NER_Model2.ipynb

{
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  "metadata": {
    "colab": {
      "name": "NER-Model2.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO94aLPH290iiOhPPd/vyZw",
      "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_Model2.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 2 on enitities :**\n",
        "# **\"Alcohol\",\"Drug\",\"Tobacco\",\"LivingStatus\",\"Employment\" **\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"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Qfh8ADOaU2k",
        "outputId": "e45a2b12-9e3a-4f92-c431-54495f0ea72a"
      },
      "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==$PUBLIC_VERSION\n",
        "\n",
        "# Installing Spark NLP Display Library for visualization\n",
        "! pip install -q spark-nlp-display\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1EoJEKpYFG9",
        "outputId": "1ae42452-72d2-42c9-8efd-feaa27c7ad76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 212.4 MB 74 kB/s \n",
            "\u001b[K     |████████████████████████████████| 142 kB 22.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 198 kB 61.1 MB/s \n",
            "\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 95 kB 2.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 66 kB 4.6 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Installing pyspark and spark-nlp\n",
        "! pip install --upgrade -q pyspark==3.1.2 spark-nlp==$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": "81e91fe6-0bb9-4aaa-f813-a1e0a79bc7ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▊                              | 10 kB 32.1 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 20 kB 9.3 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 30 kB 13.0 MB/s eta 0:00:01\r\u001b[K     |███████                         | 40 kB 2.4 MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 51 kB 2.4 MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 61 kB 2.9 MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 71 kB 3.1 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 81 kB 3.0 MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 92 kB 3.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 102 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 112 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 122 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 133 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▍       | 143 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 153 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 163 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 174 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 184 kB 2.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 188 kB 2.8 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": "7ae75fc2-a900-488f-8178-77a270b94753"
      },
      "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 0x7f74d2e75190>"
            ],
            "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://cefba206d396:4040\">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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "with open('/content/drive/MyDrive/umass_ner/NER_MODEL_2/train_ner_model2.conll') as f:\n",
        "    train_txt =f.read()\n",
        "\n",
        "print (train_txt[:500])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15uA1IJPYXFL",
        "outputId": "034e8cd1-1b59-47c4-bb98-ec3afaf1dd2a"
      },
      "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 NNP NNP B-Tobacco\n",
            "(-), NN NN O\n",
            "EtOH NNP NNP B-Alcohol\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 NNP NNP B-Drug\n",
            "(-), NN NN O\n",
            "lives NNP NNP B-LivingStatus\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 NN NN O\n",
            "not NN NN O\n",
            "work NNP NNP B-Employment\n",
            "outside NN NN O\n",
            "of NN NN O\n",
            "the NN NN O\n",
            "home NN NN O\n",
            ". NN NN O\n",
            "\n",
            "SOCIAL NN NN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading training dataset\n",
        "\n",
        "training_data = CoNLL().readDataset(spark, '/content/drive/MyDrive/umass_ner/NER_MODEL_2/train_ner_model2.conll')\n",
        "\n",
        "training_data.show(3)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hKBLPynfgVw",
        "outputId": "d0aaf60e-990a-4a5e-ba77-f77b02840904"
      },
      "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": "b838b5f8-0fa8-475e-9cff-6df1794d2b1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1316"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "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": "520fefc2-9eca-4a78-e81d-5d67955e1e21"
      },
      "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|NNP|     B-Tobacco|\n",
            "|      (-),| NN|             O|\n",
            "|      EtOH|NNP|     B-Alcohol|\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|NNP|        B-Drug|\n",
            "|      (-),| NN|             O|\n",
            "|     lives|NNP|B-LivingStatus|\n",
            "|      with| NN|             O|\n",
            "|       her| NN|             O|\n",
            "|   husband| NN|             O|\n",
            "|       and| 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": "b02581cf-4e87-44f1-8eb8-321ddbba664f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------+-----+\n",
            "|ground_truth  |count|\n",
            "+--------------+-----+\n",
            "|O             |62105|\n",
            "|B-Alcohol     |1293 |\n",
            "|B-Tobacco     |1227 |\n",
            "|B-Drug        |986  |\n",
            "|B-Employment  |981  |\n",
            "|B-LivingStatus|959  |\n",
            "|I-Drug        |930  |\n",
            "|I-Employment  |723  |\n",
            "|I-Alcohol     |444  |\n",
            "|I-Tobacco     |402  |\n",
            "|I-LivingStatus|110  |\n",
            "+--------------+-----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = CoNLL().readDataset(spark, '/content/drive/MyDrive/umass_ner/NER_MODEL_2/testF_ner_model2.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": "acac7eb4-c49f-4fcf-9012-c77e144c6d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------+-----+\n",
            "|ground_truth  |count|\n",
            "+--------------+-----+\n",
            "|O             |5954 |\n",
            "|B-Alcohol     |162  |\n",
            "|B-Tobacco     |154  |\n",
            "|B-LivingStatus|116  |\n",
            "|I-Employment  |103  |\n",
            "|B-Drug        |101  |\n",
            "|I-Drug        |98   |\n",
            "|B-Employment  |90   |\n",
            "|I-Tobacco     |59   |\n",
            "|I-Alcohol     |53   |\n",
            "|I-LivingStatus|3    |\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": "a5489acf-4e81-4e67-ed66-56644f2eb41c"
      },
      "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-LivingStatus|\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| NN|             O|\n",
            "|  history| NN|             O|\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": "bb2e5640-0cbc-4369-b20b-ef5bbee0bca4"
      },
      "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": [
        "bert_embedding.transform(test_data).write.parquet('/content/drive/MyDrive/umass_ner/NER_MODEL_2/testF_data_ner_model2_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 /root/ner_logs_model1/"
      ],
      "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(20)\\\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_2/testF_data_ner_model2_bert_embedding.parquet')\\\n",
        "      .setOutputLogsPath('./ner_dl_logs_model2')# 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_model2 && ls -lt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bu0WDE21ZJuF",
        "outputId": "0482fc47-36ed-4cd6-8431-5622257b1874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 24\n",
            "-rw-r--r-- 1 root root 17919 Apr 27 20:09 NerDLApproach_7a0ac9f52b9b.log\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat /content/ner_dl_logs_model2/NerDLApproach_7a0ac9f52b9b.log"
      ],
      "metadata": {
        "id": "W5aop-1ndH8Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b3305a7-1599-4994-cd86-8bafbd3fba55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name of the selected graph: ner-dl/blstm_38_768_128_200.pb\n",
            "Training started - total epochs: 20 - lr: 2.0E-4 - batch size: 16 - labels: 11 - chars: 89 - training examples: 1316\n",
            "\n",
            "\n",
            "Epoch 1/20 started, lr: 2.0E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 1/20 - 28.16s - loss: 3267.386 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 1.07s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 5\t 2\t 44\t 0.71428573\t 0.10204082\t 0.17857143\n",
            "I-Alcohol\t 0\t 0\t 36\t 0.0\t 0.0\t 0.0\n",
            "B-Tobacco\t 17\t 8\t 50\t 0.68\t 0.25373134\t 0.36956522\n",
            "B-Alcohol\t 16\t 3\t 58\t 0.84210527\t 0.21621622\t 0.34408605\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 14\t 3\t 39\t 0.8235294\t 0.26415095\t 0.4\n",
            "I-Tobacco\t 0\t 0\t 27\t 0.0\t 0.0\t 0.0\n",
            "B-Employment\t 0\t 0\t 44\t 0.0\t 0.0\t 0.0\n",
            "I-Drug\t 15\t 4\t 38\t 0.7894737\t 0.28301886\t 0.41666666\n",
            "I-Employment\t 0\t 0\t 51\t 0.0\t 0.0\t 0.0\n",
            "tp: 67 fp: 20 fn: 388 labels: 10\n",
            "Macro-average\t prec: 0.38493943, rec: 0.11191581, f1: 0.17341393\n",
            "Micro-average\t prec: 0.77011496, rec: 0.14725275, f1: 0.24723248\n",
            "\n",
            "\n",
            "Epoch 2/20 started, lr: 1.9900496E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 2/20 - 29.21s - loss: 1603.8988 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.87s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 26\t 11\t 23\t 0.7027027\t 0.53061223\t 0.6046511\n",
            "I-Alcohol\t 2\t 2\t 34\t 0.5\t 0.055555556\t 0.099999994\n",
            "B-Tobacco\t 45\t 24\t 22\t 0.65217394\t 0.67164177\t 0.66176474\n",
            "B-Alcohol\t 44\t 16\t 30\t 0.73333335\t 0.5945946\t 0.6567164\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 45\t 14\t 8\t 0.7627119\t 0.8490566\t 0.80357146\n",
            "I-Tobacco\t 0\t 0\t 27\t 0.0\t 0.0\t 0.0\n",
            "B-Employment\t 11\t 21\t 33\t 0.34375\t 0.25\t 0.28947368\n",
            "I-Drug\t 29\t 8\t 24\t 0.7837838\t 0.5471698\t 0.64444447\n",
            "I-Employment\t 1\t 0\t 50\t 1.0\t 0.019607844\t 0.038461536\n",
            "tp: 203 fp: 96 fn: 252 labels: 10\n",
            "Macro-average\t prec: 0.5478456, rec: 0.35182384, f1: 0.4284799\n",
            "Micro-average\t prec: 0.67892975, rec: 0.44615385, f1: 0.5384615\n",
            "\n",
            "\n",
            "Epoch 3/20 started, lr: 1.980198E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 3/20 - 28.87s - loss: 1240.2367 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.69s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 26\t 7\t 23\t 0.7878788\t 0.53061223\t 0.63414633\n",
            "I-Alcohol\t 3\t 0\t 33\t 1.0\t 0.083333336\t 0.15384616\n",
            "B-Tobacco\t 44\t 25\t 23\t 0.6376812\t 0.6567164\t 0.64705884\n",
            "B-Alcohol\t 50\t 26\t 24\t 0.65789473\t 0.6756757\t 0.6666666\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 44\t 11\t 9\t 0.8\t 0.8301887\t 0.8148148\n",
            "I-Tobacco\t 3\t 0\t 24\t 1.0\t 0.11111111\t 0.19999999\n",
            "B-Employment\t 13\t 17\t 31\t 0.43333334\t 0.29545453\t 0.35135132\n",
            "I-Drug\t 34\t 17\t 19\t 0.6666667\t 0.6415094\t 0.6538462\n",
            "I-Employment\t 7\t 2\t 44\t 0.7777778\t 0.13725491\t 0.23333335\n",
            "tp: 224 fp: 105 fn: 231 labels: 10\n",
            "Macro-average\t prec: 0.6761232, rec: 0.39618567, f1: 0.49961406\n",
            "Micro-average\t prec: 0.68085104, rec: 0.4923077, f1: 0.57142854\n",
            "\n",
            "\n",
            "Epoch 4/20 started, lr: 1.9704433E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 4/20 - 28.18s - loss: 1060.1084 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.81s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 34\t 12\t 15\t 0.73913044\t 0.6938776\t 0.7157895\n",
            "I-Alcohol\t 12\t 0\t 24\t 1.0\t 0.33333334\t 0.5\n",
            "B-Tobacco\t 48\t 31\t 19\t 0.6075949\t 0.7164179\t 0.6575343\n",
            "B-Alcohol\t 50\t 27\t 24\t 0.64935064\t 0.6756757\t 0.6622517\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 42\t 9\t 11\t 0.8235294\t 0.7924528\t 0.8076923\n",
            "I-Tobacco\t 8\t 2\t 19\t 0.8\t 0.2962963\t 0.43243244\n",
            "B-Employment\t 24\t 24\t 20\t 0.5\t 0.54545456\t 0.5217392\n",
            "I-Drug\t 28\t 7\t 25\t 0.8\t 0.5283019\t 0.6363636\n",
            "I-Employment\t 13\t 5\t 38\t 0.7222222\t 0.25490198\t 0.37681162\n",
            "tp: 259 fp: 117 fn: 196 labels: 10\n",
            "Macro-average\t prec: 0.6641828, rec: 0.4836712, f1: 0.5597334\n",
            "Micro-average\t prec: 0.6888298, rec: 0.5692308, f1: 0.6233454\n",
            "\n",
            "\n",
            "Epoch 5/20 started, lr: 1.9607843E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 5/20 - 27.66s - loss: 950.1147 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.61s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 30\t 7\t 19\t 0.8108108\t 0.6122449\t 0.69767445\n",
            "I-Alcohol\t 13\t 0\t 23\t 1.0\t 0.3611111\t 0.53061223\n",
            "B-Tobacco\t 47\t 25\t 20\t 0.6527778\t 0.70149255\t 0.67625904\n",
            "B-Alcohol\t 51\t 25\t 23\t 0.67105263\t 0.6891892\t 0.67999995\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 45\t 9\t 8\t 0.8333333\t 0.8490566\t 0.84112144\n",
            "I-Tobacco\t 7\t 1\t 20\t 0.875\t 0.25925925\t 0.4\n",
            "B-Employment\t 20\t 21\t 24\t 0.4878049\t 0.45454547\t 0.47058824\n",
            "I-Drug\t 37\t 13\t 16\t 0.74\t 0.6981132\t 0.7184466\n",
            "I-Employment\t 15\t 5\t 36\t 0.75\t 0.29411766\t 0.4225352\n",
            "tp: 265 fp: 106 fn: 190 labels: 10\n",
            "Macro-average\t prec: 0.682078, rec: 0.491913, f1: 0.5715939\n",
            "Micro-average\t prec: 0.71428573, rec: 0.5824176, f1: 0.6416465\n",
            "\n",
            "\n",
            "Epoch 6/20 started, lr: 1.9512195E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 6/20 - 27.66s - loss: 871.7674 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.47s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 34\t 10\t 15\t 0.77272725\t 0.6938776\t 0.7311828\n",
            "I-Alcohol\t 17\t 1\t 19\t 0.9444444\t 0.4722222\t 0.6296296\n",
            "B-Tobacco\t 49\t 25\t 18\t 0.6621622\t 0.73134327\t 0.69503546\n",
            "B-Alcohol\t 51\t 23\t 23\t 0.6891892\t 0.6891892\t 0.6891892\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 46\t 9\t 7\t 0.8363636\t 0.8679245\t 0.8518518\n",
            "I-Tobacco\t 9\t 2\t 18\t 0.8181818\t 0.33333334\t 0.47368422\n",
            "B-Employment\t 31\t 21\t 13\t 0.59615386\t 0.70454544\t 0.6458334\n",
            "I-Drug\t 28\t 4\t 25\t 0.875\t 0.5283019\t 0.6588235\n",
            "I-Employment\t 20\t 8\t 31\t 0.71428573\t 0.39215687\t 0.5063291\n",
            "tp: 285 fp: 103 fn: 170 labels: 10\n",
            "Macro-average\t prec: 0.69085085, rec: 0.54128945, f1: 0.606993\n",
            "Micro-average\t prec: 0.7345361, rec: 0.62637365, f1: 0.67615664\n",
            "\n",
            "\n",
            "Epoch 7/20 started, lr: 1.9417476E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 7/20 - 28.20s - loss: 818.1154 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.44s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 37\t 9\t 12\t 0.8043478\t 0.75510204\t 0.7789473\n",
            "I-Alcohol\t 19\t 1\t 17\t 0.95\t 0.5277778\t 0.67857146\n",
            "B-Tobacco\t 56\t 35\t 11\t 0.61538464\t 0.8358209\t 0.7088608\n",
            "B-Alcohol\t 58\t 36\t 16\t 0.61702126\t 0.7837838\t 0.6904762\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 51\t 14\t 2\t 0.7846154\t 0.9622642\t 0.86440676\n",
            "I-Tobacco\t 11\t 1\t 16\t 0.9166667\t 0.4074074\t 0.5641026\n",
            "B-Employment\t 36\t 20\t 8\t 0.64285713\t 0.8181818\t 0.72\n",
            "I-Drug\t 41\t 8\t 12\t 0.8367347\t 0.7735849\t 0.8039216\n",
            "I-Employment\t 26\t 10\t 25\t 0.7222222\t 0.50980395\t 0.5977012\n",
            "tp: 335 fp: 134 fn: 120 labels: 10\n",
            "Macro-average\t prec: 0.688985, rec: 0.6373726, f1: 0.66217464\n",
            "Micro-average\t prec: 0.71428573, rec: 0.73626375, f1: 0.7251082\n",
            "\n",
            "\n",
            "Epoch 8/20 started, lr: 1.9323672E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 8/20 - 28.66s - loss: 756.419 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.55s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 35\t 12\t 14\t 0.7446808\t 0.71428573\t 0.7291667\n",
            "I-Alcohol\t 20\t 0\t 16\t 1.0\t 0.5555556\t 0.71428573\n",
            "B-Tobacco\t 55\t 26\t 12\t 0.67901236\t 0.8208955\t 0.7432433\n",
            "B-Alcohol\t 55\t 26\t 19\t 0.67901236\t 0.7432432\t 0.70967746\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 46\t 9\t 7\t 0.8363636\t 0.8679245\t 0.8518518\n",
            "I-Tobacco\t 14\t 1\t 13\t 0.93333334\t 0.5185185\t 0.6666667\n",
            "B-Employment\t 36\t 18\t 8\t 0.6666667\t 0.8181818\t 0.7346939\n",
            "I-Drug\t 34\t 9\t 19\t 0.7906977\t 0.6415094\t 0.7083333\n",
            "I-Employment\t 28\t 14\t 23\t 0.6666667\t 0.54901963\t 0.60215056\n",
            "tp: 323 fp: 115 fn: 132 labels: 10\n",
            "Macro-average\t prec: 0.69964325, rec: 0.6229135, f1: 0.65905255\n",
            "Micro-average\t prec: 0.7374429, rec: 0.7098901, f1: 0.7234042\n",
            "\n",
            "\n",
            "Epoch 9/20 started, lr: 1.9230769E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 9/20 - 28.39s - loss: 702.98456 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.65s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 35\t 10\t 14\t 0.7777778\t 0.71428573\t 0.7446809\n",
            "I-Alcohol\t 19\t 0\t 17\t 1.0\t 0.5277778\t 0.6909091\n",
            "B-Tobacco\t 48\t 24\t 19\t 0.6666667\t 0.7164179\t 0.69064754\n",
            "B-Alcohol\t 55\t 25\t 19\t 0.6875\t 0.7432432\t 0.71428573\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 47\t 9\t 6\t 0.83928573\t 0.8867925\t 0.86238533\n",
            "I-Tobacco\t 8\t 1\t 19\t 0.8888889\t 0.2962963\t 0.44444442\n",
            "B-Employment\t 30\t 18\t 14\t 0.625\t 0.6818182\t 0.6521739\n",
            "I-Drug\t 36\t 8\t 17\t 0.8181818\t 0.6792453\t 0.742268\n",
            "I-Employment\t 21\t 6\t 30\t 0.7777778\t 0.4117647\t 0.5384615\n",
            "tp: 299 fp: 101 fn: 156 labels: 10\n",
            "Macro-average\t prec: 0.7081078, rec: 0.5657641, f1: 0.6289832\n",
            "Micro-average\t prec: 0.7475, rec: 0.6571429, f1: 0.69941527\n",
            "\n",
            "\n",
            "Epoch 10/20 started, lr: 1.9138756E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 10/20 - 28.18s - loss: 668.4685 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.77s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 34\t 10\t 15\t 0.77272725\t 0.6938776\t 0.7311828\n",
            "I-Alcohol\t 20\t 0\t 16\t 1.0\t 0.5555556\t 0.71428573\n",
            "B-Tobacco\t 55\t 24\t 12\t 0.6962025\t 0.8208955\t 0.75342464\n",
            "B-Alcohol\t 55\t 22\t 19\t 0.71428573\t 0.7432432\t 0.7284768\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 48\t 9\t 5\t 0.84210527\t 0.9056604\t 0.8727273\n",
            "I-Tobacco\t 15\t 1\t 12\t 0.9375\t 0.5555556\t 0.69767445\n",
            "B-Employment\t 34\t 14\t 10\t 0.7083333\t 0.77272725\t 0.73913044\n",
            "I-Drug\t 35\t 7\t 18\t 0.8333333\t 0.6603774\t 0.7368421\n",
            "I-Employment\t 28\t 11\t 23\t 0.71794873\t 0.54901963\t 0.62222224\n",
            "tp: 324 fp: 98 fn: 131 labels: 10\n",
            "Macro-average\t prec: 0.72224367, rec: 0.62569124, f1: 0.6705094\n",
            "Micro-average\t prec: 0.7677725, rec: 0.7120879, f1: 0.73888254\n",
            "\n",
            "\n",
            "Epoch 11/20 started, lr: 1.9047619E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 11/20 - 28.21s - loss: 631.72345 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.78s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 35\t 9\t 14\t 0.79545456\t 0.71428573\t 0.75268817\n",
            "I-Alcohol\t 20\t 0\t 16\t 1.0\t 0.5555556\t 0.71428573\n",
            "B-Tobacco\t 57\t 27\t 10\t 0.6785714\t 0.8507463\t 0.7549669\n",
            "B-Alcohol\t 57\t 24\t 17\t 0.7037037\t 0.7702703\t 0.7354839\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 51\t 10\t 2\t 0.8360656\t 0.9622642\t 0.8947368\n",
            "I-Tobacco\t 14\t 1\t 13\t 0.93333334\t 0.5185185\t 0.6666667\n",
            "B-Employment\t 33\t 13\t 11\t 0.7173913\t 0.75\t 0.7333334\n",
            "I-Drug\t 39\t 8\t 14\t 0.82978725\t 0.7358491\t 0.78000003\n",
            "I-Employment\t 29\t 10\t 22\t 0.74358976\t 0.5686275\t 0.64444447\n",
            "tp: 335 fp: 102 fn: 120 labels: 10\n",
            "Macro-average\t prec: 0.72378975, rec: 0.6426117, f1: 0.68078935\n",
            "Micro-average\t prec: 0.7665904, rec: 0.73626375, f1: 0.7511211\n",
            "\n",
            "\n",
            "Epoch 12/20 started, lr: 1.8957346E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 12/20 - 29.80s - loss: 593.56323 - 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-Drug\t 39\t 11\t 10\t 0.78\t 0.79591835\t 0.78787875\n",
            "I-Alcohol\t 20\t 0\t 16\t 1.0\t 0.5555556\t 0.71428573\n",
            "B-Tobacco\t 57\t 28\t 10\t 0.67058825\t 0.8507463\t 0.75\n",
            "B-Alcohol\t 56\t 23\t 18\t 0.70886075\t 0.7567568\t 0.73202616\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 52\t 9\t 1\t 0.852459\t 0.9811321\t 0.9122807\n",
            "I-Tobacco\t 13\t 1\t 14\t 0.9285714\t 0.4814815\t 0.63414633\n",
            "B-Employment\t 34\t 15\t 10\t 0.6938776\t 0.77272725\t 0.7311828\n",
            "I-Drug\t 37\t 7\t 16\t 0.84090906\t 0.6981132\t 0.76288664\n",
            "I-Employment\t 26\t 9\t 25\t 0.74285716\t 0.50980395\t 0.6046512\n",
            "tp: 334 fp: 103 fn: 121 labels: 10\n",
            "Macro-average\t prec: 0.7218123, rec: 0.6402235, f1: 0.67857426\n",
            "Micro-average\t prec: 0.7643021, rec: 0.73406595, f1: 0.74887896\n",
            "\n",
            "\n",
            "Epoch 13/20 started, lr: 1.8867925E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 13/20 - 28.46s - loss: 573.248 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.66s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 34\t 10\t 15\t 0.77272725\t 0.6938776\t 0.7311828\n",
            "I-Alcohol\t 23\t 1\t 13\t 0.9583333\t 0.6388889\t 0.76666665\n",
            "B-Tobacco\t 57\t 27\t 10\t 0.6785714\t 0.8507463\t 0.7549669\n",
            "B-Alcohol\t 55\t 19\t 19\t 0.7432432\t 0.7432432\t 0.7432432\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 46\t 8\t 7\t 0.8518519\t 0.8679245\t 0.8598131\n",
            "I-Tobacco\t 15\t 1\t 12\t 0.9375\t 0.5555556\t 0.69767445\n",
            "B-Employment\t 33\t 15\t 11\t 0.6875\t 0.75\t 0.7173913\n",
            "I-Drug\t 38\t 7\t 15\t 0.84444445\t 0.7169811\t 0.77551025\n",
            "I-Employment\t 26\t 9\t 25\t 0.74285716\t 0.50980395\t 0.6046512\n",
            "tp: 327 fp: 97 fn: 128 labels: 10\n",
            "Macro-average\t prec: 0.7217028, rec: 0.63270205, f1: 0.6742782\n",
            "Micro-average\t prec: 0.7712264, rec: 0.71868134, f1: 0.7440273\n",
            "\n",
            "\n",
            "Epoch 14/20 started, lr: 1.8779341E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 14/20 - 27.40s - loss: 548.22424 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.81s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 39\t 9\t 10\t 0.8125\t 0.79591835\t 0.8041237\n",
            "I-Alcohol\t 25\t 1\t 11\t 0.96153843\t 0.6944444\t 0.80645156\n",
            "B-Tobacco\t 57\t 25\t 10\t 0.69512194\t 0.8507463\t 0.7651007\n",
            "B-Alcohol\t 61\t 19\t 13\t 0.7625\t 0.8243243\t 0.7922077\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 47\t 7\t 6\t 0.8703704\t 0.8867925\t 0.8785047\n",
            "I-Tobacco\t 17\t 1\t 10\t 0.9444444\t 0.6296296\t 0.7555555\n",
            "B-Employment\t 38\t 14\t 6\t 0.7307692\t 0.8636364\t 0.7916666\n",
            "I-Drug\t 40\t 10\t 13\t 0.8\t 0.754717\t 0.776699\n",
            "I-Employment\t 40\t 21\t 11\t 0.6557377\t 0.78431374\t 0.71428573\n",
            "tp: 364 fp: 107 fn: 91 labels: 10\n",
            "Macro-average\t prec: 0.72329825, rec: 0.7084523, f1: 0.7157983\n",
            "Micro-average\t prec: 0.77282375, rec: 0.8, f1: 0.7861771\n",
            "\n",
            "\n",
            "Epoch 15/20 started, lr: 1.8691587E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 15/20 - 29.07s - loss: 515.33887 - 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-Drug\t 39\t 9\t 10\t 0.8125\t 0.79591835\t 0.8041237\n",
            "I-Alcohol\t 23\t 1\t 13\t 0.9583333\t 0.6388889\t 0.76666665\n",
            "B-Tobacco\t 58\t 29\t 9\t 0.6666667\t 0.86567163\t 0.7532468\n",
            "B-Alcohol\t 61\t 21\t 13\t 0.74390244\t 0.8243243\t 0.78205127\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 48\t 8\t 5\t 0.85714287\t 0.9056604\t 0.8807339\n",
            "I-Tobacco\t 17\t 2\t 10\t 0.8947368\t 0.6296296\t 0.7391305\n",
            "B-Employment\t 34\t 15\t 10\t 0.6938776\t 0.77272725\t 0.7311828\n",
            "I-Drug\t 39\t 10\t 14\t 0.79591835\t 0.7358491\t 0.7647059\n",
            "I-Employment\t 25\t 8\t 26\t 0.75757575\t 0.49019608\t 0.5952381\n",
            "tp: 344 fp: 103 fn: 111 labels: 10\n",
            "Macro-average\t prec: 0.7180654, rec: 0.6658865, f1: 0.6909923\n",
            "Micro-average\t prec: 0.76957494, rec: 0.756044, f1: 0.76274943\n",
            "\n",
            "\n",
            "Epoch 16/20 started, lr: 1.860465E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 16/20 - 28.42s - loss: 491.44 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.74s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 39\t 11\t 10\t 0.78\t 0.79591835\t 0.78787875\n",
            "I-Alcohol\t 22\t 0\t 14\t 1.0\t 0.6111111\t 0.7586207\n",
            "B-Tobacco\t 58\t 26\t 9\t 0.6904762\t 0.86567163\t 0.7682119\n",
            "B-Alcohol\t 57\t 22\t 17\t 0.721519\t 0.7702703\t 0.745098\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 50\t 7\t 3\t 0.877193\t 0.9433962\t 0.9090909\n",
            "I-Tobacco\t 15\t 1\t 12\t 0.9375\t 0.5555556\t 0.69767445\n",
            "B-Employment\t 31\t 15\t 13\t 0.67391306\t 0.70454544\t 0.6888889\n",
            "I-Drug\t 37\t 7\t 16\t 0.84090906\t 0.6981132\t 0.76288664\n",
            "I-Employment\t 25\t 5\t 26\t 0.8333333\t 0.49019608\t 0.617284\n",
            "tp: 334 fp: 94 fn: 121 labels: 10\n",
            "Macro-average\t prec: 0.73548436, rec: 0.6434778, f1: 0.6864117\n",
            "Micro-average\t prec: 0.7803738, rec: 0.73406595, f1: 0.7565118\n",
            "\n",
            "\n",
            "Epoch 17/20 started, lr: 1.8518517E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 17/20 - 29.62s - loss: 482.37463 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.74s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 39\t 12\t 10\t 0.7647059\t 0.79591835\t 0.78000003\n",
            "I-Alcohol\t 20\t 0\t 16\t 1.0\t 0.5555556\t 0.71428573\n",
            "B-Tobacco\t 58\t 30\t 9\t 0.65909094\t 0.86567163\t 0.7483871\n",
            "B-Alcohol\t 60\t 24\t 14\t 0.71428573\t 0.8108108\t 0.7594937\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 52\t 13\t 1\t 0.8\t 0.9811321\t 0.88135594\n",
            "I-Tobacco\t 13\t 1\t 14\t 0.9285714\t 0.4814815\t 0.63414633\n",
            "B-Employment\t 36\t 16\t 8\t 0.6923077\t 0.8181818\t 0.75\n",
            "I-Drug\t 34\t 7\t 19\t 0.8292683\t 0.6415094\t 0.7234042\n",
            "I-Employment\t 30\t 10\t 21\t 0.75\t 0.5882353\t 0.6593406\n",
            "tp: 342 fp: 113 fn: 113 labels: 10\n",
            "Macro-average\t prec: 0.713823, rec: 0.6538497, f1: 0.68252146\n",
            "Micro-average\t prec: 0.75164837, rec: 0.75164837, f1: 0.75164837\n",
            "\n",
            "\n",
            "Epoch 18/20 started, lr: 1.8433179E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 18/20 - 28.56s - loss: 456.48242 - 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-Drug\t 38\t 10\t 11\t 0.7916667\t 0.7755102\t 0.7835052\n",
            "I-Alcohol\t 25\t 1\t 11\t 0.96153843\t 0.6944444\t 0.80645156\n",
            "B-Tobacco\t 57\t 25\t 10\t 0.69512194\t 0.8507463\t 0.7651007\n",
            "B-Alcohol\t 62\t 22\t 12\t 0.7380952\t 0.8378378\t 0.7848101\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 48\t 7\t 5\t 0.8727273\t 0.9056604\t 0.8888889\n",
            "I-Tobacco\t 18\t 3\t 9\t 0.85714287\t 0.6666667\t 0.75\n",
            "B-Employment\t 38\t 14\t 6\t 0.7307692\t 0.8636364\t 0.7916666\n",
            "I-Drug\t 37\t 8\t 16\t 0.82222223\t 0.6981132\t 0.75510204\n",
            "I-Employment\t 38\t 16\t 13\t 0.7037037\t 0.74509805\t 0.7238095\n",
            "tp: 361 fp: 106 fn: 94 labels: 10\n",
            "Macro-average\t prec: 0.7172988, rec: 0.7037714, f1: 0.71047074\n",
            "Micro-average\t prec: 0.77301925, rec: 0.7934066, f1: 0.7830803\n",
            "\n",
            "\n",
            "Epoch 19/20 started, lr: 1.8348623E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 19/20 - 28.10s - loss: 423.8398 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.85s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 32\t 10\t 17\t 0.7619048\t 0.6530612\t 0.7032967\n",
            "I-Alcohol\t 22\t 0\t 14\t 1.0\t 0.6111111\t 0.7586207\n",
            "B-Tobacco\t 58\t 23\t 9\t 0.7160494\t 0.86567163\t 0.78378373\n",
            "B-Alcohol\t 57\t 21\t 17\t 0.7307692\t 0.7702703\t 0.75\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 49\t 7\t 4\t 0.875\t 0.9245283\t 0.89908254\n",
            "I-Tobacco\t 15\t 0\t 12\t 1.0\t 0.5555556\t 0.71428573\n",
            "B-Employment\t 29\t 15\t 15\t 0.65909094\t 0.65909094\t 0.65909094\n",
            "I-Drug\t 36\t 7\t 17\t 0.8372093\t 0.6792453\t 0.75\n",
            "I-Employment\t 24\t 6\t 27\t 0.8\t 0.47058824\t 0.5925926\n",
            "tp: 322 fp: 89 fn: 133 labels: 10\n",
            "Macro-average\t prec: 0.73800236, rec: 0.61891234, f1: 0.67323136\n",
            "Micro-average\t prec: 0.783455, rec: 0.7076923, f1: 0.743649\n",
            "\n",
            "\n",
            "Epoch 20/20 started, lr: 1.826484E-4, dataset size: 1316\n",
            "\n",
            "\n",
            "Epoch 20/20 - 27.51s - loss: 414.1997 - batches: 85\n",
            "Quality on test dataset: \n",
            "time to finish evaluation: 0.78s\n",
            "label\t tp\t fp\t fn\t prec\t rec\t f1\n",
            "B-Drug\t 37\t 11\t 12\t 0.7708333\t 0.75510204\t 0.7628866\n",
            "I-Alcohol\t 21\t 0\t 15\t 1.0\t 0.5833333\t 0.7368421\n",
            "B-Tobacco\t 57\t 18\t 10\t 0.76\t 0.8507463\t 0.8028169\n",
            "B-Alcohol\t 53\t 18\t 21\t 0.74647886\t 0.7162162\t 0.73103446\n",
            "I-LivingStatus\t 0\t 0\t 1\t 0.0\t 0.0\t 0.0\n",
            "B-LivingStatus\t 44\t 7\t 9\t 0.8627451\t 0.8301887\t 0.84615386\n",
            "I-Tobacco\t 14\t 0\t 13\t 1.0\t 0.5185185\t 0.68292683\n",
            "B-Employment\t 32\t 12\t 12\t 0.72727275\t 0.72727275\t 0.72727275\n",
            "I-Drug\t 35\t 7\t 18\t 0.8333333\t 0.6603774\t 0.7368421\n",
            "I-Employment\t 30\t 7\t 21\t 0.8108108\t 0.5882353\t 0.6818182\n",
            "tp: 323 fp: 80 fn: 132 labels: 10\n",
            "Macro-average\t prec: 0.7511474, rec: 0.6229991, f1: 0.68109787\n",
            "Micro-average\t prec: 0.8014888, rec: 0.7098901, f1: 0.7529137\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "outputId": "dc211775-264d-4f9e-cc1d-2025d4ee6ee1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-3aeb8e2e6bd5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpred_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mner_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbert_embedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# pred_df = ner_model.stages[1].transform(test_data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'ner_model' is not defined"
          ]
        }
      ]
    },
    {
      "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": "81d7c63c-4f08-4fd3-f057-e9676ef2a9d6"
      },
      "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-LivingStatus  B-LivingStatus\n",
              "4        with               O               O\n",
              "...       ...             ...             ...\n",
              "6888   father               O               O\n",
              "6889       of               O               O\n",
              "6890      one               O               O\n",
              "6891      son               O               O\n",
              "6892        .               O               O\n",
              "\n",
              "[6893 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cb3461a9-49ac-4448-9806-598b74f1db08\">\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-LivingStatus</td>\n",
              "      <td>B-LivingStatus</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>6888</th>\n",
              "      <td>father</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6889</th>\n",
              "      <td>of</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6890</th>\n",
              "      <td>one</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6891</th>\n",
              "      <td>son</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6892</th>\n",
              "      <td>.</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6893 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb3461a9-49ac-4448-9806-598b74f1db08')\"\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-cb3461a9-49ac-4448-9806-598b74f1db08 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-cb3461a9-49ac-4448-9806-598b74f1db08');\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": 28
        }
      ]
    },
    {
      "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": "6e8f9895-8b12-4d14-f103-875a67f2f312"
      },
      "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-LivingStatus|B-LivingStatus|\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       |O             |O             |\n",
            "|history  |O             |O             |\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",
        "\n",
        "print (classification_report(preds_df_analysis['ground_truth'], preds_df_analysis['prediction']))\n",
        "\n"
      ],
      "metadata": {
        "id": "4qUDJjse15bZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a4c25c3-adfb-4fdd-e467-5d4edb58384b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                precision    recall  f1-score   support\n",
            "\n",
            "     B-Alcohol       0.77      0.83      0.80       162\n",
            "        B-Drug       0.90      0.82      0.86       101\n",
            "  B-Employment       0.81      0.69      0.74        90\n",
            "B-LivingStatus       0.97      0.75      0.84       116\n",
            "     B-Tobacco       0.72      0.77      0.75       154\n",
            "     I-Alcohol       0.88      0.68      0.77        53\n",
            "        I-Drug       0.90      0.87      0.89        98\n",
            "  I-Employment       0.82      0.70      0.75       103\n",
            "I-LivingStatus       0.00      0.00      0.00         3\n",
            "     I-Tobacco       0.78      0.54      0.64        59\n",
            "             O       0.97      0.98      0.98      5954\n",
            "\n",
            "      accuracy                           0.95      6893\n",
            "     macro avg       0.77      0.69      0.73      6893\n",
            "  weighted avg       0.95      0.95      0.95      6893\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ner_model.stages[1].write().overwrite().save('/content/drive/MyDrive/umass_ner/model_ner_dl_2')"
      ],
      "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": 345
        },
        "id": "p8eLAnfp2cpG",
        "outputId": "a47c5aee-359e-4fce-a6c2-5358880f12c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processed 6893 tokens with 623 phrases; found: 627 phrases; correct: 466.\n",
            "accuracy:  75.61%; (non-O)\n",
            "accuracy:  95.29%; precision:  74.32%; recall:  74.80%; FB1:  74.56\n",
            "          Alcohol: precision:  72.73%; recall:  79.01%; FB1:  75.74  176\n",
            "             Drug: precision:  82.83%; recall:  81.19%; FB1:  82.00  99\n",
            "       Employment: precision:  60.87%; recall:  62.22%; FB1:  61.54  92\n",
            "     LivingStatus: precision:  95.56%; recall:  74.14%; FB1:  83.50  90\n",
            "          Tobacco: precision:  67.06%; recall:  74.03%; FB1:  70.37  170\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         entity  precision     recall         f1  support\n",
              "0       Alcohol  72.727273  79.012346  75.739645      176\n",
              "1          Drug  82.828283  81.188119  82.000000       99\n",
              "2    Employment  60.869565  62.222222  61.538462       92\n",
              "3  LivingStatus  95.555556  74.137931  83.495146       90\n",
              "4       Tobacco  67.058824  74.025974  70.370370      170"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1631bfe1-d1ab-4139-a779-49ed6fe1d102\">\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>Alcohol</td>\n",
              "      <td>72.727273</td>\n",
              "      <td>79.012346</td>\n",
              "      <td>75.739645</td>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Drug</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>81.188119</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Employment</td>\n",
              "      <td>60.869565</td>\n",
              "      <td>62.222222</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LivingStatus</td>\n",
              "      <td>95.555556</td>\n",
              "      <td>74.137931</td>\n",
              "      <td>83.495146</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Tobacco</td>\n",
              "      <td>67.058824</td>\n",
              "      <td>74.025974</td>\n",
              "      <td>70.370370</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1631bfe1-d1ab-4139-a779-49ed6fe1d102')\"\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",
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              "  </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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1631bfe1-d1ab-4139-a779-49ed6fe1d102 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-1631bfe1-d1ab-4139-a779-49ed6fe1d102');\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": 31
        }
      ]
    },
    {
      "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_model2/NerDLApproach_7a0ac9f52b9b.log')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gJZY3Izr98s1",
        "outputId": "8c4873fc-21c0-4acc-d2d9-b727d17d8c8b"
      },
      "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_model2/NerDLApproach_7a0ac9f52b9b.log')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "87vpWC8q-g2e",
        "outputId": "09ef1197-cf2c-40e1-b9de-95b83212b12d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\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_old/model_ner_dl_2\")\\\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": "bb15e636-796c-44fe-e05f-6e712b77a54f"
      },
      "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": [
        "\n",
        "\n",
        "from sparknlp.base import LightPipeline\n",
        "\n",
        "light_model = LightPipeline(prediction_model)\n",
        "\n"
      ],
      "metadata": {
        "id": "_TJyi_EK_7rB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"Social History:\n",
        "Tob (-), EtOH - a glass of wine 1-2x/month, IVDU (-), lives with\n",
        "her husband and 9yr old daughter, does not work outside of the\n",
        "home.\"\"\""
      ],
      "metadata": {
        "id": "chls97aKDdnF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "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\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0PQRL6UO8Eia",
        "outputId": "bc20d8de-4e66-4028-dcd7-f9b1b45fbcfa"
      },
      "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         Tob     16   18       B-Tobacco\n",
              "4         0        (-),     20   23               O\n",
              "5         0        EtOH     25   28       B-Alcohol\n",
              "6         0           -     30   30               O\n",
              "7         0           a     32   32               O\n",
              "8         0       glass     34   38               O\n",
              "9         0          of     40   41               O\n",
              "10        0        wine     43   46               O\n",
              "11        0  1-2x/month     48   57               O\n",
              "12        0           ,     58   58               O\n",
              "13        0        IVDU     60   63          B-Drug\n",
              "14        0        (-),     65   68               O\n",
              "15        0       lives     70   74  B-LivingStatus\n",
              "16        0        with     76   79               O\n",
              "17        0         her     81   83               O\n",
              "18        0     husband     85   91               O\n",
              "19        0         and     93   95               O\n",
              "20        0         9yr     97   99               O\n",
              "21        0         old    101  103               O\n",
              "22        0    daughter    105  112               O\n",
              "23        0           ,    113  113               O\n",
              "24        0        does    115  118               O\n",
              "25        0         not    120  122               O\n",
              "26        0        work    124  127    B-Employment\n",
              "27        0     outside    129  135               O\n",
              "28        0          of    137  138               O\n",
              "29        0         the    140  142               O\n",
              "30        0        home    144  147               O\n",
              "31        0           .    148  148               O"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e96cfd51-a66e-450e-8e68-a8e8b7682cce\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "<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",
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              "  </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",
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              "    </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",
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              "      <td>14</td>\n",
              "      <td>O</td>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>Tob</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>B-Tobacco</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>(-),</td>\n",
              "      <td>20</td>\n",
              "      <td>23</td>\n",
              "      <td>O</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>EtOH</td>\n",
              "      <td>25</td>\n",
              "      <td>28</td>\n",
              "      <td>B-Alcohol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>glass</td>\n",
              "      <td>34</td>\n",
              "      <td>38</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>of</td>\n",
              "      <td>40</td>\n",
              "      <td>41</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>wine</td>\n",
              "      <td>43</td>\n",
              "      <td>46</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>1-2x/month</td>\n",
              "      <td>48</td>\n",
              "      <td>57</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>,</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>IVDU</td>\n",
              "      <td>60</td>\n",
              "      <td>63</td>\n",
              "      <td>B-Drug</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>(-),</td>\n",
              "      <td>65</td>\n",
              "      <td>68</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>lives</td>\n",
              "      <td>70</td>\n",
              "      <td>74</td>\n",
              "      <td>B-LivingStatus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>with</td>\n",
              "      <td>76</td>\n",
              "      <td>79</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>her</td>\n",
              "      <td>81</td>\n",
              "      <td>83</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>husband</td>\n",
              "      <td>85</td>\n",
              "      <td>91</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>and</td>\n",
              "      <td>93</td>\n",
              "      <td>95</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>9yr</td>\n",
              "      <td>97</td>\n",
              "      <td>99</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0</td>\n",
              "      <td>old</td>\n",
              "      <td>101</td>\n",
              "      <td>103</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>daughter</td>\n",
              "      <td>105</td>\n",
              "      <td>112</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0</td>\n",
              "      <td>,</td>\n",
              "      <td>113</td>\n",
              "      <td>113</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0</td>\n",
              "      <td>does</td>\n",
              "      <td>115</td>\n",
              "      <td>118</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>not</td>\n",
              "      <td>120</td>\n",
              "      <td>122</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0</td>\n",
              "      <td>work</td>\n",
              "      <td>124</td>\n",
              "      <td>127</td>\n",
              "      <td>B-Employment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "      <td>outside</td>\n",
              "      <td>129</td>\n",
              "      <td>135</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0</td>\n",
              "      <td>of</td>\n",
              "      <td>137</td>\n",
              "      <td>138</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0</td>\n",
              "      <td>the</td>\n",
              "      <td>140</td>\n",
              "      <td>142</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0</td>\n",
              "      <td>home</td>\n",
              "      <td>144</td>\n",
              "      <td>147</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0</td>\n",
              "      <td>.</td>\n",
              "      <td>148</td>\n",
              "      <td>148</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e96cfd51-a66e-450e-8e68-a8e8b7682cce')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "                                                     [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",
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            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "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": "dfbaa63e-d736-44e5-acc7-5a092a9a3a66"
      },
      "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:<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #9D7E65\"><span class=\"spark-nlp-display-entity-name\">Tob </span><span class=\"spark-nlp-display-entity-type\">Tobacco</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> (-), </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #01BB88\"><span class=\"spark-nlp-display-entity-name\">EtOH </span><span class=\"spark-nlp-display-entity-type\">Alcohol</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> - a glass of wine 1-2x/month, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">IVDU </span><span class=\"spark-nlp-display-entity-type\">Drug</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> (-), </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #445416\"><span class=\"spark-nlp-display-entity-name\">lives </span><span class=\"spark-nlp-display-entity-type\">LivingStatus</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> with<br>her husband and 9yr old daughter, does not </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #37006F\"><span class=\"spark-nlp-display-entity-name\">work </span><span class=\"spark-nlp-display-entity-type\">Employment</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> outside of the<br>home.</span></div>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Social History: Tob, EtOH - a glass of wine 1-2x/month, IVDU lives with\n",
        "her husband and 9yr old daughter, does not work outside of the home"
      ],
      "metadata": {
        "id": "AIWqtqyb5RFU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}