkk4j09sRYRaSt/fsseBwT\nkXsrJFZjzDn7wFoKdQfQEvADYoCICo7pAqAbsMFp23+BR+3njwL/sZ8PB34DBOgFrCjnWBsA3ezn\nwVhrkEd4Yrz2NYPs577ACjuGb4Gx9vb3gdvs57cD79vPxwLTy/l3ez/wNfCL/dpT49wF1Cm2zeP+\n/vb1Pwdutp/7ASGeGqtTzN7AAaBZRcRa7h/Ykx5Ab2CO0+vHgMc8IK7mxRLEFqCB/bwBsMV+/gEw\nztVxFRT3T8AQT48XqA6sBnpiDTbyKf7vAWst9d72cx/7OCmn+BoD84FBwC/2f3yPi9O+pqsE4XF/\nf6AmsLP478YTYy0W31BgaUXFeq5XMTUC9jq9jre3eZp6xpj99vMDQD37ucfEb1dtdMX6Zu6R8drV\nNmuBQ8BcrNJjsjEm10U8jljt/SlA7XIK9XXgYSDffl3bQ+MEMMAfIrJKRCbZ2zzx798CSASm2FV3\nH4tIoIfG6mws8I39vNxjPdcTRKVjrK8IHtX1TESCgO+Be40xx5z3eVK8xpg8Y0wXrG/oPYB2FRzS\nCUTkUuCQMWZVRcdSSv2MMd2Ai4E7ROQC550e9Pf3waq6fc8Y0xVIw6qmcfCgWAGw25lGAN8V31de\nsZ7rCSIBaOL0urG9zdMcFJEGAPbPQ/b2Co9fRHyxksNXxpgf7M0eGy+AMSYZWIhVVRMiIj4u4nHE\nau+vCRwph/D6AiNEZBcwDaua6Q0PjBMAY0yC/fMQ8CNW4vXEv388EG+MWWG/noGVMDwx1gIXA6uN\nMQft1+Ue67meIFYCre0eIn5YxblZFRyTK7OAG+znN2DV9Rdsv97uxdALSHEqgrqdiAjwCbDJGPOa\nJ8crImEiEmI/r4bV0kkiDQAABUxJREFUVrIJK1GMKSHWgs8wBlhgf2tzK2PMY8aYxsaY5lj/HhcY\nY8Z7WpwAIhIoIsEFz7HqyzfggX9/Y8wBYK+ItLU3DQZiPTFWJ+MorF4qiKl8Yy3vRhdPe2D1ANiK\nVR/9uAfE8w2wH8jB+tZzE1ad8nxgGzAPqGUfK8A7duzrgahyjrUfVjF3HbDWfgz3xHiBTsAaO9YN\nwFP29pbAP8B2rKK8v709wH693d7fsgL+LQygsBeTx8VpxxRjPzYW/P/xxL+/ff0uQLT9b2AmEOrB\nsQZilQRrOm0r91h1JLVSSimXzvUqJqWUUiXQBKGUUsolTRBKKaVc0gShlFLKJU0QSimlXNIEoSoV\nEcmzZ7iMEZHVItLnFMeHiMjtpXjfRSJSKdYjLi8i8pmIjDn1kaqq0gShKpsMY0wXY0xnrMkVXzzF\n8SFYM556JKfR0Up5HE0QqjKrARwFaz4oEZlvlyrWi8hI+5iXgHC71PGyfewj9jExIvKS0/tdKdaa\nEVtF5Hz7WG8ReVlEVtpz7d9qb28gIovt991QcLwzsdZK+K99rX9EpJW9/TMReV9EVgD/tef5n2m/\n/98i0snpM02xz18nIqPt7UNFZLn9Wb+z58JCRF4Sa22OdSLyir3tSju+GBFZfIrPJCLytljro8wD\n6pblH0tVPvrtRVU21cSakTUAa8rjQfb2TGCUMeaYiNQB/haRWVgTsnUw1iR9iMjFwEigpzEmXURq\nOb23jzGmh4gMB54GLsQayZ5ijDlPRPyBpSLyB3AF1pTbL4iIN9YU4q6kGGM6isj1WLO0Xmpvbwz0\nMcbkichbwBpjzOUiMgj4AmvU75MF59uxh9qf7QngQmNMmog8AtwvIu8Ao4B2/9/evYTaFMVxHP/+\nZkIkSQYykEeSR0QiIWVgoiikPGaEAWViIEomipGRZ7d0i/KISDcGpEshXZKxorwH3nT9Df7rsGNv\nr5Iuv89kn/Y+e5+1zunstdda9VsREa1YEWALMC8i7lf2NdVpIjCKXNNjMBlFceCnfhX7J7mBsJ7m\nTeVmPw1okzSWjBvYoUwT/UjGHQ+uOX8ucDAiXgNExLPKsVbY4HVyTQ7IfKFxlbH4/sAIMsfrgDKs\n8ERE3Gwob3tlu7uy/2hEdJfXM4CFpTwXJA2U1K+UdUnrhIh4rkx7HUPe1CEXvukkY77fAvuVq9Cd\nLqddBg5JOlKpX1OdZgLtpVwPJF1oqJP9J9xAWI8VEZ3liXoQmQE1CJgUER+Uaai9fvGS78q2my//\nDQHrI+Lc128ujdF88ga8KyLa6orZ8PrVL5bt88cCHRGxtKY8U8gQukXAOmBORKyWNLWU87qkSU11\nKj0ns888B2E9lqTR5JKMT8mn4EelcZhNLtEI8IJcDrWlA1glqXe5RnWIqc45YE3pKSBppDLFdBjw\nMCL2AvvI6Og6iyvbzob3XAKWlevPAp5ErqvRAayt1HcAcAWYXpnP6FPK1JcMdjsDbADGl+PDI+Jq\nRGwhF8wZ2lQn4CKwuMxRDAFm/+C7sX+cexDW07TmICCfhFeUcfzDwClJt8jEzrsAEfFU0mVJt4Gz\nEbFJ0gTgmqT3wBlg83c+bx853HRDOabzGFhAJq1ukvQBeAksbzh/gKQusnfyzVN/sZUcruoCXvMl\n0nk7sKeUvRvYFhHHJK0E2sv8AeScxAvgpKRe5XvZWI7tlDSi7DtPJq92NdTpODmncwe4R3ODZv8J\np7ma/SFlmGtyRDz522Ux+x0eYjIzs1ruQZiZWS33IMzMrJYbCDMzq+UGwszMarmBMDOzWm4gzMys\nlhsIMzOr9Qkr4EhtK/nkaQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xUAqQahTXLU5"
      },
      "source": [
        "Optional additional training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Jsf4pEZsXLU6",
        "colab": {}
      },
      "source": [
        "# learn.load('radam_classifier_final');\n",
        "\n",
        "\n",
        "# learn.fit_one_cycle(10,\n",
        "#                    slice(lr/(2.6**4),lr), \n",
        "#                    moms=(0.8,0.7),\n",
        "#                    callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', \n",
        "#                                                              name='radam_classifier_final_2')])\n",
        "\n",
        "# learn.recorder.plot_losses()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QrRTQdEmXLU-"
      },
      "source": [
        "Additional training doesn't seem to benefit \n",
        "\n",
        "final accuracy approximately 0.86571"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JcvaRxSzXLVA"
      },
      "source": [
        "Exporting model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NLcp_mIvXLVB",
        "colab": {}
      },
      "source": [
        "learn.load('classifier_final');\n",
        "\n",
        "learn.export()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7Al0Ld2MyB1",
        "colab_type": "text"
      },
      "source": [
        "# Ensembling forward and backwards model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24FGHyElNp1P",
        "colab_type": "text"
      },
      "source": [
        "Forward model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8NN-xjyMxTg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn_c = text_classifier_learner(data_clas, \n",
        "                                  AWD_LSTM,\n",
        "                                  metrics=[accuracy, error_rate])#.to_fp16()\n",
        "                                  \n",
        "learn_c.load('classifier_final', purge=False);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCgKyYNnNM7u",
        "colab_type": "text"
      },
      "source": [
        "Backwards model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btvMhf5vNOBr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_clas_bwd = load_data(PATH, \n",
        "                          'data_clas_bkwd', \n",
        "                          bs=bs, \n",
        "                          num_workers=1, \n",
        "                          backwards=True)\n",
        "\n",
        "learn_c_bwd = text_classifier_learner(data_clas_bwd, \n",
        "                                      AWD_LSTM,\n",
        "                                      metrics=[accuracy, error_rate])#.to_fp32()\n",
        "\n",
        "learn_c_bwd.load('classifier_bkwd_final', purge=False);"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}