BertEmbeddings.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/davidmakovoz/machine-learning/blob/master/BertEmbeddings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73lNOyGV9cXl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8MnFfNT9008",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "ea08dff9-43bb-4161-c42e-e11ea949d00a"
      },
      "source": [
        "!pip install pytorch_pretrained_bert"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pytorch_pretrained_bert\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/e0/c08d5553b89973d9a240605b9c12404bcf8227590de62bae27acbcfe076b/pytorch_pretrained_bert-0.6.2-py3-none-any.whl (123kB)\n",
            "\r\u001b[K     |██▋                             | 10kB 19.1MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 20kB 6.1MB/s eta 0:00:01\r\u001b[K     |████████                        | 30kB 7.2MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 40kB 5.6MB/s eta 0:00:01\r\u001b[K     |█████████████▎                  | 51kB 5.8MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 61kB 6.9MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 71kB 7.3MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 81kB 7.0MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 92kB 7.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 102kB 8.0MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 112kB 8.0MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 122kB 8.0MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 133kB 8.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (4.28.1)\n",
            "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (1.10.47)\n",
            "Requirement already satisfied: torch>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (1.3.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (2.21.0)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (2019.12.20)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pytorch_pretrained_bert) (1.17.5)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch_pretrained_bert) (0.9.4)\n",
            "Requirement already satisfied: s3transfer<0.3.0,>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch_pretrained_bert) (0.2.1)\n",
            "Requirement already satisfied: botocore<1.14.0,>=1.13.47 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch_pretrained_bert) (1.13.47)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (2019.11.28)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch_pretrained_bert) (2.8)\n",
            "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.14.0,>=1.13.47->boto3->pytorch_pretrained_bert) (0.15.2)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in /usr/local/lib/python3.6/dist-packages (from botocore<1.14.0,>=1.13.47->boto3->pytorch_pretrained_bert) (2.6.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\"->botocore<1.14.0,>=1.13.47->boto3->pytorch_pretrained_bert) (1.12.0)\n",
            "Installing collected packages: pytorch-pretrained-bert\n",
            "Successfully installed pytorch-pretrained-bert-0.6.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoSiEjPCGOll",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set CSS properties for th elements in dataframe\n",
        "th_props = [\n",
        "  ('font-size', '20px'),\n",
        "  ('text-align', 'center'),\n",
        "  ('font-weight', 'bold'),\n",
        "  ('color', '#ffffff'),\n",
        "  ('background-color', '#000000')\n",
        "  ]\n",
        "\n",
        "# Set CSS properties for td elements in dataframe\n",
        "td_props = [\n",
        "  ('font-size', '20px'),\n",
        "    ('text-align', 'center'),\n",
        "  ]\n",
        "\n",
        "# Set table styles\n",
        "styles = [\n",
        "  dict(selector=\"th\", props=th_props),\n",
        "  dict(selector=\"td\", props=td_props)\n",
        "  ]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCljm2LF9lxE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NmvFT72dR36",
        "colab_type": "text"
      },
      "source": [
        "<img src=\"http://drive.google.com/uc?export=view&id=1bhjSBpVLmh3h___dXPbnW1pfed0yQe4z\" alt=\"Drawing\" style=\"width: 800px;\" />"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQXQQOs2-XCF",
        "colab_type": "text"
      },
      "source": [
        "## Load Tokenizer for BERT-Base, Uncased: 12-layer, 768-hidden, 12-heads, 110M parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwcyyTfgm8BM",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZihxGTP-apG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1e3da5e6-f61a-4900-9c77-1d3491927c87"
      },
      "source": [
        "# Load pre-trained model tokenizer (vocabulary)\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 231508/231508 [00:00<00:00, 673670.32B/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqFJqmpZAdmF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cd6e8d93-4a3a-4f5c-bd58-0826c742c019"
      },
      "source": [
        "len(tokenizer.vocab.keys())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30522"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "empsoVSZA2pY",
        "colab_type": "text"
      },
      "source": [
        "### Infrequent words not found in the vocabulary are broken into pieces"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFIVTcuaA3Q8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "0ad6fa90-89ff-4bc8-f283-7fde0ad17966"
      },
      "source": [
        "words = \"embeddings beddings breading  dreading heading hedding kaeding reading redding shedding shredding spreading steading tedding threading treading wedding yedding\"\n",
        "\n",
        "for word in words.split():\n",
        "    print('{:<12} {:}    {:}'.format(word, word in tokenizer.vocab.keys(), tokenizer.tokenize(word)))\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "embeddings   False    ['em', '##bed', '##ding', '##s']\n",
            "beddings     False    ['bed', '##ding', '##s']\n",
            "breading     False    ['bread', '##ing']\n",
            "dreading     False    ['dread', '##ing']\n",
            "heading      True    ['heading']\n",
            "hedding      False    ['he', '##dding']\n",
            "kaeding      False    ['ka', '##eding']\n",
            "reading      True    ['reading']\n",
            "redding      True    ['redding']\n",
            "shedding     False    ['shed', '##ding']\n",
            "shredding    False    ['sh', '##red', '##ding']\n",
            "spreading    True    ['spreading']\n",
            "steading     False    ['ste', '##ading']\n",
            "tedding      False    ['ted', '##ding']\n",
            "threading    False    ['thread', '##ing']\n",
            "treading     False    ['tread', '##ing']\n",
            "wedding      True    ['wedding']\n",
            "yedding      False    ['ye', '##dding']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABWhMbGRBiZi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "411e92bc-92ac-424b-bf62-f4012d9da9c8"
      },
      "source": [
        "'##dding' in tokenizer.vocab.keys()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-2_ZoJlBo2z",
        "colab_type": "text"
      },
      "source": [
        "## Load BERT-Base, Uncased: 12-layer, 768-hidden, 12-heads, 110M parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9jYudd9Bphn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d88785b2-1976-4adc-d057-e7da291de52b"
      },
      "source": [
        "model = BertModel.from_pretrained('bert-base-uncased')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 407873900/407873900 [00:15<00:00, 26540778.44B/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2g5tmCeiB5KU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9d774be7-d260-448a-dd2d-62365eded0ff"
      },
      "source": [
        "model.eval()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertModel(\n",
              "  (embeddings): BertEmbeddings(\n",
              "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "    (position_embeddings): Embedding(512, 768)\n",
              "    (token_type_embeddings): Embedding(2, 768)\n",
              "    (LayerNorm): BertLayerNorm()\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (encoder): BertEncoder(\n",
              "    (layer): ModuleList(\n",
              "      (0): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): BertLayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): BertLayerNorm()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pooler): BertPooler(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (activation): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rX75SlpTB_JR",
        "colab_type": "text"
      },
      "source": [
        "## bert_embedding returns the bert embeddins for the words in *text* for the specified *layer_number*. Among other things it:\n",
        "- adds special tokens [CLS] and [SEP]\n",
        "- adds positional layer\n",
        "- adds sentence layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qzooy5cwc498",
        "colab_type": "text"
      },
      "source": [
        "<img src=\"http://drive.google.com/uc?export=view&id=1xtEJ6tCPUr_cBDPoM8RaRjC9iGG8j2oA\" alt=\"Drawing\" style=\"width: 800px;\" />"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57SW-7mCCAaA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def bert_embedding(text, bert_model, bert_tokenizer, layer_number = 0, verbose = False):\n",
        "    \n",
        "    # Add the special tokens.\n",
        "    marked_text = \"[CLS] \" + text + \" [SEP]\"\n",
        "    \n",
        "    #the default is the last layer: 0-1 = -1\n",
        "    layer_number -= 1\n",
        "\n",
        "    # Split the sentence into tokens.\n",
        "    tokenized_text = bert_tokenizer.tokenize(marked_text)\n",
        "    if verbose:\n",
        "        print (tokenized_text)\n",
        "\n",
        "    # Map the token strings to their vocabulary indicies.\n",
        "    indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)   \n",
        "    \n",
        "    # Mark each of the  tokens as belonging to sentence \"1\".\n",
        "    segments_ids = [1] * len(tokenized_text)\n",
        "    \n",
        "    # Convert inputs to PyTorch tensors\n",
        "    tokens_tensor = torch.tensor([indexed_tokens])\n",
        "    segments_tensors = torch.tensor([segments_ids])\n",
        "    \n",
        "#torch.no_grad deactivates the gradient calculations, saves memory, and speeds up computation \n",
        "#(we don’t need gradients or backpropagation since we’re just running a forward pass).\n",
        "# Predict hidden states features for each layer\n",
        "    with torch.no_grad():\n",
        "        encoded_layers, _ = bert_model(tokens_tensor, segments_tensors)\n",
        "        \n",
        "    return encoded_layers[layer_number][0][:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRy5H6FYCHgN",
        "colab_type": "text"
      },
      "source": [
        "## Experiment with the sentences:\n",
        "\n",
        "<font size=\"5\">*'i love the beautiful **spring**, because the flowers are blossoming in the **spring**.'*</font> \n",
        "\n",
        "<font size=\"5\">*'when it takes a load, the torsion **spring** rotates on its axis.'*</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFkTF_kSCIOS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text1 = 'i love the beautiful spring, because the flowers are blossoming in the spring.'\n",
        "text2 = 'when it takes a load, the torsion spring rotates on its axis.'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZxkwOKtCTWf",
        "colab_type": "text"
      },
      "source": [
        "### Output tokenization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0OqyBbpCUCy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "29af41e3-0abf-4d8b-9099-d082a81ff04e"
      },
      "source": [
        "embed1 = bert_embedding(text1, model, tokenizer, verbose=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['[CLS]', 'i', 'love', 'the', 'beautiful', 'spring', ',', 'because', 'the', 'flowers', 'are', 'blossom', '##ing', 'in', 'the', 'spring', '.', '[SEP]']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpPDfIxgCZn6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0d00f336-2c02-426c-ef44-23e0956b39c9"
      },
      "source": [
        "embed2 = bert_embedding(text2, model, tokenizer, verbose=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['[CLS]', 'when', 'it', 'takes', 'a', 'load', ',', 'the', 'tor', '##sion', 'spring', 'rotate', '##s', 'on', 'its', 'axis', '.', '[SEP]']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1L7wU3JYChM-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "spring1 = embed1[5]\n",
        "spring2 = embed1[-3]\n",
        "spring3 = embed2[10]\n",
        "because = embed1[7]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1reLSxBRCljl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "096ee4a1-1412-466c-c90c-8a4613a0c2c9"
      },
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(spring1, 'b.')\n",
        "plt.xlabel(\"BERT embedding of the first word 'spring' \",  fontsize = 20)\n",
        "plt.ylim(-2,2)\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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E2CdsEeKAHQIxtNvYcNXWxj6BohLWk9BmADHO8NoIxKblGzLi/RB0FXBN6iuWoLZdsdUn\nxj4YhIztuo+h3ZJpTKCohFkgpNlmlwHV9+DSRiA2LR+FrKFJffWtq5DafxE2rB5TGAxCxVXdh95u\nfeB7LMhTNoEKLZ99oBLmCVeNqO2AGsrg0lQgtikfhWzz+hpzXdlQyGO0MI+FoQZiV5uDuroRDK1k\n9BkLXOW3aAIVyphlCyphHnDdiNoMqDEOLvnyjWlW5AJbClZRPcdS933rgD5E/hhiIHYhk7ve05eS\n0XUsaBstv428KJpAbdgQ35hVBZUwizRtYNnGvnOnCaPgqxHFPriMbVYEhKnYFNUzML66L4PL2/4Y\nYiB2MRntek9fE+OuY0HT/HaV1fkJVOxjVh4qYZZo08BWrzbO4rt3m917W7b4cxqPfXCJ0ZJXRZmy\n4/v5FNUzMK66r2PMS7ah43ogLrtfnwlR1zwOpWTky9Z1LGiaX1uyOvYxKw+VMEu0aWBzc2a33qZN\nRgnbtcvvABbz4DK2WVG+HW3dClxwgX9rU1k926r7EK1/JFxsD8RF9+trZe+axyGUjLKydRkLmubX\npqyOeczKQyXMEm0b2Jo1ew+uRddnByaAg1QRY5sV5dsR4N/alLbDjRvNcSTZeu4TvyzbtqeyrEn2\npo/ybXsgzt/PhuWmax5dKxnz880P3G5CUX5tWdrGDpUwS7RtYHXXpzOVxUUTaDQ9l4uD1L6MaVaU\nbxdAvbLukjprQJe6z9/zlFP8K5qhMCWLYOj+nGOzsgNL7euHP2x+4HbXdGxZ2sYOlTCLtG1gVddn\nZyqAGaCA9rtWpiLQx0S+XficPQ7hsAyMb7DrQuhKiW1C9+ccm+Um275EzEu1/sDtLoT+bEOCSlig\nrF69ZP1KaXP0TheBTqUtTFzPHqueuwtrQP6ea9aY19Tb3tQGrqEtTV3kW1nfi1FWZtvXzAywbNnS\n6ortus+fWHLrrabOYqmrQVHVYF/HHHOMTplNm1T32091ZkZ1//1V161T3bat2W/POkt1dlYVMH/P\nOqv6+m3bVO9/f3Pt/e/fPJ1Y2LbN1MHYytWXJs/dRd31uedYn+XY+2ARQz1Lm3Ub63PK53vTJrd1\nv22bGbOWL4+vrvoAYLu20HNoCeuJyxnR2rXAkUd2u3/bWeaYZ+FTW+ZpQ5Pn7sIS1/Werp5lCJaN\nsS1/NWEoHyGb8i1WWTl0+5qbM2nt2hVfXQ0JlbAeDDG499ld06bDjdEJNWUIoRnCIN6F2J67i2cZ\n0tI9HZfdYLOdx9ZnsrRpXzbaeMx1NRRUwnoQ+oyoTYcbepY0pNJiQxBU5TdmS1ts1hdbQj37PNv2\n45if91Sx2c7b3ivGCZqtNh6bfPEBlbAejE3LH2oWPvQg1lcQ1OU3dGW8jqGtL31jQ/UV6vnnuXFj\nmEv3MQ7eIWOznTe9V6wKe982XhQjjBRDJawH1PK74UNp6SMI6vLLnUDNsTEo9RXq2ee5uAhceGFx\nINoyhph8xTp4k73pYmUNYTzp08aHbLuh1FcfqIT1hFp+e0K1IJZ16Lr8psr41q3mHND3vMcEWOXA\nuS8hWA3T55nG4bv0UuCKK9odKOx68hVCPZH+tJF1ISnefdr4kJbiuvqKQUmzooSJyAkA3gFgFsC5\nqvrm3PcvAfBWAHckH52tqufaSJvER4gWxKoO3SS/3AnUjBAU8PR5nnmmUcD27Gn/vFxPvorqKYYB\nhexNG1kXmuLdtY0P1cfr6iskpbaK3kqYiMwCOAfAswHcDuAqEblIVW/MXfohVT29b3pkHIRmQazr\n0E3ya1v4jHHQDcWpeW7OKGFXXBGeRRbYt56A8AaUMbZPFzSVdSFMUGww1CS7rr5CU2rLsGEJexKA\nW1T1awAgIh8E8HwAeSWMjICxCl4bAtCm8IllFteFUJyaQ7TIZsnW04YNYQ0oY26fVZTJPxty0cYG\nolDa8hCT7Lr6ikWptaGEHQrgtsz72wE8ueC6k0Tk1wD8J4A/VdXbCq6BiKwFsBYAVq5caSF7xBZt\nBG9IAqEJtgZkW8InpFmcr2c5RB30eV6xhVmxic1nE4usKJN/NhXSru2xbx5cPwMfcfVCn2SlDOWY\n/zEAH1DVRRH5nwAuAPCsogtVdTOAzQCwatUqHSh/JEdRp2kqeGOdJYe0RBrKoOvzWYZSB0XEFmbF\nNjbjtcUiK8rkXwgTpj55cP0MfD7jkGR6GTaUsDsAPCLz/jAsOeADAFQ1e0b7uQD+xkK6zollhmab\nsk7TVPCGIJRix9ag27cN+3yWoSkeWXzUS0gDiq1nE5OsKJN/IUwW+uTB9TMou7+t8TX2cdqGEnYV\ngCNE5JEwytfJAH4/e4GIHKKqdyZvnwfgJgvpOiWmGZptyjpNU8EbglAaA30HXRtt2PeztFEHLgS0\n73oJARtKYUz1WLRZYsMG87/vyUIfpdj1Myjb6WtjfB3DON1bCVPVXSJyOoBLYEJUnKeqN4jIG2BO\nE78IwB+LyPMA7ALwfQAv6Zuua2KaoRXRZ/Cp6pRNBG/IFowpYaMNVz3LNm3Mx2y1qYDukrfY27hL\nB/M2xFaPqfwralvr14eRty6/6/IMmraVovvb2mgS+zgNAFDVYF/HHHOM+mLbNtX73191dtb83bbN\nW1ZaYyPv27apnnVWeOUONV8h4rINt7m3r7501lkmTcD8PeuscPLmk7IyT7EuutKkbbkgFPnXt63Y\namshtlkY41NjPYcR80uIbYaWxZYFJLQy2zY9x+5LUIfLNtymjfmarTZZZnGZt1DbV8gO5rHgYxk1\npKW3vm3F5m70WMfpFCphFYSoiDQhJj+LNtgcJEISaC5x1YazbazuvExf7bGJgHaVt5Dbl28H81CV\n0zaUtS2XZQtJSbYVV9FG/mMdp1OohI2QMcwOirA5SIQk0Gww5MCWprVxI3DttfXnZbpsj3XlrhPQ\nrvIWcvsqK3OTuujbzkJWTtuSb1uuy1Ym/3wota7HmDEo6o1ps3Y59MunTxgJE1s+EU18CXz6X7RJ\nu49fRNsybtqkut9+qjMzJq116/z4xqiG6Q+SEnLeumKjTL58qYZgiLLl+6vNdhaKvIu974A+YWTM\n2DRhV83kfM7Y26bd1erSNp2FBeC008wh5QCwc6f562vpOwZr09atw6Q3hOXARn2PeclziLLl5Z+t\nPhCSvDvllHD7tQuohPVgUiZTS4RUZ1UKXR/Fpk35+pxMkNJV+LdNZ37eXJuiChx1FLBmTXHspDbP\nt0u7CMn3sSz/F1xg8le2VGsr7SEGUFt+QK5dJXwpFD7cQFasAGZmgD17ABHzPqVNn/I5ocmnDYTT\nrwehjdls6FfIy5Gxm0x94LvOmprbt20zy2zLl7fLa9vy2QwV0GUpoUt+ly0zyy2AWZLMLrnklyqb\n5sXVcupQyytl+R9q6W3IJb5QQiRUEcOSp416TNudiCmryFL7syWLhqAo7RjaWRngcuQwNJk5hGT1\nCYG6OnNZX20Cd6bXzc4Cp55qrD3z8+Z7m8uCZdd3mVF3WaZtm87cHHDOOcDpp5s8L1++t3Nwdqly\ncbH5bLrPLLys3ENaQ8ryP5SlzkU6ZX0xhp1oIVlIi7DVNtN2p8kJy6pL7Q9o16d8buaq2igyBaiE\ndaSuo49pF5AtqurMdX01Heiz16WccUazfLUV/n1PJrBB23TWrgWOPLJ4CXXPnqXrZmebD34uBs0h\nl1fK8j/UwGY7ndhlV+i7w221zbTdLS6avjczs3f7a9unfCrYMSj3rqAS1pFsR1+xYl9LSYhOw74t\nc1XC0XV9NR3o89cBzfPVxbLUdbDw+SyLBObq1cYytrhoBoOzz25nzbI9aA5pDanKf6jKdBXZvri4\nCJx5pnm58Il0RciDuq22mR+DduxYutf8vAkhk34Wal20JW1f2fJGX7Y2a5dDv4byCeuz/hzLESBD\n5MdFPdpMr41PWJvt0kP7L4TWtlJC8+MILT8xkPWHnJlZ8v0L3a+oC6GEZLB935ieQRvSsrVtl0MD\n+oS1IzW9pzP4c84xSy5NaePX43OW6NrS1HcJo601pEt6TWfH+etCC2URopUVsGN9sNlHQraGhEje\nH3LVKmD7drPU1cfHMUR8L7m6apsxPYO2pGVL3R6atsvQmbwSNj+/tKa+Z49xOj7ySDvb5LMdzXen\nd708Y6PztxFMQwqbqnz5EHqhOx53paqP9FHOQlkiC528P+TRRwPXXWfHxzE0xqqsxPQM2lLnAxcr\nk1fCVq9eirMCmE7ZdndWEwtOk07vcrBw7aw6dOcPRdj4yIetZzmUctI0nbI+0mcC43vy44OuzzXf\nltes2TsGXF8fR9/KcDb9UOSHbULflNCHMh+46MvYZu1y6NdQPmFd4xu1oW6tvs1avg8fpLa+VCHl\nayr5aMNQviNt27XteFsxxIyySV9ZVnQsjo2+79tXqUssqhj7NfEPWvqEeVe0ql5DBmsdosNVCbim\ng8XQwsy38EzzQGFol1ADiRY9677BXH2336GoC6bb5X5N6q7JdX3am43+36UdTqXd+GDMMr2tEjb5\n5ciUIZx4q3zENm5sZh4f2pfBt+/EFJeTXJLd4j3EckzbZZ+ifthniWXMyzN55ue7x2oru1/b2Hpl\n13Vd/utyvmnRs26b/tat5mxUzQVAnUI7ymN7GZkyfW+ohA1M2qBvvXVvwbVjR7PBwpYvQ9OOVZbe\nUP4dfWIWhYov35gixd+1X4UtJajPJGkquyT7xGrL0lZRbyKTuraDNpPAqsG9TfoLC8B55y1Fol+2\nzNTFFBUHFwqT74l9aFAJa4iNgTO/BXxZUvup4GoyWNgY1Np0rLJQG0MJpPyOmEsvBa64Il4h6HMW\nmBd+O3YA69e7T7erEpTvc74du0PHhWxooqg3TbeqHdiwYNUN7k3b4fz80g5REeClLzV1MEXFoavC\nVNVXx7opoitUwhpg+6yvtIOfeiqwcmW3JZY+2/Tbdqx8ekPOZFIBf+aZRgGLPTaMz1lgTMKvSBlo\nenzUlOlr9euqqPdJ15YFy1b7LtolCoTRd4aeiHSp07rxckouAk2gEtYAWwNnUed21QCznRXo5n9W\nxtCD+dycUcKuuMK/EOyLT0UoROFXNqjk+9yFF4ZriRiTha5P+8zLnKZ1YsuCZXPpu+g+vvuODyt6\nlzptMl5OxUWgCVTCGmBr4BxqEMx31lNO6eZ/VoaPwTwmBaIK3+UISfhVDSr5PnfSSWEq4T6Xl7sq\nPVV0bZ95VwsRYNeuZnVic2Jiq30X3cd232krP3xZ0duWOyaLewhQCWuAzYFziEEw31mBfTtF33zk\nfz+ENSAWBaKOkMrhk6pBpajPHXmkuzbWtf12GRhd+Je2UXrq6NI+s/WQ7tDM7iysc3ewecSbi918\nNu5XtTrR5JnFotz4nmhGR5t4FkO/howTFjJdDqpuG5iwLr30YN916/oFmh0LXeMejTk+TltCaTdD\nxiGzVeZs+xMxL58BabPl2n9/cwB41zIO/TyGCDKbv8+6dZQfYwWMEzYuuh5UXTQTabqrJZ8eYO6T\nWtW2bAEuv3zpfj6dzX3hwmHVN66smWX3DWXG3Kf9ti2DC//SvCXMh4UkXw9A9+fap46yv60LadOk\nP9p6Xk1WJ4rI9x1a0ccHlbDAaSNUsnTtrEVCBwDuvXfpmrwwisVMXoYL366ie+brdutW/wpIiq2g\nmG3vG8Kg0rT9VimTTcvgyr8U8N+W8vXQNR996qhNSJsmCpbLHZd153KGOmkb00aUIGhjNhv6xeXI\nJTP2zMzSUSR9zeJtze/btpklhvQ4lOXLi5ckYzST216+qLqnzSWbOto+jzbLq23qLJazG4c8+zDW\nvjIkfepo2zbV449fkpll7a7NsUxd8tL1HM6UEPtOKC4EIQOeHekf20K2qVBpcp+uQqfKJyxmXCgf\nVfdM67arT0hZvvLCvq2gdKVYuVByXVGVdogDYhumpvi5VrBspe/6HraJvR8MQVsljMuRlnFhQp6b\nW4qTtbho/D9WrGh/n6b+DUNszw4FmxG5m9wzrceFBeCCC/ovcxS1ty7Lnm18m9rUWZv7+g73UJV2\naEvubZaEQl3Wckm23a1YseRWMZRcKzp7sm06ofhMZgmtH4wBKmGWceWkPjdngqyefrq59xlnmC37\nMcZvcRHfqCsulI8m97QlYIvaW95pe8uWZuELmg5IbfPe9L4+N3jUpR3SgNhWqZrixhlgqYxDK6BF\nZ0/mZUVTJTq0ya/tcCKESgjvxwYAACAASURBVJh1XCo6O3YYZ9OuR/eEMJC4jG+UT8e2ktCm/prc\n04aALWpv2Xzeeivwnve4mRTYbj9Vfce1oG/Sb4ceELMHaWfPb2yrVIUy+fKBDwV0fn7fsyezaYZu\nmaSVdViohFnGpaJjQ5j6nlllhWKboI59BEOTQ4ibppXd6ZgtU2iWPNvLnkNQVpYhBL2Lfts34Oix\nxy7t8puZMRaVl70MOOqodnIghMlXliEtJ01kpu38FO2EzBKaZbJNENm601iGKMvoLG9tHMiGfsXq\nmO+SMkfSWBxvu+wQrHJQLSp31nl0ZkZ1v/3sBdMccoejLWJpG2W0dQYOobx9naqzZc6+RMz9Nm3y\nX8Yu+HA2r2oPZbvB+9Zt2zRt07QMbYPI5vviunXDPs98fkPsB6Bj/jhoE5PIpqXA9SyjS3yjsplj\nWbmzsYIAs9zZ1EG2bpba1ZLng+yzXL+++299l6mNBTiU5ZG+1o58vCuRJVXsnnuMZbftM+2Kzbbg\nwwpUZf3P52fr1r0tx13bT1WaVRZfG/Xcpg/kyw9U97Uu8c5sks3v4qLxkd6zJ/Kl0DYa29CvqVrC\n2s6U1q2zc2xJiFuiVcvzlbd4HX/80szoVa8yFrC0XprGVxuLJazPswyxHTSd2Yeyhb5NHdZZtzdt\nMn3cR1uz3RZCa1tVliAR8z69zqXFxWa99A0h0yRmns8wMml+ly3rH7bJBWCcsPgp60Rl8bvqAql2\nSTdValx2tDaduazs2UC2WYUr20HblqWNEAph6auIPspIKIpMF0Ia5Ju0DRvKmktctAWXsbn6BlUt\nkqebNrlvUzbrOabYfF3ITk5C6etZqIQFRpcGXjY7qbMGZWduXfNqMzp/k7RsHIybDWSbvtr6go2N\nMVnC2vahEAaV2Cx3ZTSxDPuu6zQfttpsfmXh+OP39YOyXeay/G/b1i1IdijPxTUhlpNKmCXyD9eW\nMtU1/SrrmO3lAhvR+avKotps8Gla52XKYx+nzRA7d1v6lCGU8oemEDahrXXLZ/maWuvKlkt95L2r\nPGlz/7zz9xAuCEVjjq1VDjIcXpQwACcAuBnALQD+ouD75QA+lHz/eQCHN7mvLyWsqhO26XhDmZht\nD5g2hWvVDK+p71Ubk7qN3TJDDy6hKDwhErqlqIhYdnP2bec+nk1XedIlnaJJuM3jxuo466wli1y6\n0uHTx5EyqhltlbDeuyNFZBbAOQCeDeB2AFeJyEWqemPmspcD+IGqPlpETgbwFgC/1zdtV+R3jFx4\nYbcdPTaDJFbF+rEd+8tmXKGy3VB1abTdRWWzDobcwdVmJ9NQOxZj3Rnpgi510TbPvmL32drBOeSz\n6SpP2pJ/Jj7i7q1eDey33967Fm2k17ZNh7LjeLS00diKXgDmAFySeb8ewPrcNZcAmEv+XwbgewCk\n7t6+LGGbNhl/ouyylq1lxVAYKl9dZ6g+l2mGTLupNWGoPPleHivLU0iWoj5LeCFh41kPXc4Q2ueQ\nZe7qE1Z1v7b1F6M12ifwECfsUAC3Zd7fDuDJZdeo6i4RuQvAikQZC4qFBXMu4+7dJkr1xo3A2rXm\nnMYusyzfEeqLGHJm03WG6jPK95BpN7UmuLTOZWfGoUXzBsKyFAHN+k6I/T6PjXY+dDlDiP5fV2ab\nlmTb9VvWpqvyO6TF09a5wiFZ8+sILliriKwFsBYAVq5c6Ty9/MNKG2kaIHHHDnNd084Qw8O3OdA2\nKW9bQdI1yGjIwq8qnSaDiitBWHTEk8/lv5AoqvMQldSmFPWPGJTFPCHnOfSlu3ybXrGiPr99FN+u\nx831OVc49GeQx4YSdgeAR2TeH5Z8VnTN7SKyDMCDAOwoupmqbgawGQBWrVqlFvJXysIC8MxnLj2s\nyy/vN9h1ffhDK259B/TswcJnnGG3sfepw5g6XpYmg0oqCLdutZt2XqnYscO/pSEUygafGJXUWPtH\nDJPaLKEr6fk23TS/XRTftm0um5c+p5GE/gzy2FDCrgJwhIg8EkbZOhnA7+euuQjAKQAWALwAwKeS\ntVOvbN26dLTN4qJ5/653dR+Eujx8H8Kx78wmza+I6Sx79thr7F07UGwdryupU/AFF5hnCPQbpIoU\n8qwT8oYN8QyALihy0I5RSR3a+m2DtrIxBIXN90aSJuTbtKv8tm1z2brLW8La5CuGZ5CltxKW+Hid\nDuN8PwvgPFW9QUTeAOOgdhGAfwTwXhG5BcD3YRS1YOlq7u7y8LduBXbudH/+YF5AdS1jtmPNzCx1\nFluNvWsHiq3jdSEv1Gycc1emVNiaHIQwMNrG5nLYUPVjq38MOWlsM4iHYukbQkm37XbhKr9ddgm3\nPVe4yX2ClzttvPiHfrneHZkGwxMxf23tPmlzFM8Qwfhcxv1ycYp9191HfXcthbCjrSoP+bp3GbPI\nxo6oonYXQh2HQozx6NqeS9i3Pzatn6ns4GtSJy7iRsYe9HlIwIj57fDZSGweOdQ0HRtnQjats5g6\nYChb39sIWJd5tnHv/MC4bp3/Og4JW4puiCEibLXNNrImG9XeVkiHUGgaLNZF0Nohn+MYoBIWEUMM\n/GmcmeXLhzkTMpuuzzhfbTt8CDPpLnnYtMko1Zs22c9PXT02+d615S5m4d7XquGrj9XlaejI8tm0\nU1kXk6Lfph/VHZtkW465soiPmbZKWHAhKsZA0zV712vX+S2/q1YB27fbdaQvo60/R7YO+vg8dPUN\nCcGnrG0e0ph299wDXHGFiWVn83lW+T41qeciHw+b0cZ9+wH19c2p6/915fO1GSXfLop2S8/OAsuS\n0cV1f8o+h5UrjTN3LBt0mrTh7HMGgFNPNeUsajO25ZiN+/lqp7H4o1IJs0zbgcFlzJt85z36aOC6\n64Y7cqNJ5y2KU9Un7EXXDh+CM2fbPPjcEdp1a7vNOvZZflsKYFX/rytfCBOHst3SQLWy4CL9VH74\nrpM2NGnD+ee8Zk15fVZttPHl5O6jnW7eDJx+uqnX5cvDDslCJcwyIYVKKOq8a9YMo2g07bz5+up6\nTmdKnw7vUiF2kQefg3DXtG3Wsa14d00DSWavHaKf15UvhIlD1W7pKmXBRfoxxLnLt6Mmbbjtcy6y\nVPaZMPTts0O304UF4LTTjEUUMOGnQraIUgmzTAiz05Syxj9UY2zSefP1ddJJZmmta/2FMDC5JivI\np3C0U10eugSwbTMwFc2qh+jnTerY98QhXw8bNxpFyGfgaZchRFy4SjTpR33KZHPC0PVYoSHb6fz8\nkjUWMBODoC2ibRzIhn7F6pgfs7OwD/L1xforp4+T6xjrtWt9tDk4fdkyvS+MzMzM0rVjrM8u+K4H\nV+kXheOpamt1+fC1+cfmDsemGwR8kuZzZsb0XReblqoAHfP943t22pRQHBfz9RVL/fmg66zWtxO7\nK7rWR1NLVtWsOoR2GkIf9l0PrtJv4yrRpH/5WiWxZbXO1kefY4VcE4KVvg1UwibKWAflsdNVkIfk\nq2iTtD4WF40v0ooVzX7XVFCvXm2WIBcXjc/T2WeHU2/sw25p4yrRpH9VtTnXyrQNRTVbH32OFRoC\n3xODNlAJGxlNO3MIg3IIs/jY6DrLW7HCKBGqYQrNrszNGT+k1Gfrj/4IuPbaZk7hTQS1i1m1rXbf\npA+zjxm61EPRsz/yyOL7NJ0cFbW5rsq0q2dbdt98fQBhta1o23qbtcuhX7H6hPmizTExRf4OviNv\n+/Yt6Uuo+U/rWsT4SbzqVb5zZJesr016+kRofiopZf45XdpOna9PbEEy+wYHrrrvEPXQNX9dfMVc\nlSm0NtPlxATf+QZ9wqZLfmZcdcBzdlaTDbI41LJGm7w2xedMqGg2C9iL19Mk/bIZ6vy8WU5L1ZS3\nvx34rd9yV0dDP4fUCrFz51IZu1p3Xee9yHoFdLOE1FnpfFm7u9RhnTWoz9LrUPXQdQmsiRVtqBAp\nIayQpLR55iHluy1UwhKiNWVmyHdmoLphpkJjw4bhG3DbvNbh2z+miVIJuMlj/mSErK9GGk5hZmbJ\nmXb3bnfP2MdzyIaq2LKlu5/KEHkvGnD7DCBVA79LR/Ayedm1DuvqoE8dDekQb2vZM3/PfJ22KVOb\nPNmoqyGX21NCCg3VFiphaCc4+saJce182eWYGB8NuGtey/A9E2qiVBZ9Znv2WrRraf164Jxz9o51\ntXq1m/bo6zmkykifYMRD5L1swHXR/1ztEquSl13rsE4G9ZFRLnfL5S3QXZX4KmW6qE7Xr29Wpi4n\nuPSpK5sTmTbPPLYdkXvRZu1y6NdQPmFtYgb1idEU2mG7Xa5z9fu+9wjBJyDN/6ZNxQcJD+HHURW/\nJ1u/U/EpyRPigdh1+QqNInmZbfuu4tgVfW/7N22eQ769NDm03IXvXxVDxyaznV5M/SIFLX3CvCta\nVa+hlLCmjbxPA/MVqG8IqupvyE4UQofNK0Tr1g1TH3kFqy4Nl+0xhOdQRJN+HmreQ6IuiGnbTT6u\nHO7LNv9Uyao2yk6+D61b526jROibEnykF2pfbauEcTkS7WIGdTWJx7xmXUfZEsTQ/kFNHGO7HrvR\nlGxdAOYAY9dO+cC+ZR/C96NpXkKhyVJZqHkPiby8zNfrjh1muawJLh3ui74Hyn/Tdik134fqzuZ1\n5ftX97shl+mGSs+3D7BNqIQlNGnkfRpY1GvWNRTFoFpYAM480+zK27MnjB0rdQ7sNvJWptyEJjTG\n3B7LaKt4Dr1Zp2l6IWwiysvLqnqtyq9Lh/uy78t+07Z9lPWh0JzHh55YDJGebx9gq7Qxmw39Ypyw\n8EnNz9lzurKfpeftheAflF0+EDGvoZbjxrwc7Yuu/jUhxh1qml6oPndl9dplydBGulXf2/IJ60Ko\nS2ixEWo/UOVyJBmYdEayZ4+xLF17rTljLbWAzcwAxx1nrGK+Zyplx27MzgK33mpm7DbyWDQTHPNy\ntA+6WhazS0/Z93mGnGm3sRr3zVdXK1rd78qsH3X57WuRrbO6FH1f9RvXVhwud9thTJZ8KmGBEMIS\nQxfyis2WLcC99y4pYMuXh6GAAcVhMdLYUu95jwmRUTeYd10yGpPQCIGuykhT5W0opTnNT3bSUpVe\nn3w1DSjc5Hc2lgyzfaSpDxkJn6HCOI1GoW1jNhv6FepyZJet0nX3C9W02oS07Nkt2jMzqscfH35Z\n2iwTxr5kNCa61nHb5+166Sibn6Z9pmu+2u7mK/td26X0MnnJPjI++u4AHUObAJcjm2PreA2gn9N1\n7E6G6YxkYWHvgKs2LGCuLYRtLAtNn1PszzMGuloW2waAdP3c8vlp0mfy+SrqI0WfdT2loq9VsKge\n2UfCw4as7fNc634bcqD0PkxWCbN5vAbQT6CMxV/I9pKbqx2F+Q7ZNM9Nn9NYnmfodFGSQlsW7puf\nNpPCouX4JqdUuKizql3EoTwbV1SV0Vf5bclaV2Gc+uQvtJ3peSarhHXV2Ntue25CaANDH2xaD6qe\nUR8H47IBquz6tgrbmJ7nGAnNl6RPftpOCvNpNW2nLurslFPM3zVrlizpIQ+WNqgqY9vy21TYbFkm\n+8i+qt+6tLD5ZrJKWFeNvayh9B10uwi5sc8aXcTcatMh2ypsWYYY6Oue/9jbRx/a1E3I9dh3Uuiq\nndZZe7L9as0a83nog2UX8vWQL+PWreVBb7vIpjZ5yWLTet+mTRVtYLK9JB78ykQbB7KhX64d82OO\n2VJ2JEes5SnDdsytNs6fIcf26hN/aYztpA1t2kAMzsK2NwrZyE+VbCrrVzHUdRvqjk3Kn/Pa5tzN\ntrIppCO70nSy5S064s1m/obsD6BjfnNCW5ZoQ9GMKuvfMRZTftEz6jOzceED1oW+1pW6WXPZ91NY\n8qmjjcUhButMUR/xKdvqZNPGjcX9amzL+EVtZ/36pTLeeqsJjZM97smmbMrKmCbteCjrfSp/RExY\nlj17TL42baoOE9QnfyGP9ZNWwvrge4mi606ntnTdQeqybvoK6zZLii4GhSaKUL4O8+/rhHDZ9zEo\nFa5po1zbVsR9y40hqJNNVcpGyINlW8raTlrG/G7y7JJcHXWyKS9jyhTfocnKn5kZE1vS2PPMa5Iy\nqY3ZbOhXyHHCQjCbZ02sLvLUZckzlLoJmaKlhKpnWbZM0fXIFhfPp6u539eyWZt0beXRZ98Yup5d\ny6ZY6NJHbVAnY3xRJNvWrdt7WTb29oGWy5HeFa2qV6hKmG9fobLOZLuTdQnu6Ltu+jKEoKpTsrJB\nb2dnTQBPm3VqWwFpE8Q2e6+pDc6++kZZPQ85KDdtR76VhLEQct8a2odx6HbVVgnjcmQHfO62qFrK\nsm3K77LkGfxOlAqG8pfKLyXklwiBvevwpJOAK66wV6dN20nT+miyxFl0r1CXRl0tGdb1DVfpFtUz\nMExbb3o0EX0V7RKyf92QPowxtCsqYR3w2cCHHLjy5QTqgzuG3PnrGLpus/fODs5r1phXtg6PPHL4\nOm1aH00U76J7haiwb94MnH66yefy5XaFdlXfcDlYFNXzEG29TZlCVcht4cMXMC9jpuCPmCeGdkUl\nrCO+HEhdD1x1cVuaBisNraE3wZdSUDY4+3ZYblofTRTvonsNqbA3GYAWFoDTTgN27TLvFxftC+2y\n59hlsGg6qJbVs+u23qZM2fYxO2t2Di4sxClH8oRgjQkhDz4IcaK3D23WLod+heoT1hRXa9Eu7xuq\nH8FQTNkvxbWvhk8n/KYHVc/M6H17tfbbb7hyt+17Nvqqa6fxLmUam5O2ahh+siHkwSVVbTV0nzDv\nilbVK1QlzIVQdUnTRuiyo05ZuRmKvsEMQ2mvtmnartM6mJlRXbbMbJjoS5t6bfP8XA+qttpDqljV\nBeJMaVKu2GRJCH0rm4cmgVGHzFffZxlC/WahEuaYNrPqEGYebQcBV+ELQhAAIQlv23np++xCaa91\ndKk3V4pQE1zVq+uBx1a+bVv4Qhtw0zzVKZohyJ7QLI1Fu8S71FFosotKmGPazqp9Kx5djrewLSyy\neQBURYavk5CEt4u89BVEIdVPGX3yGPpSaNd7u9zWb+NYtC7tsiqd0AbcbduMLEtl2/LlYfadlJDq\nL5uXmRmz/N+1b4cku6iEOabtrNr3zCOEBprmQWRJWKXK2FB5si18mg5IRde5EIRD+Ai5pi79kAaQ\nNviu165k8921fdmWP2X381XHZ521t1wTCbtdhjAeFOVl2bIlf8yuk8hQ+tigShiAgwB8EsBXkr8H\nlly3G8AXk9dFTe8fohKmGpbvRpN8hdBAswppVmgNNZjaFD5N71U1YLiMWt/VrO+TJnUS0gAyNlwq\nwC6W3kMJ+hubJUw1jPEgn5e6w8tDynMdQythfwPgL5L//wLAW0qu+0mX+4eqhLVhKAHRN52hGrlP\n66CtMjYdkKquc1XfsSoqbZb5QxLGoeWnKW2tXCG3K5v+a12eZSrTQnF2j5UqI0Koba+IoZWwmwEc\nkvx/CICbS66brBKmWjxzc+l31WWmOnQjj3XwUu1vCXNJzEt2MQla1TjzrLpvvvPHZMWiAKfYWoof\niyVmbMQm09oqYX2DtT5MVe9M/v8WgIeVXHc/EdkOYBeAN6vqv5bdUETWAlgLACtXruyZvTDIBmh0\nFTSvT1A6H1GFYw3oCjQPMurj9IAoghMWENpJC00CocYQjRvYtyx1x2RVBeUNsXw22k7Vs5xqoNNQ\n6CPTYjgloFYJE5FLATy84KvXZN+oqoqIltzm51X1DhF5FIBPich1qvrVogtVdTOAzQCwatWqsvtF\nS9Nz9to2nD6CKJaBO6QO1XRAGnrgCk2ZaUMog3zTQTf0frOwAGzdCmzZYk4BSMuSz3fRMVmx0bft\nVD3LWJTtsdJVpsWiPNcqYap6XNl3IvJtETlEVe8UkUMAfKfkHnckf78mIvMAjgJQqISNnSaH+HZt\nOF0FUQwDt80OFZIy54IQlBnXdezy/k0H3dCOXcpff+yxwM6dxmUcWCrL+vX1x2SNmaK6rHqWoSjb\nY5dbVXSRadEoz23WLvMvAG/F3o75f1NwzYEAlif/Hwyzk/JxTe4/Fp+wPFX+BbGtfw+Fr+CRvonR\nF8X2TtR8+V0/wz73d/G8uuSnKDZfDO3dNX1CbfgO3xKT3AoBX3WGgX3C3gzgwyLycgD/BeCFACAi\nqwCsU9VXAHgsgE0isgfADIxP2I09042aKq0+lFlXaNiql66zI1ez0Kr7xmJOz7N165IFZudO875L\nvsvK73qG62L5o+g5N21TTcubvV/+QOyXvcwsOdqqp1itMl3bjm/rsg+rTqzPOCWGFR4ADNYaIr5n\nXUPTtLw26qXL7MhlXK+qYzvOOmspgOHMTPhW0XSr/n77LVlg+sROKrN+hmoVaJPfNmXoGkKCYVD2\nJda8D53vWOspBDCwJYyU0GcW4XvWNSRtrD026qXL7MjVLDR738VF4PTTgT17luphxQrzHjB/V6zo\nn6YrinyQUnbt6lZnZdbPUGe4Zfktaj9A8zbVpLxFaaxf76ZuovG1KSDUtlPH0PnuYn2NpS5Dg0qY\nA2JdRvJBDOExXC0RZ+8rYupgz569B+qZGfPZzAywY4eddF2QPse8AjYz073OYhswy/KbPufFRfOc\nV6wAjjyyXZuqa7NDujEMlZarAT7WSe6Q+W7yjDnO2YFKmEVSoXHrrXYUiynMMmLwgXOlDGTvu2IF\ncMYZ+9bD8uVh101KkQ/SUUcZxbFPnRUNPCEL/6L8zs0BGzcaS+fu3eY5X3aZ3TY1pMI6RFohP+Mp\n0NX6ymfUHiphlsgKjdlZYFlSs10Hz6kIoVisHa5modn7HnnkvvUQQ90A3Z5j10lGjMJ/xw5j0cxa\nOrsuF5bVW5M2amti59oqE+MzHhshWV/HDJUwS2SFBgCceiqwcqWbCM5jI9blAduUWVFiqZs2ee0z\nyYhR+NvK8+bNSxa15cvb1VtME7sYn/HUiGUCHTpUwixRFIW67Vb0qvtRCJEx0WeSEaPwt5HnhQXg\ntNPMRgfA+Ji1qbeYJnYxPmNbxOSGEtMkMVSohFmiTGh0nX1OWQiNkZgE6xD0nWTEKPz75nl+fmm3\nLGDcHtrUW2wTuxifcV9islbaYuqykUqYRYqERt8Z/xQb5ZAMIQCmKFjr4CSjPatXmyXIxUWz6/Ts\ns9vVWwh1PvUBt46YrJU2sC0bY2xfVMIcE9vsc0oMpRxNTbA2pWqSEaMwdY0NJcrnxI6TkXqmNl7Y\nlI2xti8qYY5xOfvkQNWPoZSjqQnWvsQqTIcgZus4JyP1hGCtzOJ6jLEpG2NtX1TCBsCF4BxqoEo7\n4YoV/WM+hcZQylFogjU08oI+VmEaO0MPuCtWABs2sE/kCUXRHmKMsSkbY53sUgnLEJNlaYiBKu2E\ni4tLUdvrtsXHVId9BEC+nHXlDkWwhkaRoI9VmMbM0ANuPjgxrZ3hMdRkyJZsjHWySyUsIbYlkCEG\nqrQTZs8vrDtHLDYnyy4CIF/OjRs5oHSlSNCvXx+nMI2ZrgNu2z6a9rcNG4ZJj3QnRstljJNdKmEJ\nsS2BDKH1Z8+8Sy1hVQrfVJws8+W88MK42k5IlE0mYhSmbQlJoegyqRs64G7IMsEnLs/YpOXSPVTC\nEmJcAnE9UOU7YZ1P2FScLPPlPOkk4Ioryssd0mAbGrEtIdh6liEqFKecYv5mA01X0Tf8TtvnHrJM\n8IXrdtTXcknqoRKWENtgMBRtFL2YnSzbDK5F5Sw69zG9b2iDbWjEYvWy+SzbKBSulfh8udasafa7\noQPuxjhRdk1fxbRp22Ldu4NKWIZYBoOQidHJssvgmi9nWbnHMnunNc/us6wa1LJ1DbhX4ovKlX5e\n9byHnrhyorwvfZSjNnKvad1TTrSHStjECbnTDKUUu1SUxjCDpDXPYPtZFi3/5ev6lFPcK/FFDtht\nBuch2wInynvTRzFtK/fq6t6FnAh5fLIFlbAJw8HV4FJRGsPsfSzWvL7YepZVy3/5ugbcK/H5cnW1\njI2BGAf9roqpbblnW05MZXyiEjZhOLgaXCtKsc/ex2DNs4WNZ1nV7/J1vWaNeblWDPLl6moZi5mp\nDPoptuVe6EpdqFAJmzAcXJeITVEacsY+BmteSFT1u7K6HnrJr84yNrY2sLAAnHnmUjiesZYzj025\nF7JSF7KFU1TVdx5KWbVqlW7fvt13NkZNn8bpsmGH3Gl8E/OMnW3GEFteY21vTehyMggZBhv9ZOj2\nKyJXq+qqptfTEjZxus6EXDbssQv9vsRqmWCbWSImy+vYLaFpf0oVsOOOM1axsZUzRlwv/4fAjO8M\nkDgpc94N/d5jIDXTz87GtYw81jazsGCCWS4sDJdmH7rkd27OHCcV0uBli2x/Wr58GgpYbG22D6HL\nS1rCSCdc+pPRV62aWC0TY2wzRRY4INxnE5vFcAhc9KeQl5un1gZCl5dUwkgnXDbstvcOWeC5Iqbl\nrJSQ2owt8ha4rVuBCy4Id4ALfWnGFzb7U+hKzhTbQMjykkqYB+qUhliUCpcNu+m9Qxd4ZG9CaDM2\nyVvggLAHOFqZ3RO6kuOzDcQytg0JlbCBqVMaqFS0I3SBR8ZN3gIH7G0J86XklA12oS/NjIHQFV1f\nbYBjWzFUwnK41tTrlIYyB+Op+Cu0JXSBR9oTW/vMW+B8Kzl1g13ISzNjIAZF10cb4IS5GCphGYbQ\n1OuUhrbnuLUdsMY2G4lB4JHm9G2fIShwvpUcDnb+8d0GQoQT5mKohGUYQnjVKQ1tolV3GbDGKKAp\n8MZDn/Y5tglGVzjYkRDhhLkYKmEZhhJedUpD/vuyPHUZsCigScj0aZ9jnGB0gYMdCRVXE+YQLOBd\noRKWIUThVZWnLgNWiGUkJKVP+8z2h9lZ4NZbjXCeYhsP0Toc80BJwiV2CzjPjowcCjZCllhYMLG6\ntmwBdu2KUyj3IVR5EPJAGWqdkWZs2AC89rXGAj47C7zxjeZ0B1/w7MiJkQqNdBclhQiZMnNzpi/s\n2jW9ZcmQFZ1Ql4pDPa9yJgAAIABJREFUrjPSjNhdbKiERQ6FSDc4+x0vsQvlrgyh6HTtNzaeiYs+\nG6pySJoTu4sNlbDIiUWIhKT0UHEN63nYpkooj7ncrpXPPv2m70Dpqs9OVWEfGyH6QDaFSljkxCBE\nQlN6YlFcXRHa83BBkVAee7ldWwT69ps+A6WrPhu7FWVqjHESRSUscmIQIqEpPTEori4J7XkMxRTK\n7dIi4LPfuEw7ZivKWClStsY6ieqlhInI7wI4E8BjATxJVQu3MorICQDeAWAWwLmq+uY+6ZK9CV2I\nhKb0xKC4uiS05zEUUy23LXz2m6n32SlRpmw1nUTFZi3rawm7HsDvANhUdoGIzAI4B8CzAdwO4CoR\nuUhVb+yZNomEEAVo6IqrS0J8HkMw1XLbxGe/sZl2bAP1lChTtppMomK0lvVSwlT1JgAQkarLngTg\nFlX9WnLtBwE8HwCVsAkxZaUnRKb6PKZabrJEjAP1lChTtppMomJ0ORjCJ+xQALdl3t8O4MllF4vI\nWgBrAWDlypVuc0YIIWRSxDhQT4kqZatuEhWjy0GtEiYilwJ4eMFXr1HVj9rOkKpuBrAZMBHzbd+/\nKTRX+4H1TghxSYwD9dToarGO0eWgVglT1eN6pnEHgEdk3h+WfBYsNFf7gfVOCHFNjAM1aU5sLgcz\nA6RxFYAjROSRIrI/gJMBXDRAup0pMlcPxcKCOQtrYWG4NEPBZ70TEhJTlgNDMDdnzheMabAm46Rv\niIrfBvBOAA8B8G8i8kVV/XUR+TmYUBQnquouETkdwCUwISrOU9UbeufcIb7M1VO3BLWtdy5dkjEy\ndTlAyJTouzvyIwA+UvD5NwGcmHl/MYCL+6Q1JL7M1VN3GG1T7xyoyFiZuhzIw8kWGTOMmF+Cj3Vl\nOow2r3cOVGSsUA4swckWGTtUwgKCDqPN4UBFxgrlwBKcbMVN1ooJsE0XIareokDUsmrVKt2+vfAk\nJEK4TEHICMkP3LSExUnWijk7C4gAu3aN/zmKyNWquqrp9bSE1cCBPlxi24pMyNRoKz+Llh+naBUc\nw7iTtWLu2WM+U6VFMw+VsAroj0AIId3oIj+Llh+nFkpiLONO1mUkbwmj+8gSVMIqoD8CIYR0o4v8\npK/neMadvG8jEL91zwVUwiqgQCBk3Ixh2SdUushPbkoY17iTdxmZ4vOsg475NVBIEzJOxrLsEzKU\nn91gvcULHfMtQ+dvQsbJWJZ9Qobysxust+kwxNmRhPSC5+gRF6TLPrOz8S/7EELihJYwEjRcMiKu\noP9R2HBJjkwBKmEkaLhkRFzCZZ8w4eSLTAUuRxKn9F1K5JIRIdOjaPJFyBihJYw4w8ZslktGhEyP\nWMI0cMmU9IVKGHGGraVELhkRMi1imHxxyZTYgEoYcUYss1kSP7RIjI/QJ1/0VyU2oBJGnBHDbHZs\nTFEZoUWC+ICTTGIDKmHEKaHPZsfEVJWRMifuqSmjZFg4ySQ2oBJGSED0sWRNdXkkb5FYsWKayigZ\nHk4ySV+ohBESCH0tWVNdHslbJKaqjBJC4oNKGCGB0Fd5mPLySN4iMUVllBASH1TCCAkEG5YsLo9M\nWxklhMQFlTBCAoHKgz2ojBJCYoBKGCEBQeWBEEKmA8+OJIQQQgjxAJUwQgghhBAPUAkjhBBCCPEA\nlTBCCCGEEA9QCSOEBMfCArBhg/lLCCFjhbsjCSFBMdUzMAkh04OWMEJIUJQdyE0IIWODShghJCjS\nkwNmZ3nsECGhQBcBN3A5khASFDw5gJCwoIuAO6iEEUKCgycHEBIORS4C7J924HIkIYQQQkqhi4A7\naAkjhBBCSCl0EXAHlTBCCCGEVEIXATdwOZIQQgghxAO9lDAR+V0RuUFE9ojIqorrviEi14nIF0Vk\ne580Y4XbewnxC/sgGTts4/HRdznyegC/A2BTg2ufqarf65lelHB7LyF+YR8kY4dtPE56WcJU9SZV\nvdlWZsYKI4AT4hf2QTJ22MbjZCifMAXwCRG5WkTWVl0oImtFZLuIbP/ud787UPbcwu29hPiFfZCM\nHbbxOBFVrb5A5FIADy/46jWq+tHkmnkAf6aqhf5eInKoqt4hIg8F8EkAf6Sqn6nL3KpVq3T79nG4\nkC0scHsvIT5hHyRjh23cPyJytaqW+sjvc32dEtYw0XlUKGG5a88E8BNV/du6a8ekhBFCCCFk3LRV\nwpwvR4rIASLygPR/AMfDOPQTQgghhEyWviEqfltEbgcwB+DfROSS5POfE5GLk8seBuBKEfkSgC8A\n+DdV/fc+6RJCCCGExE6vEBWq+hEAHyn4/JsATkz+/xqAJ/RJhxBCCCFkbDBiPiGEEEKIB6iEEUII\nISR6YjwxgAd4E0IIISRqYj0xgJYwQgghhERNrCcGUAkjhBBCSNTEemIAlyMJIYQQEjVzc2YJMrYT\nA6iEEUIIISR65ubiUb5SuBxJCCGEEOIBKmGEEEIIIR6gEkYIIYQQ4gEqYYQQQgghHqASRgghhBDi\nASphhBBCCCEeoBJGCCGEEOIBKmGEEEIIIR6gEkYIIYQQ4gEqYYQQQgghHqASRgghhBDiASphhBBC\nCCEeoBJGCCGEEOIBKmGEEEIIIR6gEkYIIYQQ4gEqYYQQQgghHqASRgghhBDiASphhBBCCCEeoBJG\nCCGEEOIBKmGEEEIIIR6gEkYIIYQQ4gEqYYQQQgghHqASRgghhBDiASphhBBCCCEeoBJGCCGEEOIB\nKmGEEEIIIR6gEkYIIYQQ4gEqYYQQQgghHqASRgghhBDiASphhBBCCCEeoBJGCCGEEOIBKmGEEEII\nIR7opYSJyFtF5D9E5Msi8hEReXDJdSeIyM0icouI/EWfNAkhhBBCxkBfS9gnATxeVX8FwH8CWJ+/\nQERmAZwD4DkAHgfgRSLyuJ7pEkIIIYRETS8lTFU/oaq7krefA3BYwWVPAnCLqn5NVe8B8EEAz++T\nLiGEEEJI7CyzeK+XAfhQweeHArgt8/52AE8uu4mIrAWwNnn7ExG52VoOizkYwPccpxEDrAcD68HA\nejCwHpZgXRhYDwbWgyFfDz/f5se1SpiIXArg4QVfvUZVP5pc8xoAuwC8v03iRajqZgCb+96nKSKy\nXVVXDZVeqLAeDKwHA+vBwHpYgnVhYD0YWA+GvvVQq4Sp6nE1GXgJgOcCOFZVteCSOwA8IvP+sOQz\nQgghhJDJ0nd35AkAXgXgeap6d8llVwE4QkQeKSL7AzgZwEV90iWEEEIIiZ2+uyPPBvAAAJ8UkS+K\nyLsBQER+TkQuBoDEcf90AJcAuAnAh1X1hp7p2mSwpc/AYT0YWA8G1oOB9bAE68LAejCwHgy96kGK\nVxAJIYQQQohLGDGfEEIIIcQDVMIIIYQQQjwwWSVsakcpich5IvIdEbk+89lBIvJJEflK8vfA5HMR\nkb9P6ubLInK0v5zbRUQeISKXi8iNInKDiPxJ8vmk6kJE7iciXxCRLyX18Prk80eKyOeT8n4o2UwD\nEVmevL8l+f5wn/m3jYjMisi1IvLx5P3k6kFEviEi1yX+vduTzybVLwBARB4sIv+cHMl3k4jMTa0e\nROSXknaQvn4kImdMrR4AQET+NJGR14vIBxLZaU0+TFIJk2kepXQ+gBNyn/0FgMtU9QgAlyXvAVMv\nRySvtQDeNVAeh2AXgFeq6uMAPAXAacmzn1pdLAJ4lqo+AcATAZwgIk8B8BYAb1fVRwP4AYCXJ9e/\nHMAPks/fnlw3Jv4EZuNQylTr4Zmq+sRM3KOp9QsAeAeAf1fVxwB4Aky7mFQ9qOrNSTt4IoBjANwN\n4COYWD2IyKEA/hjAKlV9PIBZmAgP9uSDqk7uBWAOwCWZ9+sBrPedrwHKfTiA6zPvbwZwSPL/IQBu\nTv7fBOBFRdeN7QXgowCePeW6APAzAK6BOcniewCWJZ/f109gdjfPJf8vS64T33m3VP7DYAaUZwH4\nOACZaD18A8DBuc8m1S8APAjA1/PPdGr1kCv78QA+O8V6wNKJPwcl/f3jAH7dpnyYpCUMxUcpHeop\nLz55mKremfz/LQAPS/6fRP0kpuKjAHweE6yLZAnuiwC+A+CTAL4K4Ie6dB5stqz31UPy/V0AVgyb\nY2dshIl3uCd5vwLTrAcF8AkRuVrM8XHA9PrFIwF8F8CWZHn6XBE5ANOrhywnA/hA8v+k6kFV7wDw\ntwBuBXAnTH+/Ghblw1SVMJJDjeo+mXglIvKzAC4EcIaq/ij73VTqQlV3q1luOAzAkwA8xnOWBkdE\nngvgO6p6te+8BMDTVPVomKWl00Tk17JfTqRfLANwNIB3qepRAH6KpSU3AJOpBwBA4uv0PAD/lP9u\nCvWQ+Lw9H0Y5/zkAB2Bft55eTFUJ41FKhm+LyCEAkPz9TvL5qOtHRPaDUcDer6r/knw8yboAAFX9\nIYDLYczqDxaR9DizbFnvq4fk+wcB2DFwVl3wqwCeJyLfAPBBmCXJd2B69ZDO+qGq34Hx/3kSptcv\nbgdwu6p+Pnn/zzBK2dTqIeU5AK5R1W8n76dWD8cB+LqqfldV7wXwLzAyw5p8mKoSxqOUDBcBOCX5\n/xQY/6j08zXJjpenALgrY4KOGhERAP8I4CZVfVvmq0nVhYg8REQenPx/fxi/uJtglLEXJJfl6yGt\nnxcA+FQyE44aVV2vqoep6uEwcuBTqvpiTKweROQAEXlA+j+MH9D1mFi/UNVvAbhNRH4p+ehYADdi\nYvWQ4UVYWooEplcPtwJ4ioj8TDJ2pO3Bnnzw7fjm0eHuRAD/CeMH8xrf+RmgvB+AWdO+F2a293KY\nterLAHwFwKUADkquFZjdo18FcB3MzhDvZbBUD0+DMaF/GcAXk9eJU6sLAL8C4NqkHq4H8FfJ548C\n8AUAt8AsQSxPPr9f8v6W5PtH+S6DgzpZDeDjU6yHpLxfSl43pDJxav0iKdsTAWxP+sa/AjhwovVw\nAIwV50GZz6ZYD68H8B+JnHwvgOU25QOPLSKEEEII8cBUlyMJIYQQQrxCJYwQQgghxANUwgghhBBC\nPEAljBBCCCHEA1TCCCGEEEI8QCWMkBwi8mgRURE512MerhSRXfVX3nf9siTPl+Y+/+vk86fZz+Ww\niMgJIrIgIj9MyvTPPe/nvW5E5A9E5Isi8uMkL3+bfH67iNziK19jIYS+3BcReUVShj/wnRdiHyph\nEZF0xPxrUUS+ISIXiMhjC36zuuR3e71yvzm/4Jq7ReRGEfk7EXlI7vorm6SReUUrEIkfROQXYGI2\n/TxMsN3XA/hwzW+CHrwS5e+9MAeo/wNMmT4xUNrvS+rmsCHSiwUROS7kNjM0mT4U/SQuVJbVX0IC\n5PWZ/x8Ec7zIGgAnicjTVPWLBb/5LwDnt0znozDBTAFzUOuJAP5Pks4xqpoex3AeTOC+LL8D4EiY\n40++nPvumpb5IN3ZCOB9MM8/Zp4NEyTxDFWtVL4i4rnJ3z9Q1S/kvnsGRn4uH2nMPwG4EsA3fWeE\n2IdKWISo6pn5z0TknQBOB3AGgJcU/OwbRb+r4V9V9fxMGvcD8DkAT0jSen2Sn/MK8vNoGCXsX1T1\nfS3TJZZQ1e8B+J7vfFjg55K/YxqISsukql8dOC8kUFT1LgB3+c4HcQOXI8dDuozxkMqreqCqOwG8\nP3n7P1ylkyIicyJyoYh8W0TuEZHbROTdkhwgm7v2ShHZJSL7i8iZIvI1EdkpIv8hIi/LXPe/ReR6\nEfnv5H6vE5HSfiAijxORi0Tk+yLyUxG5QkSOq7j+xSIyn/gt7UyWcF8t5ozSsuuvSa79TrKs/PCK\n+y9P8pyW72si8gYAZfffx+9JMv5jYs6QPFdEvpUsbV8vImtK7nU/EXmDiHw9m7aYc9X28UerQ0RO\nTurzR8nz+LKI/N9sXaXLQwBem3x0RWZZu3SJRESuBPCe5O17c8vh+yzBicjvichVybL7DhH5/4ra\nWXLtChF5S9K2dibP+pNV7SL3+1ckZfrD5KPb8nmTAp+w7PKqiJwoIp8WkbtE5N7MNc8QkY8nv19M\nnuuCiLw2+X5ZkvaLC9Ku9EETkd9Irnt97vNnZ+5xSO67C5PPV+Y+P15ELhGRHyR1eLOInCUiDyxI\nN+3by8X07f9MynZu5poHisjGpNw7ReQmETkD5jid3ojIE0XkQ2JcPxZF5LsicrWIvF1EZjPX3dff\nROSlYvz9/luMDDtXRB7WtnxSsqyethEx537+nYjcmvzuKyLyZyKyT9lFZEZE/jSpn0URuUNE/l5E\nHlDU5oh7aAkbD+kAsH2g9O6tv6Q7InIqgHcD+G+YQ1FvB/CLAE4F8FwRebKq3lHw0w8DOBrAvyd5\n/F0A/ygi98As2/4+gI/DLJ8+H8CZAH4C4O8K7vVoAAswS7KbABwK4IUALhGRF6rqhbk8XwCzLHwr\ngH+Gmb0+FcCbADxLRH5dVXdnrv9zAH8D4AcwS8V3AXgOgM8CuLugTgTAhQB+A+bstrNhlujWwlgn\n23JQUr67Yert/kn5LhCR3aqaKtwQo6h+BMAJMGeung2j+L0cxuLZChH5GwB/DuC7MMulP03K9WYA\nx4vICap6L4CvwVhcnwXg6QC2wNQvMn+LOA/A9wH8JvZdEv9R7to/Tq67CMA8gDmYg4ufICJHqeo9\nmXw/Eubw3p8H8BkAFwN4AMzS4idE5OWquqWm+NckZUqX7N+eyVM+b0X8Hkw7uRimjzwiydtzkzL8\nMPn7TZiz/h4LYB2ANwLYU5H292vS/QyAXTCHGL8u8/mxuf/fl+RnBsAzAdyiqvc9KxE5DcA7Yfrd\nP8G0gWcCWA/gN8W4VBRZfj4Cc67jJcn/dyb3ux+ATwE4Bqavvh/mvMczk/v2QkSOguknu2Hq9RsA\nHgjgCACnJfnenfvZn8MsoX8IwP8D8GswfeWZIvKkjCtHbflq2B9Glj0Upj3sBvDbAN4KIxvelLv+\n3TAy9Pbk/10AngczqaY+4APfh2Py1eogUU1eZ2ZebwNwBYxw/RiAB+R+szr5zTdyv8u+Ts795vzk\nNy/JfX5/mMFMAbyyJq/vS677gw7lfCyAewDcDOCQ3HfHwwiaf8p9fmWS3ucAPDDz+REwytgPYA6X\nPSTz3UEwA8+3AcxkPn90pq7fnEvnyTCCaweAn818/ork+g8DuF/uN29Mvjst89kvJPn6HoCVmc9n\nYRzQFcCu3H3WJJ9fieTA2OTzgwF8Pfnu0txv/jr5/GmZz5ZlyrcJwGzmuyOT+v1y7j4vTa6/HMD+\nmc8PhFEI90m74vk+PdMmH5rL18XJd6+qK0eDdNJnUtgGM/e8C8AvZz6X5DkqgN8paGd7APxu7vMD\nYQ4u/imAhzTMX9pHDiv47nYY5aWoPLsBPLvgNx9Nvv/lgu8Obpp2TZ63wfTNbNu/Knn9AMCWzOfH\npG0s89mjkt//EMAv5u69Obn+H0r69rUAVhTk6a+S7z+EvfvxLyTpKIBz25Qzd/93JPf4jYLvDgLM\nGcy5NrUTwBNy174zXx8Ny1fYjpM2ojBy//6Zzx8Oo1h/H3v37Wcm19+IvWXkcpiJn+bbHF/uX94z\nwFeLh7U0cBa9bgDw+wW/WV3zO4Xx/cr+5vz0cywpav8AY3lQAJ8G8DM1ee2jhKXC6tdLvv8YjAJz\nQOazVJA9o+D6zyTfrSn47r3Jd4dmPkuVsB3ZNArK9uLMZ9cBWMwKt8x3y2AGqG2Zz16X3OO1Bdcf\nATPQ55Wwy5PfPL3gN6mgbqOE/RiZwTTz/WeT9LOCfT75zVMLrj+lKO2K57sluf5lBd89Nkn7P+vK\n0SCdpkrYmQXfPRs5JRxLSsUHSu53UvL92ob566qE/VPJ/VIl7FF90q75XTqhODF5/2AYpfBNMNab\nWzPXviq59oUF7f4NBfdeAWMd+wmA/TKfp317HyUo+f7rMBOjR1Y8YxtK2LMaXJumt6nguwNhlKOf\ntixfnRJ2eMFv3p9895jMZ+cnnxWNE88AlTAvL5ofI0RV71vrF5EDAPwyzDLO+0Xkl1X1NQU/+7Sq\nrm6Z1POTV5ZPwggLl8uRc8nfZ4rIXMH3B8MoEo8G8KXcd1cXXP/Niu/SJc3DMv/fdy9V/WnBb+Zh\nfGqOgqnzBwB4PIxF7f8UuGIAZmacDSFydPL30/kLVfUrIvJNmBltlqNhBpttJXlqy82q+pOCz2+D\nWUZ9MMxyMGDKugvG0pjnypbppmX/VP4LVb1JRO4EcISI/GxJ/mxTtIR/W/L3wMxnaVs8UETOLPhN\n6u+zT6gYy+R3Uqa8H2ZpabuIfAhGaf+sFi/bd+VTAP4SZtnxYphJ3gyAywB8C8BvicgRqvoVmCVk\nTfKRUvXsd4jIl2Da3i/CTCyz7FNuETkQwOEAvq6qXy/I7zyAInnYhg/CbET6mJjYdJfCTKiqNk8U\n9esfiMiXAfwqgF8CcH3ukrLnWsUOVf1GwedF7feo5G9Rf90GM/khA0MlLHISJeELIvI7MDOjV4nI\nu1X1tpqfNuGlqnp+4nj6KJhZ8O8BeBfM7MwVK5K//7fmup/Nvd9dMminQU+L/EzS7/Yr+O7bJel+\nK/n7oOTvQcnfh2FvX5mytLK/rUrjPiUs8Qd7IIBvacavrCBPbfhhyedpPmcL0i4S1GVlKCMte5nP\ny50wOwcfBGMVcU1RPexVBwlpu/z15FVGvl3apvBZq+qHReS/YcLIvALGDwwichWA9ap6mYW0t8Eo\n5qkf2LEwE4xtmXwdKyL/BbPsfJ2qfjfz+ybPHjATgCy7c/fJ36+ur3ZGVRdE5NcAvBrGZ3INAIjI\nf8BYUT9U8LOmsiOlrHx1NOrDuTT3yZuq3isiP+iQPukJd0eOBFX9IYwP1TIszTZt3Xt3MrP9fQCf\nB/ByEXmezTRypMrSAaoqFa/POswDsGTZyJMqR3fl/l5Vk9+sopf+pi4NAICqKsxSxsHZ3Vhl19sk\nSfvHSdpFMqOsDGWkZS/L8yG560Ihzc9pNc/5VMf50NIvVD+mqs+EUWKOg1lK+xUAHxeRX+qdsOoi\njML1K2KCNh8LY23bqao3wigZxwF4CkwQ2rzFy/azb9WPuqKqn1XV34Cp16fBLL8eAuADIrK64CdN\nZceQpBswinZo7oe9rWZkIKiEjYu0Ezl5rokV5E+St28pUQZskC55Pd3R/ZtyTLLcm2d18vdaYC8F\n+EgRyc/gy0gD1j4j/4WIHIGlGFL53yyDWa4py5Mrrk3SfkrBd22jaV+b/F2d/yJRFA4B8BULS5Gp\nxdBWOw2lXdaiqj9V1ctU9QwAbwFwP5idrSl96uYymM0LL4ZZes1a2D4F4wB+XObaLFXP/iAYhfFu\nmP5Ui6r+AGaDx0oRObzgkn3S6YOqLiYK2V8C+FOYesi7bADF/fpAtCyfZdK6L+qvTwX1AS+w0keC\niPwWgEfCOKwX+QxZQVU/DxPi4TFIzPIOeCeMOf0dYoK+7oWYWGBDHKNxEIz/SzbtJwM4GcbR/qOZ\nr94GM9D9o4jklxogIgclW91T3gdTxj+RTAylRLF9K4rjG21J/p4lIsszvzkY/f1e6tia/H1TMmtO\n0z4QuTpqQBrc97Uiki7xQUSWwYQKEZijifqShgFYWXlVQ1T1czChCl4oIqcUXSMiT0iex+CIiRFW\npFSllo9s2JM+dZNat9Ynf/NK2EEA/ieMoveZ3G/fi6V2/8jcd2+CWcrd2tLndAuMMvmWrKVWzFFX\np7e4TyEi8tQkDEaeonpNOUVE8mFj3gATzuT9jn1qy0j78F9KJh5bIkvO8pAfAvqERUnOKfgAAI+D\niRsEAK9W1SJ/hMNLnIlTNiYWnSb8FUxMp9eJyPs1E0fJBqp6g4i8AibY5o0i8v9gwiAshxk0ng7j\nbP94m+kW8GkA/1tEngqzY/BQGJ84gdkBd5+lRlU3i8gxMDG7niEin4DZTXoQjD/d05PynJ5c/1UR\neQ2MleKLIvJhLMUJOwDGaTfv4P1eGJ+U3wBwnYhcBFMnL4Bx6j3cdgVk2AJT9mcnaX8MJkZRmvaj\n0dCxV1U/IyJvg/FduiFxdr4bplyPg6n3t1nI8zYYf6VXishDseQL8w5V/XHHe54Mo3ScLyYY6Bdg\n/HIOg4nx9DiYmEs+Tik4B8DDxASq/QaMsrMKxhr0dex91uZlMJac80TkQhjfu++r6j80SGc7TFt9\naPI3u+ElVcgeCuBzqrpX3LOk3b8SZpk0bfffg7GePRkmfMJ6tOOtMNaoF8Js6PgETL97IUxb6us6\n8WoATxeRK2Dq8acwsuc5MGEg3lPwm0sAbEvK9y2YOGFPhYl79+qe+emEql4mIucBeBlMv7sQpo08\nH+YZfBt0zh8e39sz+Wr+QnF4iV0wzqwfRXHsoNUlv8u/Ds/85vzks5dU5OXC5Jo/Kvm+c4iKzD2e\nAOACmHMPF2EE3vUwGwNW5669ErmQDgV5KQoFUBTCIQ1RcS7MztOLYCxfd8PEZDuuIs/PA/BvMAEo\n74URwJ+H2dTwSwXXvxhmmWAngO/AzFYPKSsPjNJ1JsxgsJj8fQOM/03bEBWFISXK6gsmTtxfwwzw\n2bRXJtf/c8vn+2IY5fbHSfmvhxmAlxdc2zpERfK7E2GWEX+SaeuH1d0z2wYKvnsgjPXvmuS+d8MM\nrh+HCYRZGb6lYbusClFRFnLjRTA7+b6S5OtHSZ2+Ebk4Ycn1fw7gP5Jn2So8AZbCYXy04LuvJt+9\nqeL3J8DstP5hkv5XYHZ4P6jg2tK+nbnmQTCK3TeT+90Ec4TbEWXPsUVZT4CRiTfBKJ0/SertHcjE\n+Mu3KRhl50swGxm+A2PdfVjb8pU996I2UtdfYFa/XgmzHLoIsyP8nUn93Q1ge9d64qvbS5IHQwgh\nnRCRNHr7X6vqa+uuJ2SsiMhfw7gGPF1V24Zu8YaIPBbGCvk+Vf3DuuuJPegTRghphIjss1kg8X/a\nkLz9yLA5IoS0QUQeLrlAhsnmo7cnb9mHB4Y+YYSQpvy9iDwOxjn9uzBnFj4HZlfuOap6TdWPCSHe\n+TMALxCRT8O/6+yYAAAAeUlEQVS4sTwcZifroTDL6VTCBoZKGCGkKRfCnFbwmzDxklI/rnNV9byq\nHxJCguATMJsKjofZvLALxj/s7QD+XumfNDj0CSOEEEII8QB9wgghhBBCPEAljBBCCCHEA1TCCCGE\nEEI8QCWMEEIIIcQDVMIIIYQQQjzw/wN0KGhwPKdVgwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dr-cOrNkC2M0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "75ba5ab4-dc9e-4b23-904c-a27d6c346095"
      },
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(spring1,spring2, 'b.')\n",
        "plt.xlabel(\"BERT embedding of the first word 'spring' vs the second word 'spring' \",  fontsize = 20)\n",
        "plt.ylim(-2,2)\n",
        "plt.xlim(-2,2)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SWTXMlQlA8fF3dqT77/cfP33af+7hh33V98wZ6Z57qvdXd7u+ohs2NHnrW30b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\nZV2/v8yxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSnBupfHC8f4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "a71bee17-0ad7-4e86-9fd2-295fc434b47f"
      },
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(spring1,spring3, 'b.')\n",
        "plt.xlabel(\"BERT embedding of the first word 'spring' vs third word 'spring' \",  fontsize = 20)\n",
        "plt.ylim(-2,2)\n",
        "plt.xlim(-2,2)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bR9O9teM9Zffty6+2TitDvGr20ks3V2Fee213M6mEysyokmRpafM+hL2Wq5Yr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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUp7TElYDBE5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "84539a55-5914-4ef7-b3e6-6995ea5a3593"
      },
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(spring1,because, 'b.')\n",
        "plt.xlabel(\"BERT embedding of the first word 'spring' vs the word 'because' \",  fontsize = 20)\n",
        "plt.ylim(-2,2)\n",
        "plt.xlim(-2,2)\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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foIUuHStSspTWa950Yhof14/xjj5mLnNgfUegvNLyu0ggE+rHiWs9RXrlt7kk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\nsAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkI-T3ZTDFdy",
        "colab_type": "text"
      },
      "source": [
        "### Distance between \n",
        "- spring1 and spring2 $\\approx 0.14$\n",
        "- spring1 and because $\\approx 0.59$\n",
        "- spring1 and spring3 $\\approx 0.51$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCdjDHhmDGMK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "48aa6f2f-94e9-4472-9721-af5f883540a1"
      },
      "source": [
        "print(distance(spring1, spring2).tolist())\n",
        "print(distance(spring1, because).tolist())\n",
        "print(distance(spring1, spring3).tolist())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.1410624384880066\n",
            "0.5851813554763794\n",
            "0.5119153261184692\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXrM3Q8vEH7O",
        "colab_type": "text"
      },
      "source": [
        "# SYNSETS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6A56WTMxEIqO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import ipywidgets as widgets\n",
        "from ipywidgets import interact"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6su4gbkuFX26",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filename  = './sample_data/spring_synsets.csv'\n",
        "df_spring = pd.read_csv(filename, encoding='utf-8')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFg0s2aTF0LP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "meanings = df_spring.meaning.unique()\n",
        "synsets = df_spring.synset.unique()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOuueNPtF2pK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.set_option('display.max_colwidth', -1) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7727HwcF85P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298,
          "referenced_widgets": [
            "f1316683594640e0bdaeadd3f75521b2",
            "721ee9d8604143e5b0b6bc66e1aadf02",
            "95942a0e5c0842409ae179a4bc6c89f5",
            "0b3e839969d349b88891ebf1425d34d2",
            "fd9ce30e9a7b4fe9bc6d0e4eadfa7cc3",
            "3cad3164556743c6b4981315d2b8a3a5"
          ]
        },
        "outputId": "2d2b4c63-b689-408f-a538-e0153b0b2aa4"
      },
      "source": [
        "@interact\n",
        "def view_synsets(meaning = meanings):\n",
        "    return df_spring[df_spring.meaning==meaning] "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1316683594640e0bdaeadd3f75521b2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(Dropdown(description='meaning', options=('season, n', 'season, a', 'water source', 'meta…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NGsZ8NwGApb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "cf88c329-fd07-44c9-c10d-a68effe3a149"
      },
      "source": [
        "counts= df_spring.synset.value_counts()\n",
        "df_synsets = pd.DataFrame(list(zip(synsets,meanings,counts)),\n",
        "             columns = ['synset', 'meaning', 'counts'])\n",
        "df_synsets.style.set_table_styles(styles)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "    #T_821ad37c_4221_11ea_b6dc_0242ac1c0002 th {\n",
              "          font-size: 20px;\n",
              "          text-align: center;\n",
              "          font-weight: bold;\n",
              "          color: #ffffff;\n",
              "          background-color: #000000;\n",
              "    }    #T_821ad37c_4221_11ea_b6dc_0242ac1c0002 td {\n",
              "          font-size: 20px;\n",
              "          text-align: center;\n",
              "    }</style><table id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >synset</th>        <th class=\"col_heading level0 col1\" >meaning</th>        <th class=\"col_heading level0 col2\" >counts</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row0_col1\" class=\"data row0 col1\" >season, n</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row0_col2\" class=\"data row0 col2\" >20</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row1_col1\" class=\"data row1 col1\" >season, a</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row1_col2\" class=\"data row1 col2\" >16</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row2_col1\" class=\"data row2 col1\" >water source</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row2_col2\" class=\"data row2 col2\" >12</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row3_col1\" class=\"data row3 col1\" >metal thingy</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row3_col2\" class=\"data row3 col2\" >10</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row4_col1\" class=\"data row4 col1\" >jump,v</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row4_col2\" class=\"data row4 col2\" >10</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row5_col1\" class=\"data row5 col1\" >political</td>\n",
              "                        <td id=\"T_821ad37c_4221_11ea_b6dc_0242ac1c0002row5_col2\" class=\"data row5 col2\" >7</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3e67ee7eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lN3jXOxlZJH",
        "colab_type": "text"
      },
      "source": [
        "## Words *spring* with the same meaning should have their embedding vectors closer to each other than to words with a different meaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uvVyxsxGbTA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "spring_embeddings = [bert_embedding(text, model, tokenizer, verbose=False) for text in df_spring.text.to_list()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aBRTQ0tGhte",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#to find the index of the key_word in the tokenized string\n",
        "def bert_key_word_index(text, bert_tokenizer, key_word, verbose = False):\n",
        "    # Add the special tokens.\n",
        "    marked_text = \"[CLS] \" + text + \" [SEP]\"\n",
        "\n",
        "    # Split the sentence into tokens.\n",
        "    tokenized_text = bert_tokenizer.tokenize(marked_text)\n",
        "    if verbose:\n",
        "        print (tokenized_text)\n",
        "    return tokenized_text.index(key_word)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lg61m1F-GlG2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "spring_S= [(i,bert_key_word_index(s, tokenizer, 'spring')) for i,s in enumerate(df_spring.text.to_list())]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o50JLTeYGo6G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "spring_distances = []\n",
        "for i in range(len(spring_S)):\n",
        "    for j in range(i+1,len(spring_S)):\n",
        "        k1 = spring_S[i][0]\n",
        "        i1 = spring_S[i][1]\n",
        "        k2 = spring_S[j][0]\n",
        "        i2 = spring_S[j][1] \n",
        "        dist = distance(spring_embeddings[k1][i1],spring_embeddings[k2][i2]).tolist()\n",
        "        #print(dist)\n",
        "        spring_distances.append((k1,k2,dist))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgwXbEdkG3xL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "bc83e430-cba3-4596-9ac8-2bf96078afbb"
      },
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "out = plt.hist([d[2] for d in spring_distances], bins = 300)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F+wEA2Ag+wgMAaFr2CNRI8h+qaiR55xjjwtUbVNW5JOeS5Oabb15yONZp+/zFXL73jpXs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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2GitmScHJn5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "records = [(df_spring.text.to_list()[d[0]], df_spring.text.to_list()[d[1]], \n",
        "            df_spring.synset.to_list()[d[0]], df_spring.synset.to_list()[d[1]], d[2]) for d in spring_distances]\n",
        "df_dist = pd.DataFrame.from_records(records, columns =['sentence1', 'sentence2', 'synset1','synset2' ,'distance'])\n",
        "#df_dist.to_csv('.\\data\\spring_distances_pytorch_correct.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pk1ane6zHTCw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600,
          "referenced_widgets": [
            "ccd1bce098ee4c2d8ab8d6a2c194a7ac",
            "32636475227545ecae89ce63219ea088",
            "a168310a06ea4a5e9458f4d7cef0cea8",
            "9ad25f2ab44444518ebf795f5dced99e",
            "edc7c1130be44781a9df4bbce2920438",
            "fe170dd75c0b4615995db12117c72a19",
            "694c2679e04d4206b061cd6bd0a75790",
            "a6f4a326c5724e529671d4aced0b7581",
            "fc1d1efe9e8d444ab89cf06233816b78",
            "741be24a358343cfad8eefa97c7624fc",
            "b14e6dc74b6348c7b91ae431d1232bed",
            "b470a39b987242b38c7bb4ea16e660e9"
          ]
        },
        "outputId": "62488e60-10cc-48fc-d631-b2739d55c3e7"
      },
      "source": [
        "range_slider = widgets.FloatRangeSlider(\n",
        "    value=[0.0, 1.0],\n",
        "    min=0.0, max=1.0, step=0.1,\n",
        "    description='distance:',\n",
        "    readout_format='.1f',\n",
        ")\n",
        "@interact\n",
        "def view_synsets_distances(distance = range_slider, synset1 = synsets, synset2 = synsets):\n",
        "    return df_dist[(df_dist.synset1==synset1) & (df_dist.synset2==synset2) &\n",
        "                  (df_dist.distance > distance[0]) & (df_dist.distance < distance[1])] "
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ccd1bce098ee4c2d8ab8d6a2c194a7ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(FloatRangeSlider(value=(0.0, 1.0), description='distance:', max=1.0, readout_format='.1f…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocbJGKIKITDi",
        "colab_type": "text"
      },
      "source": [
        "## For each word *'spring'* in one sentence find the sentence with the closest word *'spring'*\n",
        " <br>\n",
        "<font size=\"4\">\n",
        "1. accuracy1 matching the synset of the matched sentences <br><br>\n",
        "2. accuracy2 as accuracy1 but considers <i>'season, n'</i> a match of <i>'season, a'</i><br><br>\n",
        "</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpeNkY8TIJDt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "results = []\n",
        "for layer_number in range(1,13,1):\n",
        "    spring_embeddings = [bert_embedding(text, model, tokenizer, layer_number=layer_number) for text in df_spring.text.to_list()]\n",
        "    spring_distances = []\n",
        "    for i in range(len(spring_S)):\n",
        "        for j in range(i+1,len(spring_S)):\n",
        "            k1 = spring_S[i][0]\n",
        "            i1 = spring_S[i][1]\n",
        "            k2 = spring_S[j][0]\n",
        "            i2 = spring_S[j][1] \n",
        "            dist = distance(spring_embeddings[k1][i1],spring_embeddings[k2][i2]).tolist()\n",
        "            #print(dist)\n",
        "            spring_distances.append((k1,k2,dist))\n",
        "\n",
        "    match = dict.fromkeys(df_spring.index.to_list())\n",
        "    min_dist = dict.fromkeys(df_spring.index.to_list(),10)\n",
        "    for d in spring_distances:\n",
        "        i1, i2 = d[:2]\n",
        "        if min_dist[i1] > d[2]:\n",
        "            min_dist[i1] = d[2]\n",
        "            match[i1] = i2\n",
        "        if min_dist[i2] > d[2]:\n",
        "            min_dist[i2] = d[2]\n",
        "            match[i2] = i1\n",
        "\n",
        "    df_spring['text_match_index'] = list(match.values())\n",
        "    df_spring['text_match_distance'] = list(min_dist.values())\n",
        "    df_spring['text_match_synset'] = df_spring.iloc[list(match.values())]['synset'].to_list()\n",
        "    df_spring['text_match'] = df_spring.iloc[list(match.values())]['text'].to_list()\n",
        "\n",
        "    acc1 = sum(df_spring.synset == df_spring.text_match_synset)/df_spring.shape[0]\n",
        "\n",
        "    # if we count spring noun and spring adjective as same\n",
        "    acc2= sum(df_spring.apply(lambda x: 1  if x['synset'] == x['text_match_synset'] else( 1 if x['synset']*x['text_match_synset'] ==2\n",
        "                    else 0), axis = 1))/df_spring.shape[0]   \n",
        "    \n",
        "    results.append((layer_number, round(acc1,2), round(acc2,2)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYPEHM1NIgXA",
        "colab_type": "text"
      },
      "source": [
        "## Find the layer with the best embedding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdbdfFhHIu2U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "1f50bd82-af02-4799-cd03-4f059b50bbb2"
      },
      "source": [
        "pd.DataFrame.from_records(results, columns = ['layer #', 'accuracy1', 'accuracy2']).style.set_table_styles(styles)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "    #T_e87975dc_4218_11ea_b6dc_0242ac1c0002 th {\n",
              "          font-size: 20px;\n",
              "          text-align: center;\n",
              "          font-weight: bold;\n",
              "          color: #ffffff;\n",
              "          background-color: #000000;\n",
              "    }    #T_e87975dc_4218_11ea_b6dc_0242ac1c0002 td {\n",
              "          font-size: 20px;\n",
              "          text-align: center;\n",
              "    }</style><table id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >layer #</th>        <th class=\"col_heading level0 col1\" >accuracy1</th>        <th class=\"col_heading level0 col2\" >accuracy2</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.57</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.71</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.8</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.87</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.81</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.89</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.84</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.88</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.88</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.93</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.91</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.93</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row6_col0\" class=\"data row6 col0\" >7</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.92</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.93</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row7_col0\" class=\"data row7 col0\" >8</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.93</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.95</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row8_col0\" class=\"data row8 col0\" >9</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.93</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.95</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row9_col0\" class=\"data row9 col0\" >10</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.93</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.96</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row10_col0\" class=\"data row10 col0\" >11</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.91</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.93</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row11_col0\" class=\"data row11 col0\" >12</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.92</td>\n",
              "                        <td id=\"T_e87975dc_4218_11ea_b6dc_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.96</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3e66de5d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RSsBb2cfQ5D",
        "colab_type": "text"
      },
      "source": [
        "### Layer 9 is the best"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pllvdrajfV1e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layer_number=9\n",
        "spring_embeddings = [bert_embedding(text, model, tokenizer, layer_number=layer_number) for text in df_spring.text.to_list()]\n",
        "spring_distances = []\n",
        "for i in range(len(spring_S)):\n",
        "    for j in range(i+1,len(spring_S)):\n",
        "        k1 = spring_S[i][0]\n",
        "        i1 = spring_S[i][1]\n",
        "        k2 = spring_S[j][0]\n",
        "        i2 = spring_S[j][1] \n",
        "        dist = distance(spring_embeddings[k1][i1],spring_embeddings[k2][i2]).tolist()\n",
        "        #print(dist)\n",
        "        spring_distances.append((k1,k2,dist))\n",
        "\n",
        "match = dict.fromkeys(df_spring.index.to_list())\n",
        "min_dist = dict.fromkeys(df_spring.index.to_list(),10)\n",
        "for d in spring_distances:\n",
        "    i1, i2 = d[:2]\n",
        "    if min_dist[i1] > d[2]:\n",
        "        min_dist[i1] = d[2]\n",
        "        match[i1] = i2\n",
        "    if min_dist[i2] > d[2]:\n",
        "        min_dist[i2] = d[2]\n",
        "        match[i2] = i1\n",
        "\n",
        "df_spring['text_match_index'] = list(match.values())\n",
        "df_spring['text_match_distance'] = list(min_dist.values())\n",
        "df_spring['text_match_synset'] = df_spring.iloc[list(match.values())]['synset'].to_list()\n",
        "df_spring['text_match'] = df_spring.iloc[list(match.values())]['text'].to_list()\n",
        "\n",
        "acc1 = sum(df_spring.synset == df_spring.text_match_synset)/df_spring.shape[0]\n",
        "\n",
        "# if we count spring noun and spring adjective as same\n",
        "acc2= sum(df_spring.apply(lambda x: 1  if x['synset'] == x['text_match_synset'] else( 1 if x['synset']*x['text_match_synset'] ==2\n",
        "                else 0), axis = 1))/df_spring.shape[0]   \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXf-nuMng-da",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "4c518157-04b1-41f6-c0a1-a090da8bd5dc"
      },
      "source": [
        "df_spring"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "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>text</th>\n",
              "      <th>synset</th>\n",
              "      <th>meaning</th>\n",
              "      <th>text_match_index</th>\n",
              "      <th>text_match_distance</th>\n",
              "      <th>text_match_synset</th>\n",
              "      <th>text_match</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>spring is around the corner.</td>\n",
              "      <td>1</td>\n",
              "      <td>season, n</td>\n",
              "      <td>4</td>\n",
              "      <td>0.243190</td>\n",
              "      <td>1</td>\n",
              "      <td>in a few days the beautiful spring will be here.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the night air of spring was chilly in the mountains.</td>\n",
              "      <td>1</td>\n",
              "      <td>season, n</td>\n",
              "      <td>6</td>\n",
              "      <td>0.185196</td>\n",
              "      <td>1</td>\n",
              "      <td>it seemed as if the spirit of spring had passed through the summer-house.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>i don't know how the ranch ever saw a spring without your sunny smile.</td>\n",
              "      <td>1</td>\n",
              "      <td>season, n</td>\n",
              "      <td>4</td>\n",
              "      <td>0.228063</td>\n",
              "      <td>1</td>\n",
              "      <td>in a few days the beautiful spring will be here.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>it was spring and she was ravenously hungry.</td>\n",
              "      <td>1</td>\n",
              "      <td>season, n</td>\n",
              "      <td>1</td>\n",
              "      <td>0.197071</td>\n",
              "      <td>1</td>\n",
              "      <td>the night air of spring was chilly in the mountains.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>in a few days the beautiful spring will be here.</td>\n",
              "      <td>1</td>\n",
              "      <td>season, n</td>\n",
              "      <td>7</td>\n",
              "      <td>0.165967</td>\n",
              "      <td>1</td>\n",
              "      <td>i love the beautiful spring , because the budding trees and the blossoming flowers and the tender green leaves fill my heart with joy.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>the downfall of the croatian spring marked the beginning of a period known as the croatian silence.</td>\n",
              "      <td>6</td>\n",
              "      <td>political</td>\n",
              "      <td>73</td>\n",
              "      <td>0.187741</td>\n",
              "      <td>6</td>\n",
              "      <td>the damascus spring made a major impact across the arab world, and initially there was considerable optimism that it would lead to real change.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>during the beijing spring , the general public was allowed greater freedom to criticize the government than the chinese people had previously been allowed under the government of the people's republic of china.</td>\n",
              "      <td>6</td>\n",
              "      <td>political</td>\n",
              "      <td>72</td>\n",
              "      <td>0.137701</td>\n",
              "      <td>6</td>\n",
              "      <td>the seoul spring was a period of democratization in south korea from 26 october 1979 to 17 may 1980.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>the seoul spring was a period of democratization in south korea from 26 october 1979 to 17 may 1980.</td>\n",
              "      <td>6</td>\n",
              "      <td>political</td>\n",
              "      <td>71</td>\n",
              "      <td>0.137701</td>\n",
              "      <td>6</td>\n",
              "      <td>during the beijing spring , the general public was allowed greater freedom to criticize the government than the chinese people had previously been allowed under the government of the people's republic of china.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>the damascus spring made a major impact across the arab world, and initially there was considerable optimism that it would lead to real change.</td>\n",
              "      <td>6</td>\n",
              "      <td>political</td>\n",
              "      <td>72</td>\n",
              "      <td>0.174569</td>\n",
              "      <td>6</td>\n",
              "      <td>the seoul spring was a period of democratization in south korea from 26 october 1979 to 17 may 1980.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>while some might see this as a venezuelan spring , the reality is that popular movements in countries like venezuela over the last few years have often been highly suppressed.</td>\n",
              "      <td>6</td>\n",
              "      <td>political</td>\n",
              "      <td>73</td>\n",
              "      <td>0.242655</td>\n",
              "      <td>6</td>\n",
              "      <td>the damascus spring made a major impact across the arab world, and initially there was considerable optimism that it would lead to real change.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>75 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                  text  ...                                                                                                                                                                                                          text_match\n",
              "0   spring is around the corner.                                                                                                                                                                                        ...  in a few days the beautiful spring will be here.                                                                                                                                                                  \n",
              "1   the night air of spring was chilly in the mountains.                                                                                                                                                                ...  it seemed as if the spirit of spring had passed through the summer-house.                                                                                                                                         \n",
              "2   i don't know how the ranch ever saw a spring without your sunny smile.                                                                                                                                              ...  in a few days the beautiful spring will be here.                                                                                                                                                                  \n",
              "3   it was spring and she was ravenously hungry.                                                                                                                                                                        ...  the night air of spring was chilly in the mountains.                                                                                                                                                              \n",
              "4   in a few days the beautiful spring will be here.                                                                                                                                                                    ...  i love the beautiful spring , because the budding trees and the blossoming flowers and the tender green leaves fill my heart with joy.                                                                            \n",
              "..                                               ...                                                                                                                                                                    ...                                                                                                                                     ...                                                                            \n",
              "70  the downfall of the croatian spring marked the beginning of a period known as the croatian silence.                                                                                                                 ...  the damascus spring made a major impact across the arab world, and initially there was considerable optimism that it would lead to real change.                                                                   \n",
              "71  during the beijing spring , the general public was allowed greater freedom to criticize the government than the chinese people had previously been allowed under the government of the people's republic of china.  ...  the seoul spring was a period of democratization in south korea from 26 october 1979 to 17 may 1980.                                                                                                              \n",
              "72  the seoul spring was a period of democratization in south korea from 26 october 1979 to 17 may 1980.                                                                                                                ...  during the beijing spring , the general public was allowed greater freedom to criticize the government than the chinese people had previously been allowed under the government of the people's republic of china.\n",
              "73  the damascus spring made a major impact across the arab world, and initially there was considerable optimism that it would lead to real change.                                                                     ...  the seoul spring was a period of democratization in south korea from 26 october 1979 to 17 may 1980.                                                                                                              \n",
              "74  while some might see this as a venezuelan spring , the reality is that popular movements in countries like venezuela over the last few years have often been highly suppressed.                                     ...  the damascus spring made a major impact across the arab world, and initially there was considerable optimism that it would lead to real change.                                                                   \n",
              "\n",
              "[75 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    }
  ]
}