NER_Model3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NER-Model3.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOWKIXYv/st8FabOZFL4/BW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anamika1302/CS-541-NER-SARK_NLP/blob/main/NER_Model3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Training NER MODEL 3 on enitities :**\n",
        "# **StatusTime,StatusEmploy**\n"
      ],
      "metadata": {
        "id": "tnRJF4jaYCLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import os\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "if 'spark_jsl.json' not in os.listdir():\n",
        "  license_keys = files.upload()\n",
        "  os.rename(list(license_keys.keys())[0], 'spark_jsl.json')\n",
        "\n",
        "with open('spark_jsl.json') as f:\n",
        "    license_keys = json.load(f)\n",
        "\n",
        "# Defining license key-value pairs as local variables\n",
        "locals().update(license_keys)\n",
        "os.environ.update(license_keys)"
      ],
      "metadata": {
        "id": "MKPwLBkPZ3xf",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "50f0d306-f079-434e-d9ac-567eba622aa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-52ae4d13-f2e4-49bc-a559-38a03c7edcc0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-52ae4d13-f2e4-49bc-a559-38a03c7edcc0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving spark_nlp_for_healthcare_spark_ocr_4699.json to spark_nlp_for_healthcare_spark_ocr_4699.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Qfh8ADOaU2k",
        "outputId": "d1a7e9da-2ce3-48b2-a588-45f8cb5b2a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Installing pyspark and spark-nlp\n",
        "! pip install --upgrade -q pyspark==3.1.2 spark-nlp==$PUBLIC_VERSION\n",
        "\n",
        "# Installing Spark NLP Display Library for visualization\n",
        "! pip install -q spark-nlp-display\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1EoJEKpYFG9",
        "outputId": "7dcab849-0575-4fe8-e7e1-5b2657f90edb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 212.4 MB 76 kB/s \n",
            "\u001b[K     |████████████████████████████████| 142 kB 25.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 198 kB 84.8 MB/s \n",
            "\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 95 kB 4.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 66 kB 6.9 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Installing pyspark and spark-nlp\n",
        "! pip install --upgrade -q pyspark==3.1.2 spark-nlp==$PUBLIC_VERSION\n",
        "\n",
        "# Installing Spark NLP Healthcare\n",
        "! pip install --upgrade -q spark-nlp-jsl==$JSL_VERSION  --extra-index-url https://pypi.johnsnowlabs.com/$SECRET\n",
        "\n",
        "# Installing Spark NLP Display Library for visualization\n",
        "! pip install -q spark-nlp-display"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-pCva5zNzJa",
        "outputId": "f9fc4d26-66cc-4f74-ebd6-e3973296f627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▊                              | 10 kB 5.7 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 20 kB 10.4 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 30 kB 2.7 MB/s eta 0:00:01\r\u001b[K     |███████                         | 40 kB 3.6 MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 51 kB 4.0 MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 61 kB 4.7 MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 71 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 81 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 92 kB 4.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 102 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 112 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 122 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 133 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▍       | 143 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 153 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 163 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 174 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 184 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 188 kB 4.4 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sparknlp\n",
        "import os\n",
        "from sparknlp.training import CoNLL\n",
        "spark = sparknlp.start(gpu = True) # for GPU training >> sparknlp.start(gpu = True)\n",
        "\n",
        "from sparknlp.base import *\n",
        "from sparknlp.annotator import *\n",
        "\n",
        "print(\"Spark NLP version\", sparknlp.version())\n",
        "\n",
        "print(\"Apache Spark version:\", spark.version)\n",
        "\n",
        "spark\n",
        "\n"
      ],
      "metadata": {
        "id": "7Lc1eheFYNXA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "32361d78-7660-489a-f2d0-1d4e8045bf90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Spark NLP version 3.4.2\n",
            "Apache Spark version: 3.1.2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyspark.sql.session.SparkSession at 0x7fb3dac64550>"
            ],
            "text/html": [
              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://1abcea5a764c:4040\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v3.1.2</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local[*]</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>Spark NLP</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/umass_ner/NER_MODEL_3/train_ner_model3.conll') as f:\n",
        "    train_txt =f.read()\n",
        "\n",
        "print (train_txt[:500])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15uA1IJPYXFL",
        "outputId": "5aba97f8-aa86-46ef-d96b-335559eaa71c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-DOCSTART- -X- -X- O\n",
            "\n",
            "Social NN NN O\n",
            "History NN NN O\n",
            ": NN NN O\n",
            "Tob NN NN O\n",
            "(-) NNP NNP B-StatusTime\n",
            ", NN NN O\n",
            "EtOH NNP NNP B-StatusTime\n",
            "- NN NN O\n",
            "a NN NN O\n",
            "glass NN NN O\n",
            "of NN NN O\n",
            "wine NN NN O\n",
            "1-2x/month NN NN O\n",
            ", NN NN O\n",
            "IVDU NN NN O\n",
            "(-) NNP NNP B-StatusTime\n",
            ", NN NN O\n",
            "lives NNP NNP B-StatusTime\n",
            "with NN NN O\n",
            "her NN NN O\n",
            "husband NN NN O\n",
            "and NN NN O\n",
            "9yr NN NN O\n",
            "old NN NN O\n",
            "daughter NN NN O\n",
            ", NN NN O\n",
            "does NNP NNP B-StatusEmploy\n",
            "not NNP NNP I-StatusEmploy\n",
            "work NNP NNP I-StatusEmploy\n",
            "outside NNP NNP\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading training dataset\n",
        "\n",
        "training_data = CoNLL().readDataset(spark, '/content/drive/MyDrive/umass_ner/NER_MODEL_3/train_ner_model3.conll')\n",
        "\n",
        "training_data.show(3)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hKBLPynfgVw",
        "outputId": "72dee36a-a4ad-42fa-b4a6-6f9aca12c5b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n",
            "|                text|            document|            sentence|               token|                 pos|               label|\n",
            "+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n",
            "|Social History : ...|[{document, 0, 15...|[{document, 0, 15...|[{token, 0, 5, So...|[{pos, 0, 5, NN, ...|[{named_entity, 0...|\n",
            "|SOCIAL HISTORY : ...|[{document, 0, 14...|[{document, 0, 14...|[{token, 0, 5, SO...|[{pos, 0, 5, NN, ...|[{named_entity, 0...|\n",
            "|Social History : ...|[{document, 0, 12...|[{document, 0, 12...|[{token, 0, 5, So...|[{pos, 0, 5, NN, ...|[{named_entity, 0...|\n",
            "+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n",
            "only showing top 3 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_data.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3DXDDBgYp5m",
        "outputId": "8e4b39d4-caa8-4cb6-a0d5-fb2cf53fb2f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1316"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyspark.sql.functions as F\n",
        "\n",
        "training_data.select(F.explode(F.arrays_zip('token.result', 'pos.result',  'label.result')).alias(\"cols\")) \\\n",
        ".select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "        F.expr(\"cols['1']\").alias(\"pos\"),\n",
        "        F.expr(\"cols['2']\").alias(\"ner_label\")).limit(100).show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LDvgrXmYuV0",
        "outputId": "b01fb19f-b6b8-4e06-9232-550e38a6c844"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+---+------------+\n",
            "|     token|pos|   ner_label|\n",
            "+----------+---+------------+\n",
            "|    Social| NN|           O|\n",
            "|   History| NN|           O|\n",
            "|         :| NN|           O|\n",
            "|       Tob| NN|           O|\n",
            "|       (-)|NNP|B-StatusTime|\n",
            "|         ,| NN|           O|\n",
            "|      EtOH|NNP|B-StatusTime|\n",
            "|         -| NN|           O|\n",
            "|         a| NN|           O|\n",
            "|     glass| NN|           O|\n",
            "|        of| NN|           O|\n",
            "|      wine| NN|           O|\n",
            "|1-2x/month| NN|           O|\n",
            "|         ,| NN|           O|\n",
            "|      IVDU| NN|           O|\n",
            "|       (-)|NNP|B-StatusTime|\n",
            "|         ,| NN|           O|\n",
            "|     lives|NNP|B-StatusTime|\n",
            "|      with| NN|           O|\n",
            "|       her| NN|           O|\n",
            "+----------+---+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_data.select(F.explode(F.arrays_zip('token.result','label.result')).alias(\"cols\")) \\\n",
        ".select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "        F.expr(\"cols['1']\").alias(\"ground_truth\")).groupBy('ground_truth').count().orderBy('count', ascending=False).show(100,truncate=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2baSrIu4Yw5J",
        "outputId": "67fc642b-8f72-450b-b600-97ea9b2cfdb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------+-----+\n",
            "|ground_truth  |count|\n",
            "+--------------+-----+\n",
            "|O             |61723|\n",
            "|B-StatusTime  |4134 |\n",
            "|I-StatusTime  |2376 |\n",
            "|B-StatusEmploy|982  |\n",
            "|I-StatusEmploy|866  |\n",
            "+--------------+-----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = CoNLL().readDataset(spark, '/content/drive/MyDrive/umass_ner/NER_MODEL_3/testF_ner_model3.conll')\n",
        "\n",
        "test_data.select(F.explode(F.arrays_zip('token.result','label.result')).alias(\"cols\")) \\\n",
        ".select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "        F.expr(\"cols['1']\").alias(\"ground_truth\")).groupBy('ground_truth').count().orderBy('count', ascending=False).show(100,truncate=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mlN7j3aY2GR",
        "outputId": "aecac2e7-6b75-46cd-8570-df6070c356b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------+-----+\n",
            "|ground_truth  |count|\n",
            "+--------------+-----+\n",
            "|O             |5987 |\n",
            "|B-StatusTime  |474  |\n",
            "|I-StatusTime  |212  |\n",
            "|I-StatusEmploy|110  |\n",
            "|B-StatusEmploy|90   |\n",
            "+--------------+-----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyspark.sql.functions as F\n",
        "\n",
        "test_data.select(F.explode(F.arrays_zip('token.result', 'pos.result',  'label.result')).alias(\"cols\")) \\\n",
        ".select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "        F.expr(\"cols['1']\").alias(\"pos\"),\n",
        "        F.expr(\"cols['2']\").alias(\"ner_label\")).limit(100).show()"
      ],
      "metadata": {
        "id": "mfNZl_xDy1xh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fea890b5-3f84-496f-ce36-55b8107d5967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------+---+------------+\n",
            "|    token|pos|   ner_label|\n",
            "+---------+---+------------+\n",
            "|   SOCIAL| NN|           O|\n",
            "|  HISTORY| NN|           O|\n",
            "|        :| NN|           O|\n",
            "|    Lives|NNP|B-StatusTime|\n",
            "|     with| NN|           O|\n",
            "|      his| NN|           O|\n",
            "|   caring| NN|           O|\n",
            "|      and| NN|           O|\n",
            "|  devoted| NN|           O|\n",
            "|  parents| NN|           O|\n",
            "|       at| NN|           O|\n",
            "|     home| NN|           O|\n",
            "|        .| NN|           O|\n",
            "|   Enjoys| NN|           O|\n",
            "|   movies| NN|           O|\n",
            "|      and| NN|           O|\n",
            "|computers| NN|           O|\n",
            "|        .| NN|           O|\n",
            "|       No|NNP|B-StatusTime|\n",
            "|  history|NNP|I-StatusTime|\n",
            "+---------+---+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bert_embedding = BertEmbeddings.pretrained('bert_base_cased', 'en')\\\n",
        "   .setInputCols([\"sentence\",'token'])\\\n",
        "   .setOutputCol(\"bert_embeddings\")\\\n",
        "   .setCaseSensitive(True)"
      ],
      "metadata": {
        "id": "Mo7a3PT50n0F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7734a742-8cad-43fa-8e85-6e1f0bd2d4b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert_base_cased download started this may take some time.\n",
            "Approximate size to download 389.1 MB\n",
            "[OK!]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r /content/drive/MyDrive/umass_ner/NER_MODEL_3/testF_data_ner_model3_bert_embedding.parquet"
      ],
      "metadata": {
        "id": "9yvI55byS7ZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bert_embedding.transform(test_data).write.parquet('/content/drive/MyDrive/umass_ner/NER_MODEL_3/testF_data_ner_model3_bert_embedding.parquet')"
      ],
      "metadata": {
        "id": "bqcVs5CjZQhi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -r /content/drive/MyDrive/umass_ner/NER_MODEL_1/test_data_bert_embedding.parquet"
      ],
      "metadata": {
        "id": "CbVTZ0tD_IQq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !mkdir /root/ner_logs_model1"
      ],
      "metadata": {
        "id": "GZQuSE7OHzBz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -r /content/ner_dl_logs_model3"
      ],
      "metadata": {
        "id": "WrmqPxJuKipo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nerTagger = NerDLApproach()\\\n",
        "      .setInputCols([\"sentence\", \"token\", \"bert_embeddings\"])\\\n",
        "      .setLabelColumn(\"label\")\\\n",
        "      .setOutputCol(\"ner\")\\\n",
        "      .setMaxEpochs(15)\\\n",
        "      .setLr(0.0002)\\\n",
        "      .setBatchSize(16)\\\n",
        "      .setRandomSeed(0)\\\n",
        "      .setVerbose(1)\\\n",
        "      .setEvaluationLogExtended(True) \\\n",
        "      .setEnableOutputLogs(True)\\\n",
        "      .setIncludeConfidence(True)\\\n",
        "      .setUseBestModel(True)\\\n",
        "      .setTestDataset('/content/drive/MyDrive/umass_ner/NER_MODEL_3/testF_data_ner_model3_bert_embedding.parquet')\\\n",
        "      .setOutputLogsPath('./ner_dl_logs_model3')# if not set, logs will be written to ~/annotator_logs\n",
        " #    .setGraphFolder('graphs') >> put your graph file (pb) under this folder if you are using a custom graph generated thru 4.1 NerDL-Graph.ipynb notebook\n",
        " #    .setEnableMemoryOptimizer() >> if you have a limited memory and a large conll file, you can set this True to train batch by batch \n",
        "    \n",
        "# do hyperparameter by tuning the params above (max epoch, LR, dropout etc.) to get better results\n",
        "ner_pipeline = Pipeline(stages=[\n",
        "      bert_embedding,\n",
        "      nerTagger\n",
        " ])\n",
        "\n"
      ],
      "metadata": {
        "id": "D83P5kd3ZCCq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_model = ner_pipeline.fit(training_data)"
      ],
      "metadata": {
        "id": "KOUbUvsyZG26"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cd /content/ner_dl_logs_model3 && ls -lt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bu0WDE21ZJuF",
        "outputId": "319c5973-d3b7-4107-ad1c-68579277b230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 24\n",
            "-rw-r--r-- 1 root root 9119 Apr 28 03:59 NerDLApproach_4702adc74552.log\n",
            "-rw-r--r-- 1 root root 8927 Apr 28 03:59 NerDLApproach_51ea32b2bd75.log\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat /content/ner_dl_logs_model3/NerDLApproach_4702adc74552.log"
      ],
      "metadata": {
        "id": "W5aop-1ndH8Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50a1b049-8a0e-4561-efb0-6b9cfebf08ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name of the selected graph: ner-dl/blstm_10_768_128_120.pb\n",
            "Training started - total epochs: 15 - lr: 2.0E-4 - batch size: 16 - labels: 5 - chars: 89 - training examples: 1316\n",
            "\n",
            "\n",
            "Epoch 1/15 started, lr: 2.0E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 1/15 - 23.52s - loss: 2173.3035 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 2.13s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 0\t 0\t 90\t 0.0\t 0.0\t 0.0\n",
            "B-StatusTime\t 214\t 38\t 258\t 0.8492063\t 0.45338982\t 0.59116024\n",
            "I-StatusTime\t 0\t 0\t 209\t 0.0\t 0.0\t 0.0\n",
            "I-StatusEmploy\t 0\t 0\t 110\t 0.0\t 0.0\t 0.0\n",
            "tp: 214 fp: 38 fn: 667 labels: 4\n",
            "Macro-average\t prec: 0.21230158, rec: 0.113347456, f1: 0.14779006\n",
            "Micro-average\t prec: 0.8492063, rec: 0.2429058, f1: 0.37775818\n",
            "\n",
            "\n",
            "Epoch 2/15 started, lr: 1.9900496E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 2/15 - 23.73s - loss: 1438.5688 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.59s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 24\t 8\t 66\t 0.75\t 0.26666668\t 0.39344266\n",
            "B-StatusTime\t 336\t 63\t 136\t 0.84210527\t 0.7118644\t 0.771527\n",
            "I-StatusTime\t 0\t 0\t 209\t 0.0\t 0.0\t 0.0\n",
            "I-StatusEmploy\t 1\t 0\t 109\t 1.0\t 0.009090909\t 0.018018018\n",
            "tp: 361 fp: 71 fn: 520 labels: 4\n",
            "Macro-average\t prec: 0.64802635, rec: 0.2469055, f1: 0.357572\n",
            "Micro-average\t prec: 0.8356481, rec: 0.40976164, f1: 0.5498858\n",
            "\n",
            "\n",
            "Epoch 3/15 started, lr: 1.980198E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 3/15 - 23.06s - loss: 1243.8081 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.55s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 37\t 20\t 53\t 0.64912283\t 0.41111112\t 0.5034014\n",
            "B-StatusTime\t 374\t 101\t 98\t 0.7873684\t 0.7923729\t 0.78986275\n",
            "I-StatusTime\t 0\t 0\t 209\t 0.0\t 0.0\t 0.0\n",
            "I-StatusEmploy\t 9\t 3\t 101\t 0.75\t 0.08181818\t 0.14754099\n",
            "tp: 420 fp: 124 fn: 461 labels: 4\n",
            "Macro-average\t prec: 0.5466228, rec: 0.32132557, f1: 0.40473348\n",
            "Micro-average\t prec: 0.77205884, rec: 0.476731, f1: 0.5894737\n",
            "\n",
            "\n",
            "Epoch 4/15 started, lr: 1.9704433E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 4/15 - 22.52s - loss: 1130.9984 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.50s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 66\t 29\t 24\t 0.69473684\t 0.73333335\t 0.7135135\n",
            "B-StatusTime\t 388\t 115\t 84\t 0.7713718\t 0.8220339\t 0.7958974\n",
            "I-StatusTime\t 34\t 37\t 175\t 0.47887325\t 0.16267942\t 0.24285713\n",
            "I-StatusEmploy\t 24\t 8\t 86\t 0.75\t 0.21818182\t 0.33802813\n",
            "tp: 512 fp: 189 fn: 369 labels: 4\n",
            "Macro-average\t prec: 0.67374545, rec: 0.48405713, f1: 0.56336254\n",
            "Micro-average\t prec: 0.7303852, rec: 0.5811578, f1: 0.64728194\n",
            "\n",
            "\n",
            "Epoch 5/15 started, lr: 1.9607843E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 5/15 - 23.09s - loss: 1046.4415 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.86s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 63\t 25\t 27\t 0.71590906\t 0.7\t 0.7078652\n",
            "B-StatusTime\t 364\t 67\t 108\t 0.84454757\t 0.7711864\t 0.8062015\n",
            "I-StatusTime\t 4\t 2\t 205\t 0.6666667\t 0.019138755\t 0.037209302\n",
            "I-StatusEmploy\t 26\t 7\t 84\t 0.7878788\t 0.23636363\t 0.3636364\n",
            "tp: 457 fp: 101 fn: 424 labels: 4\n",
            "Macro-average\t prec: 0.7537505, rec: 0.43167222, f1: 0.54895717\n",
            "Micro-average\t prec: 0.8189964, rec: 0.51872873, f1: 0.63516337\n",
            "\n",
            "\n",
            "Epoch 6/15 started, lr: 1.9512195E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 6/15 - 22.87s - loss: 983.92175 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.50s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 72\t 29\t 18\t 0.7128713\t 0.8\t 0.75392675\n",
            "B-StatusTime\t 376\t 80\t 96\t 0.8245614\t 0.7966102\t 0.8103449\n",
            "I-StatusTime\t 6\t 4\t 203\t 0.6\t 0.028708134\t 0.05479452\n",
            "I-StatusEmploy\t 45\t 9\t 65\t 0.8333333\t 0.4090909\t 0.5487805\n",
            "tp: 499 fp: 122 fn: 382 labels: 4\n",
            "Macro-average\t prec: 0.74269146, rec: 0.5086023, f1: 0.60375047\n",
            "Micro-average\t prec: 0.8035427, rec: 0.56640184, f1: 0.66444737\n",
            "\n",
            "\n",
            "Epoch 7/15 started, lr: 1.9417476E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 7/15 - 23.09s - loss: 925.55774 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.54s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 76\t 29\t 14\t 0.72380954\t 0.84444445\t 0.7794872\n",
            "B-StatusTime\t 402\t 104\t 70\t 0.7944664\t 0.85169494\t 0.8220859\n",
            "I-StatusTime\t 46\t 34\t 163\t 0.575\t 0.2200957\t 0.3183391\n",
            "I-StatusEmploy\t 61\t 15\t 49\t 0.80263156\t 0.55454546\t 0.65591395\n",
            "tp: 585 fp: 182 fn: 296 labels: 4\n",
            "Macro-average\t prec: 0.7239769, rec: 0.6176951, f1: 0.66662645\n",
            "Micro-average\t prec: 0.7627119, rec: 0.66401815, f1: 0.70995146\n",
            "\n",
            "\n",
            "Epoch 8/15 started, lr: 1.9323672E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 8/15 - 23.37s - loss: 884.2072 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.53s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 75\t 25\t 15\t 0.75\t 0.8333333\t 0.7894737\n",
            "B-StatusTime\t 381\t 75\t 91\t 0.8355263\t 0.8072034\t 0.8211207\n",
            "I-StatusTime\t 41\t 26\t 168\t 0.6119403\t 0.19617225\t 0.29710147\n",
            "I-StatusEmploy\t 72\t 19\t 38\t 0.7912088\t 0.6545454\t 0.71641785\n",
            "tp: 569 fp: 145 fn: 312 labels: 4\n",
            "Macro-average\t prec: 0.74716884, rec: 0.6228136, f1: 0.6793472\n",
            "Micro-average\t prec: 0.79691875, rec: 0.645857, f1: 0.7134796\n",
            "\n",
            "\n",
            "Epoch 9/15 started, lr: 1.9230769E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 9/15 - 22.93s - loss: 827.5715 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.51s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 76\t 19\t 14\t 0.8\t 0.84444445\t 0.82162166\n",
            "B-StatusTime\t 390\t 87\t 82\t 0.8176101\t 0.8262712\t 0.8219178\n",
            "I-StatusTime\t 23\t 12\t 186\t 0.6571429\t 0.11004785\t 0.1885246\n",
            "I-StatusEmploy\t 64\t 12\t 46\t 0.84210527\t 0.58181816\t 0.688172\n",
            "tp: 553 fp: 130 fn: 328 labels: 4\n",
            "Macro-average\t prec: 0.7792146, rec: 0.5906454, f1: 0.6719512\n",
            "Micro-average\t prec: 0.80966324, rec: 0.6276958, f1: 0.7071611\n",
            "\n",
            "\n",
            "Epoch 10/15 started, lr: 1.9138756E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 10/15 - 23.24s - loss: 794.1115 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.55s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 76\t 19\t 14\t 0.8\t 0.84444445\t 0.82162166\n",
            "B-StatusTime\t 398\t 90\t 74\t 0.81557375\t 0.84322035\t 0.82916665\n",
            "I-StatusTime\t 39\t 18\t 170\t 0.68421054\t 0.18660288\t 0.29323307\n",
            "I-StatusEmploy\t 73\t 16\t 37\t 0.8202247\t 0.6636364\t 0.7336683\n",
            "tp: 586 fp: 143 fn: 295 labels: 4\n",
            "Macro-average\t prec: 0.78000224, rec: 0.634476, f1: 0.699753\n",
            "Micro-average\t prec: 0.8038409, rec: 0.6651532, f1: 0.7279503\n",
            "\n",
            "\n",
            "Epoch 11/15 started, lr: 1.9047619E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 11/15 - 23.85s - loss: 740.74506 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.16s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 78\t 21\t 12\t 0.7878788\t 0.8666667\t 0.82539684\n",
            "B-StatusTime\t 412\t 110\t 60\t 0.789272\t 0.87288135\t 0.8289738\n",
            "I-StatusTime\t 67\t 66\t 142\t 0.5037594\t 0.32057416\t 0.39181286\n",
            "I-StatusEmploy\t 81\t 22\t 29\t 0.78640777\t 0.73636365\t 0.7605634\n",
            "tp: 638 fp: 219 fn: 243 labels: 4\n",
            "Macro-average\t prec: 0.7168295, rec: 0.6991214, f1: 0.7078647\n",
            "Micro-average\t prec: 0.7444574, rec: 0.72417706, f1: 0.73417723\n",
            "\n",
            "\n",
            "Epoch 12/15 started, lr: 1.8957346E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 12/15 - 23.67s - loss: 713.27014 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.12s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 76\t 20\t 14\t 0.7916667\t 0.84444445\t 0.8172043\n",
            "B-StatusTime\t 405\t 89\t 67\t 0.81983805\t 0.8580508\t 0.8385093\n",
            "I-StatusTime\t 50\t 22\t 159\t 0.6944444\t 0.23923445\t 0.3558719\n",
            "I-StatusEmploy\t 66\t 11\t 44\t 0.85714287\t 0.6\t 0.70588243\n",
            "tp: 597 fp: 142 fn: 284 labels: 4\n",
            "Macro-average\t prec: 0.79077303, rec: 0.6354325, f1: 0.704643\n",
            "Micro-average\t prec: 0.80784845, rec: 0.67763907, f1: 0.7370371\n",
            "\n",
            "\n",
            "Epoch 13/15 started, lr: 1.8867925E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 13/15 - 23.87s - loss: 682.0927 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.10s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 74\t 13\t 16\t 0.85057473\t 0.82222223\t 0.8361582\n",
            "B-StatusTime\t 356\t 50\t 116\t 0.87684727\t 0.7542373\t 0.81093395\n",
            "I-StatusTime\t 19\t 8\t 190\t 0.7037037\t 0.09090909\t 0.16101697\n",
            "I-StatusEmploy\t 71\t 12\t 39\t 0.85542166\t 0.6454545\t 0.7357513\n",
            "tp: 520 fp: 83 fn: 361 labels: 4\n",
            "Macro-average\t prec: 0.8216368, rec: 0.5782058, f1: 0.67875516\n",
            "Micro-average\t prec: 0.8623549, rec: 0.5902384, f1: 0.7008086\n",
            "\n",
            "\n",
            "Epoch 14/15 started, lr: 1.8779341E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 14/15 - 12.67s - loss: 647.4911 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.75s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 73\t 13\t 17\t 0.8488372\t 0.8111111\t 0.8295454\n",
            "B-StatusTime\t 371\t 53\t 101\t 0.875\t 0.78601694\t 0.828125\n",
            "I-StatusTime\t 21\t 9\t 188\t 0.7\t 0.10047847\t 0.17573221\n",
            "I-StatusEmploy\t 74\t 12\t 36\t 0.8604651\t 0.6727273\t 0.7551021\n",
            "tp: 539 fp: 87 fn: 342 labels: 4\n",
            "Macro-average\t prec: 0.82107556, rec: 0.5925835, f1: 0.68836373\n",
            "Micro-average\t prec: 0.86102235, rec: 0.6118048, f1: 0.7153284\n",
            "\n",
            "\n",
            "Epoch 15/15 started, lr: 1.8691587E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 15/15 - 12.71s - loss: 615.4733 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.70s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-StatusEmploy\t 79\t 16\t 11\t 0.83157897\t 0.87777776\t 0.85405403\n",
            "B-StatusTime\t 406\t 96\t 66\t 0.80876493\t 0.8601695\t 0.83367556\n",
            "I-StatusTime\t 62\t 35\t 147\t 0.63917524\t 0.2966507\t 0.40522876\n",
            "I-StatusEmploy\t 74\t 12\t 36\t 0.8604651\t 0.6727273\t 0.7551021\n",
            "tp: 621 fp: 159 fn: 260 labels: 4\n",
            "Macro-average\t prec: 0.78499603, rec: 0.6768313, f1: 0.726912\n",
            "Micro-average\t prec: 0.79615384, rec: 0.70488083, f1: 0.7477423\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_df = ner_model.stages[1].transform(bert_embedding.transform(test_data))\n",
        "# pred_df = ner_model.stages[1].transform(test_data)"
      ],
      "metadata": {
        "id": "q8J1FgRNDLBU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pyspark.sql.functions as F\n",
        "\n",
        "pred_df.select(F.explode(F.arrays_zip(pred_df.token.result,\n",
        "                                          pred_df.label.result,\n",
        "                                          pred_df.ner.result)).alias(\"cols\")) \\\n",
        "                              .select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "                                      F.expr(\"cols['1']\").alias(\"ground_truth\"),\n",
        "                                      F.expr(\"cols['2']\").alias(\"prediction\")).toPandas()"
      ],
      "metadata": {
        "id": "UkKBqzP2A3Ju",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "cb3aca38-a78d-429e-ad9a-e5054c138b22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        token  ground_truth    prediction\n",
              "0      SOCIAL             O             O\n",
              "1     HISTORY             O             O\n",
              "2           :             O             O\n",
              "3       Lives  B-StatusTime  B-StatusTime\n",
              "4        with             O             O\n",
              "...       ...           ...           ...\n",
              "6868   father             O             O\n",
              "6869       of             O             O\n",
              "6870      one             O             O\n",
              "6871      son             O             O\n",
              "6872        .             O             O\n",
              "\n",
              "[6873 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5666408b-32cb-4dbc-b112-a95954712712\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>token</th>\n",
              "      <th>ground_truth</th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HISTORY</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>:</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lives</td>\n",
              "      <td>B-StatusTime</td>\n",
              "      <td>B-StatusTime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>with</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6868</th>\n",
              "      <td>father</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6869</th>\n",
              "      <td>of</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6870</th>\n",
              "      <td>one</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6871</th>\n",
              "      <td>son</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6872</th>\n",
              "      <td>.</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6873 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5666408b-32cb-4dbc-b112-a95954712712')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5666408b-32cb-4dbc-b112-a95954712712 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5666408b-32cb-4dbc-b112-a95954712712');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyspark.sql.functions as F\n",
        "\n",
        "pred_df.select(F.explode(F.arrays_zip(pred_df.token.result,\n",
        "                                          pred_df.label.result,\n",
        "                                          pred_df.ner.result)).alias(\"cols\")) \\\n",
        "                              .select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "                                      F.expr(\"cols['1']\").alias(\"ground_truth\"),\n",
        "                                      F.expr(\"cols['2']\").alias(\"prediction\")).show(truncate=False)\n",
        "\n"
      ],
      "metadata": {
        "id": "ZUA4sONKDQlB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a58f3982-9f59-4b87-8931-bf9d01624066"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------+------------+------------+\n",
            "|token    |ground_truth|prediction  |\n",
            "+---------+------------+------------+\n",
            "|SOCIAL   |O           |O           |\n",
            "|HISTORY  |O           |O           |\n",
            "|:        |O           |O           |\n",
            "|Lives    |B-StatusTime|B-StatusTime|\n",
            "|with     |O           |O           |\n",
            "|his      |O           |O           |\n",
            "|caring   |O           |O           |\n",
            "|and      |O           |O           |\n",
            "|devoted  |O           |O           |\n",
            "|parents  |O           |O           |\n",
            "|at       |O           |O           |\n",
            "|home     |O           |O           |\n",
            "|.        |O           |O           |\n",
            "|Enjoys   |O           |O           |\n",
            "|movies   |O           |O           |\n",
            "|and      |O           |O           |\n",
            "|computers|O           |O           |\n",
            "|.        |O           |O           |\n",
            "|No       |B-StatusTime|B-StatusTime|\n",
            "|history  |I-StatusTime|I-StatusTime|\n",
            "+---------+------------+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "preds_df_analysis = pred_df.select(F.explode(F.arrays_zip(pred_df.token.result,\n",
        "                                                     pred_df.label.result,\n",
        "                                                     pred_df.ner.result)).alias(\"cols\")) \\\n",
        "                      .select(F.expr(\"cols['0']\").alias(\"token\"),\n",
        "                              F.expr(\"cols['1']\").alias(\"ground_truth\"),\n",
        "                              F.expr(\"cols['2']\").alias(\"prediction\")).toPandas()\n",
        "preds_df_analysis[\"prediction\"].fillna( method ='ffill', inplace = True)\n",
        "print (classification_report(preds_df_analysis['ground_truth'], preds_df_analysis['prediction']))\n"
      ],
      "metadata": {
        "id": "4qUDJjse15bZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3ad71c3-9164-4f86-c5be-534e92ad6184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                precision    recall  f1-score   support\n",
            "\n",
            "B-StatusEmploy       0.83      0.88      0.85        90\n",
            "  B-StatusTime       0.81      0.86      0.83       474\n",
            "I-StatusEmploy       0.86      0.67      0.76       110\n",
            "  I-StatusTime       0.64      0.29      0.40       212\n",
            "             O       0.96      0.98      0.97      5987\n",
            "\n",
            "      accuracy                           0.94      6873\n",
            "     macro avg       0.82      0.74      0.76      6873\n",
            "  weighted avg       0.94      0.94      0.94      6873\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# idx = preds_df_analysis.loc[preds_df_analysis['ground_truth'].isnull(),'ground_truth'].index\n",
        "\n",
        "\n",
        "# preds_df_analysis.loc[idx]"
      ],
      "metadata": {
        "id": "jTRaBvJt5O9s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_model.stages[1].write().overwrite().save('/content/drive/MyDrive/umass_ner/model_ner_dl_3')"
      ],
      "metadata": {
        "id": "U6JUWcMTzp9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget  -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Public/utils/conll_eval.py\n",
        "\n",
        "import conll_eval\n",
        "\n",
        "metrics = conll_eval.evaluate(preds_df_analysis['ground_truth'].values, preds_df_analysis['prediction'].values)\n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "pd.DataFrame(metrics[1], columns=['entity','precision','recall','f1','support'])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "p8eLAnfp2cpG",
        "outputId": "9c20eef8-99a9-4382-f47a-e67923af4e1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processed 6873 tokens with 564 phrases; found: 606 phrases; correct: 417.\n",
            "accuracy:  70.09%; (non-O)\n",
            "accuracy:  94.15%; precision:  68.81%; recall:  73.94%; FB1:  71.28\n",
            "     StatusEmploy: precision:  64.71%; recall:  73.33%; FB1:  68.75  102\n",
            "       StatusTime: precision:  69.64%; recall:  74.05%; FB1:  71.78  504\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         entity  precision     recall         f1  support\n",
              "0  StatusEmploy  64.705882  73.333333  68.750000      102\n",
              "1    StatusTime  69.642857  74.050633  71.779141      504"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53dae138-bb92-4dfe-9f40-684a8f164f9c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>entity</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>StatusEmploy</td>\n",
              "      <td>64.705882</td>\n",
              "      <td>73.333333</td>\n",
              "      <td>68.750000</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>StatusTime</td>\n",
              "      <td>69.642857</td>\n",
              "      <td>74.050633</td>\n",
              "      <td>71.779141</td>\n",
              "      <td>504</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53dae138-bb92-4dfe-9f40-684a8f164f9c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-53dae138-bb92-4dfe-9f40-684a8f164f9c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-53dae138-bb92-4dfe-9f40-684a8f164f9c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Public/utils/ner_log_parser.py\n"
      ],
      "metadata": {
        "id": "ul7BLIF19fwV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import ner_log_parser"
      ],
      "metadata": {
        "id": "VZKsyNt79vsD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_log_parser.get_charts('/content/ner_dl_logs_model3/NerDLApproach_4702adc74552.log')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gJZY3Izr98s1",
        "outputId": "9c2bfb5d-37a2-4be3-d181-d4b6033fded7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ner_log_parser.loss_plot('/content/ner_dl_logs_model3/NerDLApproach_4702adc74552.log')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "87vpWC8q-g2e",
        "outputId": "9b3788ee-3423-4202-acb2-9dd88dbd5e7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAHwCAYAAAChTMYRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxU9b3/8dcnC4EkQAiZsAQQMgQEVBYRUOu+oVWpvb1V2yraxS5u7bXtrb33V9vbu/S2vV3sosWKW11qW622aq0riIoSxQ3ZQtgSgSSEhCV78vn9MQcbFTTgzJzJ5P18POaRyfecmfnMo5Z3zvd8zvmauyMiIiLpKyPsAkRERCSxFPYiIiJpTmEvIiKS5hT2IiIiaU5hLyIikuYU9iIiImlOYS8ioTGz75rZ78KuQyTdKexF+ggz22Bmp4bwubeaWZuZ7TazejN7zMwOPYj3CaV+kXSgsBeRZPihu+cDo4Aa4NZwyxHpWxT2In2cmeWY2c/M7K3g8TMzywm2FZnZX82sITgqf8bMMoJt/2pm1Wa2y8xWm9kpH/RZ7t4E3AUctp9azjWzFcHnPW1mk4LxO4AxwF+CGYJvxuv7i/QFCnsR+TdgDjANmArMAv492HYNUAVEgGHAtwE3s4nAFcBR7j4QOAPY8EEfZGb5wKeB5fvYNgG4G/hq8HkPEwv3fu5+EbAJOMfd8939hwf9bUX6IIW9iHwa+A93r3H3WuB7wEXBtnZgBHCIu7e7+zMeW1CjE8gBJptZtrtvcPd17/MZXzezBqACyAcu2cc+5wMPuftj7t4O/BgYABwTh+8o0qcp7EVkJLCx2+8bgzGAHxEL6L+bWaWZfQvA3SuIHYF/F6gxs3vMbCT792N3L3D34e5+7n7+MHhHHe7eBWwGSg7ye4lIQGEvIm8Bh3T7fUwwhrvvcvdr3L0UOBf4l73n5t39Lnf/SPBaB/43nnWYmQGjgepgSEt0ihwkhb1I35JtZv27PbKInSf/dzOLmFkR8B3gdwBmdraZjQ+Ct5HY9H2XmU00s5ODRr4WoBno+pC13Qt81MxOMbNsYv0CrcBzwfZtQOmH/AyRPklhL9K3PEwsmPc+vgv8J1AOvAa8DrwcjAGUAY8Du4HngV+7+1PEztf/AKgDtgLFwLUfpjB3Xw18BvhF8L7nEGvIawt2+R9if5Q0mNnXP8xnifQ1Fuu1ERERkXSlI3sREZE0p7AXERFJcwp7ERGRNKewFxERSXMKexERkTSXFXYBiVBUVORjx44NuwwREZGkeemll+rcPbKvbWkZ9mPHjqW8vDzsMkRERJLGzDbub5um8UVERNKcwl5ERCTNKexFRETSnMJeREQkzSnsRURE0pzCXkREJM0p7EVERNJcwsLezEab2VNm9qaZrTCzq4PxH5nZKjN7zczuN7OCbq+51swqzGy1mZ3RbXxuMFZhZt9KVM0iIiLpKJFH9h3ANe4+GZgDXG5mk4HHgMPc/QhgDXAtQLDtAmAKMBf4tZllmlkm8CvgTGAycGGwr4iIiPRAwsLe3be4+8vB813ASqDE3f/u7h3BbkuBUcHzecA97t7q7uuBCmBW8Khw90p3bwPuCfYVERGRHkjKOXszGwtMB15416bPAo8Ez0uAzd22VQVj+xsXERGRHkh42JtZPvAn4KvuvrPb+L8Rm+q/M06fc5mZlZtZeW1tbTzeUkREJC0kNOzNLJtY0N/p7vd1G78EOBv4tLt7MFwNjO728lHB2P7G38HdF7j7THefGYnsc9EfERGRPimR3fgG3AysdPefdBufC3wTONfdm7q95EHgAjPLMbNxQBnwIrAMKDOzcWbWj1gT34OJqltERCTdJHKJ22OBi4DXzeyVYOzbwPVADvBY7O8Blrr7l9x9hZndC7xJbHr/cnfvBDCzK4BHgUxgobuvSGDdIiIiacX+MYuePmbOnOlaz15ERPoSM3vJ3Wfua5vuoNcDTW0d7GppD7sMERGRg6Kw/wB7WjuYct2j3LF0Y9iliIiIHBSF/QfIy8mieGAO62r2hF2KiIjIQVHY90BpUT7raneHXYaIiMhBUdj3QLQ4j3W1u0nHZkYREUl/CvseiEby2dXSQd3utrBLEREROWAK+x6IRvIBNJUvIiK9ksK+B6LFCnsREem9FPY9MGJQf/pnZ6gjX0REeiWFfQ9kZJg68kVEpNdS2PdQtDifyjqFvYiI9D4K+x6KRvKo2tFMS3tn2KWIiIgcEIV9D0Uj+bjD+jqdtxcRkd5FYd9DpZE8QB35IiLS+yjse6i0KLj8Th35IiLSyyjse2hAv0xKCgaoSU9ERHodhf0BiBbr8jsREel9FPYHIBrJY13NHrq6tCCOiIj0Hgr7A1Aayae5vZOtO1vCLkVERKTHFPYHIBp05FfWqklPRER6D4X9ARiv1e9ERKQXUtgfgMjAHAbmZCnsRUSkV1HYHwAzo1Qd+SIi0sso7A9QtChPN9YREZFeRWF/gKLF+Wzd2cLu1o6wSxEREekRhf0B2tuRv14d+SIi0kso7A9QVB35IiLSyyjsD9CYoblkZpjCXkREeg2F/QHKycpk9JABCnsREek1FPYHIRrJ1130RESk11DYH4RocT6VdXvo1II4IiLSCyjsD0I0kkdbRxfVO5rDLkVEROQDKewPgjryRUSkN1HYH4RShb2IiPQiCvuDUJjXjyG52axTk56IiPQCCvuDFI1oQRwREekdFPYHKXb5ncJeRERSn8L+IEWL86jb3UZDU1vYpYiIiLwvhf1B+kdHvs7bi4hIalPYH6S9HfmayhcRkVSnsD9Io4cMIDvTdGQvIiIpT2F/kLIyMxg7NE8d+SIikvISFvZmNtrMnjKzN81shZldHYwXmtljZrY2+DkkGDczu97MKszsNTOb0e295gf7rzWz+Ymq+UDp8jsREekNEnlk3wFc4+6TgTnA5WY2GfgW8IS7lwFPBL8DnAmUBY/LgBsg9scBcB0wG5gFXLf3D4SwRYvz2LS9ifbOrrBLERER2a+Ehb27b3H3l4Pnu4CVQAkwD7gt2O024GPB83nA7R6zFCgwsxHAGcBj7l7v7juAx4C5iar7QJQW5dPR5Wyqbwq7FBERkf1Kyjl7MxsLTAdeAIa5+5Zg01ZgWPC8BNjc7WVVwdj+xkMXLQ4uv6vRVL6IiKSuhIe9meUDfwK+6u47u29zdwfisii8mV1mZuVmVl5bWxuPt/xApZE8QNfai4hIakto2JtZNrGgv9Pd7wuGtwXT8wQ/a4LxamB0t5ePCsb2N/4O7r7A3We6+8xIJBLfL7Ifg/pnUzwwR016IiKS0hLZjW/AzcBKd/9Jt00PAns76ucDD3Qbvzjoyp8DNAbT/Y8Cp5vZkKAx7/RgLCWoI19ERFJdVgLf+1jgIuB1M3slGPs28APgXjP7HLAR+GSw7WHgLKACaAIuBXD3ejP7PrAs2O8/3L0+gXUfkNJIHn99bQvuTuzvGxERkdSSsLB39yXA/tLvlH3s78Dl+3mvhcDC+FUXP9FIPo3N7Wzf00ZRfk7Y5YiIiLyH7qD3IakjX0REUp3C/kOKqiNfRERSnML+Qxo5eAD9szPUpCciIilLYf8hZWQY44rytdStiIikLIV9HEQjeZrGFxGRlKWwj4NoJJ/NO5poae8MuxQREZH3UNjHQbQ4H3fYsF1H9yIiknoU9nHwdkd+jcJeRERSj8I+DsYVxcJeTXoiIpKKFPZxkNsvi5KCAbr8TkREUpLCPk5K1ZEvIiIpSmEfJ9FI7Fr72C3+RUREUofCPk6ixfnsaetk287WsEsRERF5B4V9nESL9t4jX+ftRUQktSjs4+Tt1e8U9iIikmIU9nFSPDCH/JwsLXUrIiIpR2EfJ2ame+SLiEhKUtjH0d6OfBERkVSisI+j0kgebzW2sKe1I+xSRERE3qawj6NoJNakt75OU/kiIpI6FPZxpI58ERFJRQr7ODpkaC4ZhjryRUQkpSjs4ygnK5Mxhbms0zS+iIikEIV9nJVG8nVkLyIiKUVhH2fRSB7r6/bQ2aUFcUREJDUo7OMsGsmntaOLtxqawy5FREQEUNjH3d6O/Ap15IuISIpQ2MfZ3mvtK3XbXBERSREK+zgrzOtHQW62rrUXEZGUobBPgKg68kVEJIUo7BNAq9+JiEgqUdgnQDSST93uVhqb28MuRURERGGfCP9o0tNUvoiIhE9hnwClkTwATeWLiEhKUNgnwOjCXLIzTR35IiKSEhT2CZCdmcEhQ/PUkS8iIilBYZ8gsY58hb2IiIRPYZ8g0Ug+m+qbaO/sCrsUERHp4xT2CVIayae909lc3xR2KSIi0scp7BMkqo58ERFJEQr7BCkNrrXXeXsREQmbwj5BBg/IJjIwRx35IiISuoSFvZktNLMaM3uj29g0M1tqZq+YWbmZzQrGzcyuN7MKM3vNzGZ0e818M1sbPOYnqt5EiEbyqKzTNL6IiIQrkUf2twJz3zX2Q+B77j4N+E7wO8CZQFnwuAy4AcDMCoHrgNnALOA6MxuSwJrjqjSST0XNbtw97FJERKQPS1jYu/tioP7dw8Cg4Plg4K3g+Tzgdo9ZChSY2QjgDOAxd6939x3AY7z3D4iUFY3k09jcTv2etrBLERGRPiwryZ/3VeBRM/sxsT80jgnGS4DN3farCsb2N/4eZnYZsVkBxowZE9+qD1L3jvyh+TkhVyMiIn1Vshv0vgx8zd1HA18Dbo7XG7v7Anef6e4zI5FIvN72Q4mqI19ERFJAssN+PnBf8PwPxM7DA1QDo7vtNyoY2994r1BSMICcrAwtdSsiIqFKdti/BZwQPD8ZWBs8fxC4OOjKnwM0uvsW4FHgdDMbEjTmnR6M9QoZGca4ojzdWEdEREKVsHP2ZnY3cCJQZGZVxLrqvwD83MyygBaCc+zAw8BZQAXQBFwK4O71ZvZ9YFmw33+4+7ub/lJatDifN6obwy5DRET6sISFvbtfuJ9NR+5jXwcu38/7LAQWxrG0pIpG8nnk9S20dnSSk5UZdjkiItIH6Q56CRaN5NHlsHG7FsQREZFwKOwT7O2OfN02V0REQqKwT7DSt6+1V9iLiEg4FPYJltsvi5GD+6sjX0REQqOwT4Jocb6O7EVEJDQK+ySIRvKprN2jBXFERCQUCvskiEby2N3aQc2u1rBLERGRPkhhnwSl6sgXEZEQKeyTQAviiIhImBT2STBsUA55/TLVkS8iIqFQ2CeBmakjX0REQqOwT5K9HfkiIiLJprBPktKiPKobmmlq6wi7FBER6WMU9kkSLY416enoXkREkk1hnyTqyBcRkbAo7JPkkKG5ZJiO7EVEJPkU9knSPzuT0YW5OrIXEZGkU9gnUTSSr2vtRUQk6RT2SVRalEdl7W66urQgjoiIJI/CPomixfm0dnRR3dAcdikiItKHKOyTaG9HfmWdpvJFRCR5FPZJFI3kAVr9TkREkkthn0SFef0oyM1WR76IiCSVwj6JzIzSojyFvYiIJJXCPsl0+Z2IiCSbwj7JosX51O5qZWdLe9iliIhIH6GwT7K3O/J1dC8iIkmisE8ydeSLiEiyKeyTbHRhLlkZpiY9ERFJGoV9kmVnZnDIUC2IIyIiyaOwD0E0kq9z9iIikjQK+xBEi/PZsH0PHZ1dYZciIiJ9gMI+BNFIPu2dzuYdWhBHREQST2EfglJ15IuISBIp7EMQLYpda68mPRERSQaFfQgG52ZTlJ+jJj0REUkKhX1IohEtiCMiIsmhsA9JtDhfYS8iIkmhsA9JaVEeO5raqd/TFnYpIiKS5hT2IYkW710QR0f3IiKSWAr7kIyPqCNfRESSI2Fhb2YLzazGzN541/iVZrbKzFaY2Q+7jV9rZhVmttrMzug2PjcYqzCzbyWq3mQbWTCAnKwM1qkjX0REEiwrge99K/BL4Pa9A2Z2EjAPmOrurWZWHIxPBi4ApgAjgcfNbELwsl8BpwFVwDIze9Dd30xg3UmRmWGMK8rTjXVERCThEnZk7+6Lgfp3DX8Z+IG7twb71ATj84B73L3V3dcDFcCs4FHh7pXu3gbcE+ybFqIRdeSLiEjiJfuc/QTgODN7wcwWmdlRwXgJsLnbflXB2P7G00I0ksfmHc20dnSGXYqIiKSxZId9FlAIzAG+AdxrZhaPNzazy8ys3MzKa2tr4/GWCRctzqezy9m0vSnsUkREJI0lO+yrgPs85kWgCygCqoHR3fYbFYztb/w93H2Bu89095mRSCQhxcdbVB35IiKSBMkO+z8DJwEEDXj9gDrgQeACM8sxs3FAGfAisAwoM7NxZtaPWBPfg0muOWHGFQWr36kjX0REEihh3fhmdjdwIlBkZlXAdcBCYGFwOV4bMN/dHVhhZvcCbwIdwOXu3hm8zxXAo0AmsNDdVySq5mTLy8lixOD+6sgXEZGESljYu/uF+9n0mf3s/1/Af+1j/GHg4TiWllKikXzW1enIXkREEkd30AtZNJJHZc1uYhMcIiIi8aewD1m0OJ9drR3U7moNuxQREUlTCvuQ7e3Ir1BHvoiIJIjCPmSlEXXki4hIYinsQzZ8UH9y+2VqqVsREUkYhX3IzCy4R76O7EVEJDEU9ikgGtHqdyIikjgK+xQQjeRT3dBMc5sWxBERkfhT2KeA0qAjv7JOR/ciIhJ/CvsUEC2OdeRX6ry9iIgkgMI+BYwdmoeZVr8TEZHEUNingP7ZmYwekquOfBERSQiFfYpQR76IiCSKwj5FlEbyqazbTVeXFsQREZH4UtiniGgkn5b2LrbsbAm7FBERSTMK+xQR3XuPfE3li4hInCnsU0S0OHatvTryRUQk3hT2KWJoXj8GD8hW2IuISNwp7FOEmVEayWNdjS6/ExGR+FLYp5Bo0JEvIiISTwr7FBKN5LNtZyu7WtrDLkVERNKIwj6F7O3I1z3yRUQknhT2KUQd+SIikggK+xQypjCXrAxT2IuISFwp7FNIdmYGY4bmahpfRETiSmGfYqKRfB3Zi4hIXCnsU0w0ks+GuiY6OrvCLkVERNKEwj7FRCN5tHV2UbWjOexSREQkTSjsU0xpJNaRr5vriIhIvCjsU8w/Vr9Tk56IiMSHwj7FFOT2oyi/n5r0REQkbhT2KahUHfkiIhJHCvsUFLv8TtP4IiISHwr7FBSN5FG/p40de9rCLkVERNKAwj4FRdWRLyIicaSwT0F7w76iRmEvIiIfnsI+BZUMGcDwQf353dJNdHV52OWIiEgvp7BPQZkZxrVnHcrr1Y384aXNYZcjIiK9nMI+RZ07dSRHjR3CD/+2msbm9rDLERGRXqxHYW9meWaWETyfYGbnmll2Ykvr28yM7547hR1Nbfzs8TVhlyMiIr1YT4/sFwP9zawE+DtwEXBrooqSmCkjB3PhrDHc/vxG1mzbFXY5IiLSS/U07M3dm4CPA792938GpiSuLNnrmtMnkp+Txff+sgJ3NeuJiMiB63HYm9nRwKeBh4KxzA94wUIzqzGzN/ax7RozczMr2vvmZna9mVWY2WtmNqPbvvPNbG3wmN/DetNGYV4/rjl9As9WbOfRFVvDLkdERHqhnob9V4FrgfvdfYWZlQJPfcBrbgXmvnvQzEYDpwObug2fCZQFj8uAG4J9C4HrgNnALOA6MxvSw5rTxqdmjeHQ4QP5/l9X0tLeGXY5IiLSy/Qo7N19kbuf6+7/GzTq1bn7VR/wmsVA/T42/RT4JtB9TnoecLvHLAUKzGwEcAbwmLvXu/sO4DH28QdEusvKzOC6c6ZQ3dDMbxZVhl2OiIj0Mj3txr/LzAaZWR7wBvCmmX3jQD/MzOYB1e7+6rs2lQDdLyivCsb2N97nHB0dykePGMGvn66gakdT2OWIiEgv0tNp/MnuvhP4GPAIMI5YR36PmVku8G3gOwdUYc/f/zIzKzez8tra2kR8ROj+7axJmMF/P7wy7FJERKQX6WnYZwfX1X8MeNDd23nnNHxPRIn9kfCqmW0ARgEvm9lwoBoY3W3fUcHY/sbfw90XuPtMd58ZiUQOsLTeYWTBAC4/cTwPv76V5yrqwi5HRER6iZ6G/W+ADUAesNjMDgF2HsgHufvr7l7s7mPdfSyxKfkZ7r4VeBC4OOjKnwM0uvsW4FHgdDMbEjTmnR6M9VlfOL6U0YUD+O5fVtDR2RV2OSIi0gv0tEHvencvcfezgia6jcBJ7/caM7sbeB6YaGZVZva599n9YaASqABuAr4SfG498H1gWfD4j2Csz+qfncm/f3Qya7bt5ndLN4ZdjoiI9ALWkxu1mNlgYpfAHR8MLSIWvI0JrO2gzZw508vLy8MuI2HcnYsXvsirmxt46usnMjQ/J+ySREQkZGb2krvP3Ne2nk7jLwR2AZ8MHjuBW+JTnhwoM+O6cybT1NbJj/+u++aLiMj762nYR939OnevDB7fA0oTWZi8v/HFA5l/zFjuWbaJN6pTcoJFRERSRE/DvtnMPrL3FzM7FmhOTEnSU1efWsbQvH5c96Dumy8iIvvX07D/EvArM9sQXDb3S+CLCatKemRQ/2y+OfdQXtq4gz+/ss8rEkVERHrcjf+qu08FjgCOcPfpwMkJrUx65BMzRjF11GD+5+FV7G7tCLscERFJQT09sgfA3XcGd9ID+JcE1CMHKCPD+O65U6jZ1covn6wIuxwREUlBBxT272Jxq0I+lOljhvCJI0dx85JK1tftCbscERFJMR8m7NURlkK+OXciOVmZfP+vb4ZdioiIpJj3DXsz22VmO/fx2AWMTFKN0gPFA/tz9SllPLmqhidXbQu7HBERSSHvG/buPtDdB+3jMdDds5JVpPTM/GPGUhrJ4z/+8iatHZ1hlyMiIiniw0zjS4rpl5XBdedMYcP2JhYu2RB2OSIikiIU9mnmhAkRTps8jF88uZZtO1vCLkdERFKAwj4N/b+PTqajy/nBI6vCLkVERFKAwj4NjRmay2XHlXL/8mrKN/TpFYFFRASFfdr6yklRhg/qz3f/soLOLl0lKSLSlyns01Ruvyy+/dFJvFG9k3vLN4ddjoiIhEhhn8bOOWIEs8YW8qNHV9PY1B52OSIiEhKFfRozM647dzINTW389PE1YZcjIiIhUdinuSkjB/Op2WO4Y+lGVm3d+cEvEBGRtKOw7wOuOW0iA/tn8b0H38RdzXoiIn2Nwr4PGJLXj2tOn8jzldt55I2tYZcjIiJJprDvIz41awyTRgzivx5aSXOb7psvItKXKOz7iMwM47vnTKa6oZkbF60LuxwREUkihX0fMrt0KOdMHcmNi9axub4p7HJERCRJFPZ9zLVnHkqGGf/10MqwSxERkSRR2PcxIwsGcPlJUf62YitL1taFXY6IiCSBwr4P+vxxpYwpzOV7f1lBe2dX2OWIiEiCKez7oP7Zmfy/syeztmY3dzy/MexyREQkwRT2fdSpk4o5fkKEnz6+hrrdrWGXIyIiCaSw76PMjO+cPZnmtk5+/OjqsMsREZEEUtj3YeOL87n02LH8vnwzr1U1hF2OiIgkiMK+j7vqlDKG5uVw3YMr6OrSffNFRNKRwr6PG9g/m3+dO5Hlmxq4f3l12OWIiEgCKOyFf5oxiqmjC/jB31axq6U97HJERCTOFPZCRobxvXOnULurla/9/lXaOnTtvYhIOlHYCwDTRhfwvXOn8PjKbXzlzpdo7dDKeCIi6UJhL2+bf8xYvj9vCo+vrOFLd7xES7sCX0QkHSjs5R0uOnos/33e4Ty1upYvKvBFRNKCwl7e41Ozx/CDjx/O4rW1fOH2cgW+iEgvp7CXfbpg1hj+95+OYElFHZ+7bRnNbQp8EZHeSmEv+/XJmaP50Sem8ty67Xz21mU0tXWEXZKIiBwEhb28r08cOYqffHIqL6zfzqW3LGNPqwJfRKS3SVjYm9lCM6sxsze6jf3IzFaZ2Wtmdr+ZFXTbdq2ZVZjZajM7o9v43GCswsy+lah6Zf/Omz6Kn54/jWUb6rnklhfZrcAXEelVEnlkfysw911jjwGHufsRwBrgWgAzmwxcAEwJXvNrM8s0s0zgV8CZwGTgwmBfSbJ500q4/sLpvLypgfkLX9Sd9kREepGEhb27Lwbq3zX2d3ffe1i4FBgVPJ8H3OPure6+HqgAZgWPCnevdPc24J5gXwnB2UeM5BcXTufVzQ1cvPBFdirwRUR6hTDP2X8WeCR4XgJs7ratKhjb37iE5KzDR/DLT83g9apGLrr5RRqbFfgiIqkulLA3s38DOoA74/iel5lZuZmV19bWxuttZR/mHjacX396Bm++1chFN79AY5MCX0QklSU97M3sEuBs4NPuvncB9WpgdLfdRgVj+xt/D3df4O4z3X1mJBKJe93yTqdPGc6NnzmSVVt28embl9LQ1BZ2SSIish9JDXszmwt8EzjX3Zu6bXoQuMDMcsxsHFAGvAgsA8rMbJyZ9SPWxPdgMmuW/Ttl0jB+c9GRrNm2m0/d9AI79ijwRURSUSIvvbsbeB6YaGZVZvY54JfAQOAxM3vFzG4EcPcVwL3Am8DfgMvdvTNo5rsCeBRYCdwb7Csp4qRDi7np4plU1O7mwpuWsn13a9gliYjIu9g/ZtLTx8yZM728vDzsMvqUZ9bW8vnbyhk7NI87vzCbovycsEsSEelTzOwld5+5r226g57ExXFlEW655Cg21u/hwgVLqd2lI3wRkVShsJe4OWZ8EbdcMouqHc1csOB5ana2hF2SiIigsJc4Ozo6lFsvPYotjS1csGAp2xT4IiKhU9hL3M0uHcptn53Ftp2xwN/aqMAXEQmTwl4S4qixhdz+uVnU7mrl/AXP81ZDc9gliYj0WQp7SZgjD4kFfv3uNs5f8DxVO5o++EUiIhJ3CntJqBljhnDH52fT0NTOBQuWsrlegS8ikmwKe0m4aaMLuPPzs9nZHAv8TdsV+CIiyaSwl6Q4YlQBd31hDrtbO7hgwfNs3L4n7JJERPoMhb0kzWElg7nrC7Npbu/k/N8sZX2dAl9EJBkU9pJUU0YO5q4vzKGts4sLFjzPutrdYZckIpL2FPaSdJNGDOLuL8yho9O5YMFSVm7ZGXZJIiJpTWEvoZg4fCD3XDYHAz72q2e564VNpOOiTCIiqUBhL6EpGzaQh646jlnjCvn2/a9zxd3L2dnSHtTTaqEAAB1mSURBVHZZIiJpR2EvoYoMzOG2S2fxzbkT+dsbWzn7+iW8VtUQdlkiImlFYS+hy8gwvnLieO794hw6Orv4pxue47fPVGpaX0QkThT2kjKOPKSQh68+jhMmFPOfD63kC7eXs2NPW9hliYj0egp7SSkFuf246eIjue6cySxeU8dZ1z/Dsg31YZclItKrKewl5ZgZlx47jj99+Rj6ZWVwwYKl/OqpCrq6NK0vInIwFPaSsg4fNZi/XvkRzjp8BD96dDUXL3yRml0tYZclItLrKOwlpQ3sn831F0zjBx8/nGUb6jnr50tYsrYu7LJERHoVhb2kPDPjglljePCKjzAkN5uLFr7Ajx9dTUdnV9iliYj0Cgp76TUmDh/IA1ccyz8fOYpfPlXBhTct5a2G5rDLEhFJeQp76VVy+2Xxw09M5ecXTOPNt3Zy1vXP8Pib28IuS0QkpSnspVeaN62Ev151HCUFA/j87eV8/69v0tahaX0RkX1R2EuvNa4oj/u+cgyXHDOWm5es5xM3PsfG7XvCLktEJOUo7KVXy8nK5LvnTuHGz8xgQ90ezr5+CX997a2wyxIRSSkKe0kLcw8bwUNXHcf4Yflccddyvn3/67S0d4ZdlohISlDYS9oYXZjLvV88mi+eUMpdL2ziY796loqaXWGXJSISOoW9pJXszAyuPXMSt156FDW7WjnnF8/yh/LNWkFPRPo0hb2kpRMnFvPI1ccxdfRgvvHH17jm3lfZ09oRdlkiIqFQ2EvaGjaoP3d+fg5fPbWMP79SzTm/WMKKtxrDLktEJOkU9pLWMjOMr546gTs/P4fdrR2c9+vnuOP5DZrWF5E+RWEvfcLR0aE8cvVxHBMdyv97YAUXLFjKq5sbwi5LRCQpFPbSZwzNz2Hh/KP4/scOo6JmN/N+9SxX3PWybsQjImnP0nE6c+bMmV5eXh52GZLCdrW0c9PiSm56Zj0dXV18Zs4hXHlyGYV5/cIuTUTkoJjZS+4+c5/bFPbSl23b2cLPHl/D75dtJq9fFl8+Kcpnjx1H/+zMsEsTETkg7xf2msaXPm3YoP78z8eP4NGvHs/s0kJ++LfVnPTjp/lD+WY6u9LvD2ER6ZsU9iJA2bCB/Hb+Udz9hTlEBubwjT++xkevf4ZFa2rVuS8ivZ7CXqSbo6ND+fNXjuUXF05nT1sH8xe+yEU3v8gb1bo+X0R6L4W9yLtkZBjnTB3J4/9yAt85ezJvvNXI2b9Ywtd+/wpVO5rCLk9E5ICpQU/kAzQ2t3PD0+tY+Ox6cLjk2LFcfuJ4Budmh12aiMjbQmnQM7OFZlZjZm90Gys0s8fMbG3wc0gwbmZ2vZlVmNlrZjaj22vmB/uvNbP5iapXZH8GD8jmW2ceylNfP5Fzpo7kpmcqOf5HT/HbZypp7dAyuiKS+hI5jX8rMPddY98CnnD3MuCJ4HeAM4Gy4HEZcAPE/jgArgNmA7OA6/b+gSCSbCUFA/i/T07loSuPY+roAv7zoZWc8n+LeOCVarrUuS8iKSxhYe/ui4H6dw3PA24Lnt8GfKzb+O0esxQoMLMRwBnAY+5e7+47gMd47x8QIkk1eeQgbv/sLO743CwG9c/m6nte4dxfLeG5irqwSxMR2adkN+gNc/ctwfOtwLDgeQmwudt+VcHY/sbfw8wuM7NyMyuvra2Nb9Ui+3BcWYS/XvkRfvLJqdTvbuNTv32BS255kVVbd4ZdmojIO4TWje+xzsC4zX26+wJ3n+nuMyORSLzeVuR9ZWQYH58xiie/fiLXnnkoL23cwZk/f4Zv/OFVtjQ2h12eiAiQ/LDfFkzPE/ysCcargdHd9hsVjO1vXCSl9M/O5IsnRFn8jZP43LHjeOCVtzjpx0/zo0dXsbOlPezyRKSPS3bYPwjs7aifDzzQbfzioCt/DtAYTPc/CpxuZkOCxrzTgzGRlDQkrx//fvZknrjmBM6YMpxfPbWOE3/0NLc+u562jq6wyxORPiph19mb2d3AiUARsI1YV/2fgXuBMcBG4JPuXm9mBvySWPNdE3Cpu5cH7/NZ4NvB2/6Xu9/yQZ+t6+wlVbxe1ch/P7yS5yu3U1IwgKtOGc/HZ4wiO1P3sxKR+NKqdyIhcncWranlJ4+t4bWqRg4ZmsvVp5Qxb1oJmRkWdnkikia06p1IiMyMEycW88Dlx3LTxTPJ7ZfFv9z7Kqf9dBEPvvqWrtEXkYRT2IskiZlx2uRhPHTlR7jh0zPIyjCuuns5Z/78Gf72xhatriciCaOwF0myjAzjzMNH8MjVx/PzC6bR3tnFl373Mmf/YglPrNym0BeRuFPYi4QkM8OYN62Ev3/teP7vn6eyq6WDz91Wznm/fo7Fa2oV+iISN2rQE0kR7Z1d/OmlKn7xZAXVDc0cNXYIXzttAsdEi8IuTUR6AXXji/QirR2d3LtsM798qoJtO1s5unQo15w+gZljC8MuTURSmMJepBdqae/krhc28eun11G3u5XjJ0T4l9MmMG10QdiliUgKUtiL9GJNbR3c8fxGbly0jh1N7ZxyaDFfO20Ch5UMDrs0EUkhCnuRNLC7tYNbn13PgsWV7GzpYO6U4XzttAlMHD4w7NJEJAUo7EXSyM6Wdm5+Zj0Ll6xnd1sHZx8xkq+eWkY0kh92aSISIoW9SBpqaGpjweJKbn1uAy3tnXxseglXn1LGIUPzwi5NREKgsBdJY9t3t/KbxZXc/vwG2judT8wYxZWnjGfUkNywSxORJFLYi/QBNTtb+PXT67jrhU04zvlHjeaSY8YxvljT+yJ9gcJepA/Z0tjML5+s4N7yzbR3OkeMGsx500s4Z+pIivJzwi5PRBJEYS/SB9XsauEvr27h/uVVvFG9k8wM4/iyIs6bMYrTJg1jQL/MsEsUkThS2Iv0cWu37eK+5dU8sLyatxpbyM/JYu5hw/n49BJmlw4lM8PCLlFEPiSFvYgA0NXlvLC+nvuXV/HI61vZ1drB8EH9mTd9JOdNL+HQ4YPCLlFEDpLCXkTeo6W9k8dXbuPPy6t5enUtHV3OpBGD+Pj0Es6dNpJhg/qHXaKIHACFvYi8r+27W/nra1u4f3k1r2xuIMPg2PFFnDe9hDOmDCcvJyvsEkXkAyjsRaTHKmt38+fl1dz/SjWb65sZkJ3JGVOGcd6MURwbHUpWZkbYJYrIPijsReSAuTsvbdzBfcureei1LTQ2txMZmMO5U2Pn96eMHISZGvtEUoXCXkQ+lNaOTp5aVcv9y6t4clUN7Z1OWXE+580o4WPTShhZMCDsEkX6PIW9iMRNQ1MbD72+hftfrqZ84w7MYPa4Qj4+fRRnHj6cgf2zwy5RpE9S2ItIQmza3sSfX6nm/uXVrK/bQ05WBv88cxRfPnE8JTraF0kqhb2IJJS788rmBn6/bDN/erkKgH+eOZqvnBjVgjwiSaKwF5GkqW5o5oanK7h3WRWO84kjR/GVE8czulChL5JICnsRSbq3Gpq5cdE67nlxM13ufHxGCVecVMaYoQp9kURQ2ItIaLY2tnDjonXc9eImOruc86aXcMVJ4xlblBd2aSJpRWEvIqHbtjMI/Rc20dHlzJs2kitPLmOcQl8kLhT2IpIyana28JvFldz5wkbaOrqYN62EK04eTzSSH3ZpIr2awl5EUk7trlYWLF7HHUtjoX/O1JFcefJ4xhcPDLs0kV5JYS8iKatudys3La7k9uc30tLRydlHjOSqk8dTNkyhL3IgFPYikvK2727lpmfWc/vzG2hu7+Ssw0dw1cllTByu0BfpCYW9iPQa9XvauHlJJbc+u4E9bZ2cdfhwrjy5jEkjBoVdmkhKU9iLSK/T0NTGzUvWc8uzG9jd2sEZU4Zx1SllTBk5OOzSRFKSwl5Eeq3GpnZufnY9tyxZz67WDk6bPIyrTynjsBKFvkh3CnsR6fUam9u55dn1LFyynp0tHZw6qZirT5nA4aMU+iKgsBeRNLKzpZ1bn93AzUvW09jczsmHFvOJI0dxXFmRlteVPk1hLyJpZ1dLO7c9Fwv9HU3tZGUYR40t5ORDiznp0GKikTzMLOwyRZJGYS8iaaujs4uXNu7gydU1PLWqhjXbdgMwpjD37eCfPa6Q/tmZIVcqklgKexHpM6p2NPHUqhqeXFXDc+u209rRxYDsTI4dX8TJhxZz8qHFDB/cP+wyReIu5cLezL4GfB5w4HXgUmAEcA8wFHgJuMjd28wsB7gdOBLYDpzv7hve7/0V9iIC0NzWyfOVdTy5qoanVtVS3dAMwKQRgzj50AgnH1rMtNFDyMzQdL/0fikV9mZWAiwBJrt7s5ndCzwMnAXc5+73mNmNwKvufoOZfQU4wt2/ZGYXAOe5+/nv9xkKexF5N3dnzbbdQfDX8NKmHXR2OUNyszlhQoSTDi3mhAkRCnL7hV2qyEF5v7DPSnYx3T53gJm1A7nAFuBk4FPB9tuA7wI3APOC5wB/BH5pZubpeP5BRBLGzJg4fCAThw/kyydGaWxqZ9HaWp5aVcPTq2v48ytvkWFw5CFDOCmY7p84bKCa/CQtJD3s3b3azH4MbAKagb8Tm7ZvcPeOYLcqoCR4XgJsDl7bYWaNxKb665JauIiklcG52Zw7dSTnTh1JZ5fzyuaGt8/1//Bvq/nh31ZTUjCAEyfGpvuPiRYxoJ+a/KR3SnrYm9kQYkfr44AG4A/A3Di872XAZQBjxoz5sG8nIn1IZoZx5CFDOPKQIXz9jIlsbWzhqdWx4L9/eTV3vrCJnKwMjo4OjXX4TyxmdGFu2GWL9FgY0/inAuvdvRbAzO4DjgUKzCwrOLofBVQH+1cDo4EqM8sCBhNr1HsHd18ALIDYOfuEfwsRSVvDB/fnwlljuHDWGFo7Onmhsj52rn91Dd95YAWwguPKivjyiVGOLh2qqX5JeWGE/SZgjpnlEpvGPwUoB54CPkGsI38+8ECw/4PB788H25/U+XoRSZacrEyOnxDh+AkRrvPJVNbt4ZHXt3Drcxv51E0vMHXUYL58YpTTJw8nQ139kqLCuvTue8D5QAewnNhleCXEgr4wGPuMu7eaWX/gDmA6UA9c4O6V7/f+6sYXkURrae/kTy9XsWBxJRu3N1EayeNLx0eZN30kOVk6ty/Jl1KX3iWDwl5EkqWzy3nkjS3c8PQ6Vry1k2GDcvj8R0q5cPYY8nPCuuBJ+iKFvYhIgrk7SyrquOHpdTy3bjuD+mdx8dFjueTYsRTl54RdnvQBCnsRkSR6dXMDNy5ax99WbKVfZgafnDmay44vVQe/JJTCXkQkBOtqd7NgUSX3La+iy+Gjh4/gSydEmTxyUNilSRpS2IuIhGhrYwsLn13PnUs3sqetkxMnRvjSCVFmjyvUZXsSNwp7EZEU0NjUzu9e2MjCJevZvqeN6WMK+NIJUU6bNEyX7cmHprAXEUkhLe2d/OGlKhYsXsfm+maikTy+dEKUedNK6JeVEXZ50ksp7EVEUlBHZxcPv7GVG55ex8otOxkxuD+f+8g4Lpw1hjxdticHSGEvIpLC3J1Fa2q5cdE6llbWM3hANvOPPoT5x4xlqC7bkx5S2IuI9BLLN+3gxkXreHTFNvpnZ3D+zNF8/jhdticfTGEvItLLVNTs4jeLKvnzK9V0OZxzxAjmHjaCo8YO0dG+7JPCXkSkl9rS2MzNz6zn7hc3saetE4DxxfnMGlfIrLGFzBpXyMiCASFXKalAYS8i0su1dXTxenUjL66v58X12ynfuINdLR0AlBQMYPa4Qo4aFwv/0qI8Xb/fBynsRUTSTGeXs2rrTpatr+fFDfW8uL6eut1tABTl9+Oo4Kj/qLGFTBoxiExdx5/2FPYiImnO3Vlftyd25B+Ef9WOZgAG5mRx5NghzBpXyOxxhRxeUqDr+dOQwl5EpA96q6GZZRvqeWF9PcvW17O2ZjcAOVkZTB9TEJzzH8qMQwrI7afr+ns7hb2IiLB9dyvLNuxgWXDkv+KtRrocMjOMw0oGx877jy3kqLFDKMjtF3a5coAU9iIi8h67Wtp5eVMDL67fzrL1O3hlcwNtnV0ATBw2kFnjCjl2fBFHR4cyeEB2yNXKB1HYi4jIB2pp7+TVzQ1vT/2/vHEHe9o6ycwwpo4azHFlEY6fUMTUUQVkZeqcf6pR2IuIyAFr7+xi+aYGnllby+K1dbxW1YB7rOHvmPFDY+FfFmHMUN3dLxUo7EVE5ENraGrjuXXbY+G/po7qhli3/5jCXI4rK+K4sgjHjB/KoP6a8g+Dwl5EROJq76V+z6yt45m1tTy/bvvbU/7TRhe8Hf5TRw3WlH+SKOxFRCSh2jq6WL5pB0sq6t455d8/i2OimvJPBoW9iIgk1Y49/5jyf2btP6b8Dxn6jyn/o6Oa8o8nhb2IiITG3ams28OSfUz5Tx9dwHFlET5SVqQp/w9JYS8iIilj75T/3vP9r1U3vj3lf2y0iJMPLeakQ4uJDNRSvgdCYS8iIimr+5T/ojW1bGlswQymjS7g1EnDOG3yMMqK87WS3wdQ2IuISK/g7ry5ZSdPrKzh8ZXbeK2qEYDRhQM4ddIwTp00jFnjCsnWdP97KOxFRKRX2raz5e3gf7aijtaOLgb2z+KECRFOmzyMEycUMzhXTX6gsBcRkTTQ1NbBkrV1PLGyhidWbaNudxuZGcZRY4e8fdQ/tigv7DJDo7AXEZG00tXlvFrVwOMrt/H4mzWs3rYLgGgkj1MnD+O0ScOYPmYImRl95zy/wl5ERNLa5vomHl+5jSdW1rC0cjsdXU5hXj9OmljMqZOKOW5ChPycrLDLTCiFvYiI9Bk7W9pZvKaWx9/cxlOra2lsbqdfZgZzokM5bVIxp0waxsiCAWGXGXcKexER6ZM6Orso37iDx9/cxuMrt7FhexMAk0cM4tTJwzh1UjGHjRxMRhpM9yvsRUSkz3N31tXu4YmVseB/aeMOuhyGDcrhhAkRTphQzEfGF/Xa7n6FvYiIyLvU72njqVWxzv4la+vY2dJBRnAznxMmFHPCxAiHlwzuNU1+CnsREZH30dHZxatVDSxaHbuL395b+A7JzeYjZRFOmBDh+LIiigf1D7vU/VLYi4iIHID6PW1v37538Zo66na3AjBpxKBgyj/CkYcMoV9W6tzJT2EvIiJykLq6nJVbd7JoTS2LVtfy0sYddHQ5ef0yOTpaxAkTI5w4IcLowtxQ61TYi4iIxMmulnaeX7c9Fv5raqna0QxAaVEexwdH/XNKhzKgX2ZS61LYi4iIJIC7U1m3h8VB8C+t3E5Lexf9sjKYPa6Q48sinDAxkpRV+xT2IiIiSdDS3smyDfVvN/qtrdkNwIjB/WNNfhMiHDu+iMED4n95n8JeREQkBNUNzSxeU8viNbUsWVvHrtYOMjOM6aML+ObcQ5k1rjBun/V+YR/KjYLNrAD4LXAY4MBngdXA74GxwAbgk+6+w2LzHj8HzgKagEvc/eUQyhYRETkgJQUDuHDWGC6cNYb2zi5e2Ry7vG/x2lr6Zyevkz+UI3szuw14xt1/a2b9gFzg20C9u//AzL4FDHH3fzWzs4AriYX9bODn7j77/d5fR/YiItLXvN+RfdIvEDSzwcDxwM0A7t7m7g3APOC2YLfbgI8Fz+cBt3vMUqDAzEYkuWwREZFeK4y7AYwDaoFbzGy5mf3WzPKAYe6+JdhnKzAseF4CbO72+qpgTERERHogjLDPAmYAN7j7dGAP8K3uO3js3MIBnV8ws8vMrNzMymtra+NWrIiISG8XRthXAVXu/kLw+x+Jhf+2vdPzwc+aYHs1MLrb60cFY+/g7gvcfaa7z4xEIgkrXkREpLdJeti7+1Zgs5lNDIZOAd4EHgTmB2PzgQeC5w8CF1vMHKCx23S/iIiIfIBQLr0j1l1/Z9CJXwlcSuwPj3vN7HPARuCTwb4PE+vEryB26d2lyS9XRESk9wol7N39FWBflwecso99Hbg84UWJiIikqdRZm09EREQSQmEvIiKS5hT2IiIiaU5hLyIikuYU9iIiImlOYS8iIpLmFPYiIiJpTmEvIiKS5hT2IiIiaU5hLyIikuYU9iIiImnOYreeTy9mVktsMZ3ergioC7uIJND3TC/6nulF37P3OMTd97nGe1qGfbows3J339eCQWlF3zO96HumF33P9KBpfBERkTSnsBcREUlzCvvUtiDsApJE3zO96HumF33PNKBz9iIiImlOR/YiIiJpTmGfYsxstJk9ZWZvmtkKM7s67JoSycwyzWy5mf017FoSxcwKzOyPZrbKzFaa2dFh15QIZva14L/ZN8zsbjPrH3ZN8WBmC82sxsze6DZWaGaPmdna4OeQMGuMh/18zx8F/92+Zmb3m1lBmDXGw76+Z7dt15iZm1lRGLUlksI+9XQA17j7ZGAOcLmZTQ65pkS6GlgZdhEJ9nPgb+5+KDCVNPy+ZlYCXAXMdPfDgEzggnCriptbgbnvGvsW8IS7lwFPBL/3drfy3u/5GHCYux8BrAGuTXZRCXAr7/2emNlo4HRgU7ILSgaFfYpx9y3u/nLwfBexYCgJt6rEMLNRwEeB34ZdS6KY2WDgeOBmAHdvc/eGcKtKmCxggJllAbnAWyHXExfuvhiof9fwPOC24PltwMeSWlQC7Ot7uvvf3b0j+HUpMCrphcXZfv73BPgp8E0gLRvZFPYpzMzGAtOBF8KtJGF+Ruz/XF1hF5JA44Ba4JbgdMVvzSwv7KLizd2rgR8TOyraAjS6+9/DrSqhhrn7luD5VmBYmMUkyWeBR8IuIhHMbB5Q7e6vhl1LoijsU5SZ5QN/Ar7q7jvDrifezOxsoMbdXwq7lgTLAmYAN7j7dGAP6THl+w7BOet5xP64GQnkmdlnwq0qOTx2SVNaHg3uZWb/RuwU451h1xJvZpYLfBv4Tti1JJLCPgWZWTaxoL/T3e8Lu54EORY418w2APcAJ5vZ78ItKSGqgCp33zs780di4Z9uTgXWu3utu7cD9wHHhFxTIm0zsxEAwc+akOtJGDO7BDgb+LSn57XaUWJ/pL4a/Hs0CnjZzIaHWlWcKexTjJkZsfO7K939J2HXkyjufq27j3L3scQauZ5097Q7EnT3rcBmM5sYDJ0CvBliSYmyCZhjZrnBf8OnkIaNiN08CMwPns8HHgixloQxs7nETrWd6+5NYdeTCO7+ursXu/vY4N+jKmBG8P/dtKGwTz3HAhcRO9J9JXicFXZR8qFcCdxpZq8B04D/DrmeuAtmLv4IvAy8TuzflrS4I5mZ3Q08D0w0syoz+xzwA+A0M1tLbFbjB2HWGA/7+Z6/BAYCjwX/Ft0YapFxsJ/vmfZ0Bz0REZE0pyN7ERGRNKewFxERSXMKexERkTSnsBcREUlzCnsREZE0p7AXkbeZWWe3Sz5fMbO43e3PzMbua6UxEUm8rLALEJGU0uzu08IuQkTiS0f2IvKBzGyDmf3QzF43sxfNbHwwPtbMngzWO3/CzMYE48OC9c9fDR57b52baWY3Beve/93MBgT7X2Vmbwbvc09IX1MkbSnsRaS7Ae+axj+/27ZGdz+c2F3VfhaM/QK4LVjv/E7g+mD8emDR/2/v/l2yiuI4jr+/SkMgSNQiJLQ4OUTgX9Da2CDi1FaDNIn9Af0FgouLi3+AoyDREOTg0tIabQo6ODzLg8jH4TnVhXxIQxGu79dyz/kO594zfe8598c3yXNGtQC+t/gcsJFkHjgFXrf4B+BFG+ftbU1Ouq/8g56k36pqkGTqkvhP4GWSH61Q01GSx1V1AswkOWvxwyRPquoYeJpk2BnjGbCXZK7114AHST5W1S4wAHaAnSSDW56qdK+4spd0VRnTvo5hp33On/eGXgEbjHYBDqrK94mkG2Syl3RVi53jfmt/ZVS1EGAZ+NLan4B3AFU1WVXT4watqglgNslnYA2YBv7aXZD0/7x7ltT1sKq+dfq7SX59fveoVe4bAksttgJsVdUqcAy8afH3wGarKHbOKPEfjjnnJLDdbggKWE9yemMzkuQze0n/1p7ZLyQ5uetrkXR9buNLktRzruwlSeo5V/aSJPWcyV6SpJ4z2UuS1HMme0mSes5kL0lSz5nsJUnquQsTPT2H3kHHIAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "document = DocumentAssembler()\\\n",
        "        .setInputCol(\"text\")\\\n",
        "        .setOutputCol(\"document\")\n",
        "\n",
        "sentence = SentenceDetector()\\\n",
        "        .setInputCols(['document'])\\\n",
        "        .setOutputCol('sentence')\n",
        "\n",
        "token = Tokenizer()\\\n",
        "        .setInputCols(['sentence'])\\\n",
        "        .setOutputCol('token')\n",
        "\n",
        "bert_embedding = BertEmbeddings.pretrained('bert_base_cased', 'en')\\\n",
        "   .setInputCols([\"sentence\",'token'])\\\n",
        "   .setOutputCol(\"bert_embeddings\")\\\n",
        "   .setCaseSensitive(True)\n",
        "  \n",
        "# load trained model\n",
        "loaded_ner_model = NerDLModel.load(\"/content/drive/MyDrive/umass_ner/model_ner_dl_3\")\\\n",
        "        .setInputCols([\"sentence\", \"token\", \"bert_embeddings\"])\\\n",
        "        .setOutputCol(\"ner\")\n",
        "\n",
        "converter = NerConverter()\\\n",
        "        .setInputCols([\"document\", \"token\", \"ner\"])\\\n",
        "        .setOutputCol(\"ner_span\")\n",
        "\n",
        "ner_prediction_pipeline = Pipeline(stages = [\n",
        "        document,\n",
        "        sentence,\n",
        "        token,\n",
        "        bert_embedding,\n",
        "        loaded_ner_model,\n",
        "        converter\n",
        "])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OnRBRKn0DYZV",
        "outputId": "ae491e25-381c-4ee6-8fd7-fdf640e8c402"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert_base_cased download started this may take some time.\n",
            "Approximate size to download 389.1 MB\n",
            "[OK!]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "empty_data = spark.createDataFrame([['']]).toDF(\"text\")\n",
        "\n",
        "prediction_model = ner_prediction_pipeline.fit(empty_data)\n",
        "\n"
      ],
      "metadata": {
        "id": "xWpzQ3AxAL_D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_df= prediction_model.transform(test_data)"
      ],
      "metadata": {
        "id": "8JRzKgiJCr5p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sparknlp.base import LightPipeline\n",
        "\n",
        "light_model = LightPipeline(prediction_model)\n"
      ],
      "metadata": {
        "id": "_TJyi_EK_7rB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# file = 415\n",
        "text = \"\"\"SOCIAL HISTORY:  The patient quit smoking cigarettes 15 years\n",
        "ago.  She has a history of heavy alcohol consumption in the\n",
        "past, but none for the past 13 years.  She is a former\n",
        "employee of the state lottery.\"\"\""
      ],
      "metadata": {
        "id": "chls97aKDdnF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "result = light_model.fullAnnotate(text)\n",
        "\n",
        "ner_df= pd.DataFrame([(int(x.metadata['sentence']), x.result, x.begin, x.end, y.result) for x,y in zip(result[0][\"token\"], result[0][\"ner\"])], \n",
        "                      columns=['sent_id','token','start','end','ner'])\n",
        "ner_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0PQRL6UO8Eia",
        "outputId": "49da8500-7a19-4b1e-e87e-68dd703ce8b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sent_id        token  start  end             ner\n",
              "0         0       SOCIAL      0    5               O\n",
              "1         0      HISTORY      7   13               O\n",
              "2         0            :     14   14               O\n",
              "3         0          The     17   19               O\n",
              "4         0      patient     21   27               O\n",
              "5         0         quit     29   32    B-StatusTime\n",
              "6         0      smoking     34   40               O\n",
              "7         0   cigarettes     42   51               O\n",
              "8         0           15     53   54               O\n",
              "9         0        years     56   60               O\n",
              "10        0          ago     62   64               O\n",
              "11        0            .     65   65               O\n",
              "12        1          She     68   70               O\n",
              "13        1          has     72   74               O\n",
              "14        1            a     76   76               O\n",
              "15        1      history     78   84               O\n",
              "16        1           of     86   87               O\n",
              "17        1        heavy     89   93               O\n",
              "18        1      alcohol     95  101               O\n",
              "19        1  consumption    103  113               O\n",
              "20        1           in    115  116               O\n",
              "21        1          the    118  120               O\n",
              "22        1         past    122  125               O\n",
              "23        1            ,    126  126               O\n",
              "24        1          but    128  130               O\n",
              "25        1         none    132  135               O\n",
              "26        1          for    137  139               O\n",
              "27        1          the    141  143               O\n",
              "28        1         past    145  148               O\n",
              "29        1           13    150  151               O\n",
              "30        1        years    153  157               O\n",
              "31        1            .    158  158               O\n",
              "32        2          She    161  163               O\n",
              "33        2           is    165  166               O\n",
              "34        2            a    168  168  B-StatusEmploy\n",
              "35        2       former    170  175  B-StatusEmploy\n",
              "36        2     employee    177  184  I-StatusEmploy\n",
              "37        2           of    186  187               O\n",
              "38        2          the    189  191               O\n",
              "39        2        state    193  197               O\n",
              "40        2      lottery    199  205               O\n",
              "41        2            .    206  206               O"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da803685-c53e-48a4-b2b3-a60f906087c6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sent_id</th>\n",
              "      <th>token</th>\n",
              "      <th>start</th>\n",
              "      <th>end</th>\n",
              "      <th>ner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>SOCIAL</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>HISTORY</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>:</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>The</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>patient</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>quit</td>\n",
              "      <td>29</td>\n",
              "      <td>32</td>\n",
              "      <td>B-StatusTime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>smoking</td>\n",
              "      <td>34</td>\n",
              "      <td>40</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>cigarettes</td>\n",
              "      <td>42</td>\n",
              "      <td>51</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>53</td>\n",
              "      <td>54</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>years</td>\n",
              "      <td>56</td>\n",
              "      <td>60</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>ago</td>\n",
              "      <td>62</td>\n",
              "      <td>64</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>.</td>\n",
              "      <td>65</td>\n",
              "      <td>65</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>She</td>\n",
              "      <td>68</td>\n",
              "      <td>70</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>has</td>\n",
              "      <td>72</td>\n",
              "      <td>74</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>76</td>\n",
              "      <td>76</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>history</td>\n",
              "      <td>78</td>\n",
              "      <td>84</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>of</td>\n",
              "      <td>86</td>\n",
              "      <td>87</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>heavy</td>\n",
              "      <td>89</td>\n",
              "      <td>93</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>alcohol</td>\n",
              "      <td>95</td>\n",
              "      <td>101</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>consumption</td>\n",
              "      <td>103</td>\n",
              "      <td>113</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>in</td>\n",
              "      <td>115</td>\n",
              "      <td>116</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>the</td>\n",
              "      <td>118</td>\n",
              "      <td>120</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>past</td>\n",
              "      <td>122</td>\n",
              "      <td>125</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>,</td>\n",
              "      <td>126</td>\n",
              "      <td>126</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>but</td>\n",
              "      <td>128</td>\n",
              "      <td>130</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>none</td>\n",
              "      <td>132</td>\n",
              "      <td>135</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>for</td>\n",
              "      <td>137</td>\n",
              "      <td>139</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>the</td>\n",
              "      <td>141</td>\n",
              "      <td>143</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>past</td>\n",
              "      <td>145</td>\n",
              "      <td>148</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>150</td>\n",
              "      <td>151</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1</td>\n",
              "      <td>years</td>\n",
              "      <td>153</td>\n",
              "      <td>157</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1</td>\n",
              "      <td>.</td>\n",
              "      <td>158</td>\n",
              "      <td>158</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2</td>\n",
              "      <td>She</td>\n",
              "      <td>161</td>\n",
              "      <td>163</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2</td>\n",
              "      <td>is</td>\n",
              "      <td>165</td>\n",
              "      <td>166</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2</td>\n",
              "      <td>a</td>\n",
              "      <td>168</td>\n",
              "      <td>168</td>\n",
              "      <td>B-StatusEmploy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2</td>\n",
              "      <td>former</td>\n",
              "      <td>170</td>\n",
              "      <td>175</td>\n",
              "      <td>B-StatusEmploy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2</td>\n",
              "      <td>employee</td>\n",
              "      <td>177</td>\n",
              "      <td>184</td>\n",
              "      <td>I-StatusEmploy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2</td>\n",
              "      <td>of</td>\n",
              "      <td>186</td>\n",
              "      <td>187</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2</td>\n",
              "      <td>the</td>\n",
              "      <td>189</td>\n",
              "      <td>191</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2</td>\n",
              "      <td>state</td>\n",
              "      <td>193</td>\n",
              "      <td>197</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2</td>\n",
              "      <td>lottery</td>\n",
              "      <td>199</td>\n",
              "      <td>205</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2</td>\n",
              "      <td>.</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da803685-c53e-48a4-b2b3-a60f906087c6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-da803685-c53e-48a4-b2b3-a60f906087c6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-da803685-c53e-48a4-b2b3-a60f906087c6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sparknlp_display import NerVisualizer\n",
        "\n",
        "visualiser = NerVisualizer()\n",
        "\n",
        "visualiser.display(result[0], label_col='ner_span', document_col='document', save_path=\"display_result.html\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "5Ml7tlqCDqMz",
        "outputId": "47cea545-7a53-4c49-a23f-8d330215e78c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n",
              "    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n",
              "    \n",
              "    .spark-nlp-display-scroll-entities {\n",
              "        border: 1px solid #E7EDF0;\n",
              "        border-radius: 3px;\n",
              "        text-align: justify;\n",
              "        \n",
              "    }\n",
              "    .spark-nlp-display-scroll-entities span {  \n",
              "        font-size: 14px;\n",
              "        line-height: 24px;\n",
              "        color: #536B76;\n",
              "        font-family: 'Montserrat', sans-serif !important;\n",
              "    }\n",
              "    \n",
              "    .spark-nlp-display-entity-wrapper{\n",
              "    \n",
              "        display: inline-grid;\n",
              "        text-align: center;\n",
              "        border-radius: 4px;\n",
              "        margin: 0 2px 5px 2px;\n",
              "        padding: 1px\n",
              "    }\n",
              "    .spark-nlp-display-entity-name{\n",
              "        font-size: 14px;\n",
              "        line-height: 24px;\n",
              "        font-family: 'Montserrat', sans-serif !important;\n",
              "        \n",
              "        background: #f1f2f3;\n",
              "        border-width: medium;\n",
              "        text-align: center;\n",
              "        \n",
              "        font-weight: 400;\n",
              "        \n",
              "        border-radius: 5px;\n",
              "        padding: 2px 5px;\n",
              "        display: block;\n",
              "        margin: 3px 2px;\n",
              "    \n",
              "    }\n",
              "    .spark-nlp-display-entity-type{\n",
              "        font-size: 14px;\n",
              "        line-height: 24px;\n",
              "        color: #ffffff;\n",
              "        font-family: 'Montserrat', sans-serif !important;\n",
              "        \n",
              "        text-transform: uppercase;\n",
              "        \n",
              "        font-weight: 500;\n",
              "\n",
              "        display: block;\n",
              "        padding: 3px 5px;\n",
              "    }\n",
              "    \n",
              "    .spark-nlp-display-entity-resolution{\n",
              "        font-size: 14px;\n",
              "        line-height: 24px;\n",
              "        color: #ffffff;\n",
              "        font-family: 'Vistol Regular', sans-serif !important;\n",
              "        \n",
              "        text-transform: uppercase;\n",
              "        \n",
              "        font-weight: 500;\n",
              "\n",
              "        display: block;\n",
              "        padding: 3px 5px;\n",
              "    }\n",
              "    \n",
              "    .spark-nlp-display-others{\n",
              "        font-size: 14px;\n",
              "        line-height: 24px;\n",
              "        font-family: 'Montserrat', sans-serif !important;\n",
              "        \n",
              "        font-weight: 400;\n",
              "    }\n",
              "\n",
              "</style>\n",
              " <span class=\"spark-nlp-display-others\" style=\"background-color: white\">SOCIAL HISTORY:  The patient </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #626306\"><span class=\"spark-nlp-display-entity-name\">quit </span><span class=\"spark-nlp-display-entity-type\">StatusTime</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> smoking cigarettes 15 years<br>ago.  She has a history of heavy alcohol consumption in the<br>past, but none for the past 13 years.  She is </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #A13962\"><span class=\"spark-nlp-display-entity-name\">a </span><span class=\"spark-nlp-display-entity-type\">StatusEmploy</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #A13962\"><span class=\"spark-nlp-display-entity-name\">former<br>employee </span><span class=\"spark-nlp-display-entity-type\">StatusEmploy</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> of the state lottery.</span></div>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AIWqtqyb5RFU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}