SkimLit.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## SkimLit\n",
        " we're going to be replicating the deep learning model behind the 2017 paper PubMed 200k RCT: a Dataset for Sequenctial Sentence Classification in Medical Abstracts. https://arxiv.org/abs/1710.06071\n",
        "\n",
        " When it was released, the paper presented a new dataset called PubMed 200k RCT which consists of ~200,000 labelled Randomized Controlled Trial (RCT) abstracts.\n",
        "\n",
        " The goal of the dataset was to explore the ability for NLP models to classify sentences which appear in sequential order."
      ],
      "metadata": {
        "id": "BY1fhMeDhUZg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Data\n",
        "!git clone https://github.com/Franck-Dernoncourt/pubmed-rct.git\n",
        "!ls pubmed-rct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0loKSeHnot_",
        "outputId": "78703dbd-0c03-4e60-b95b-fce7b9a52332"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'pubmed-rct'...\n",
            "remote: Enumerating objects: 39, done.\u001b[K\n",
            "remote: Counting objects: 100% (14/14), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 39 (delta 8), reused 5 (delta 5), pack-reused 25\u001b[K\n",
            "Receiving objects: 100% (39/39), 177.08 MiB | 42.26 MiB/s, done.\n",
            "Resolving deltas: 100% (15/15), done.\n",
            "PubMed_200k_RCT\t\t\t\t       PubMed_20k_RCT_numbers_replaced_with_at_sign\n",
            "PubMed_200k_RCT_numbers_replaced_with_at_sign  README.md\n",
            "PubMed_20k_RCT\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwCm_sYRowqv",
        "outputId": "8ad66880-99a3-431c-a2c6-42061dcd40b0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dev.txt  test.txt  train.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/\""
      ],
      "metadata": {
        "id": "6xeLX5T2pJ_g"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "filenames = [data_dir + filename for filename in os.listdir(data_dir)]\n",
        "filenames\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2A9u9yXQqEja",
        "outputId": "4c4932a1-147b-4cfa-9b86-4b44a6718331"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/dev.txt',\n",
              " 'pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/test.txt',\n",
              " 'pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/train.txt']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Preprocessing"
      ],
      "metadata": {
        "id": "EjBUmg__qjzX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lines(filename):\n",
        "  with open(filename, \"r\") as f:\n",
        "    return f.readlines()"
      ],
      "metadata": {
        "id": "onzw6M8qqzx0"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_lines = get_lines(data_dir+\"train.txt\")\n",
        "train_lines[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6Gemt7KrrSh",
        "outputId": "dcf1a2dd-1b26-49cb-8ba5-a5a1de7e8b0f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['###24293578\\n',\n",
              " 'OBJECTIVE\\tTo investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( OA ) .\\n',\n",
              " 'METHODS\\tA total of @ patients with primary knee OA were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .\\n',\n",
              " 'METHODS\\tOutcome measures included pain reduction and improvement in function scores and systemic inflammation markers .\\n',\n",
              " 'METHODS\\tPain was assessed using the visual analog pain scale ( @-@ mm ) .\\n',\n",
              " 'METHODS\\tSecondary outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index scores , patient global assessment ( PGA ) of the severity of knee OA , and @-min walk distance ( @MWD ) .\\n',\n",
              " 'METHODS\\tSerum levels of interleukin @ ( IL-@ ) , IL-@ , tumor necrosis factor ( TNF ) - , and high-sensitivity C-reactive protein ( hsCRP ) were measured .\\n',\n",
              " 'RESULTS\\tThere was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , PGA , and @MWD at @ weeks .\\n',\n",
              " 'RESULTS\\tThe mean difference between treatment arms ( @ % CI ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .\\n',\n",
              " 'RESULTS\\tFurther , there was a clinically relevant reduction in the serum levels of IL-@ , IL-@ , TNF - , and hsCRP at @ weeks in the intervention group when compared to the placebo group .\\n',\n",
              " 'RESULTS\\tThese differences remained significant at @ weeks .\\n',\n",
              " 'RESULTS\\tThe Outcome Measures in Rheumatology Clinical Trials-Osteoarthritis Research Society International responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ) .\\n',\n",
              " 'CONCLUSIONS\\tLow-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee OA ( ClinicalTrials.gov identifier NCT@ ) .\\n',\n",
              " '\\n',\n",
              " '###24854809\\n',\n",
              " 'BACKGROUND\\tEmotional eating is associated with overeating and the development of obesity .\\n',\n",
              " 'BACKGROUND\\tYet , empirical evidence for individual ( trait ) differences in emotional eating and cognitive mechanisms that contribute to eating during sad mood remain equivocal .\\n',\n",
              " 'OBJECTIVE\\tThe aim of this study was to test if attention bias for food moderates the effect of self-reported emotional eating during sad mood ( vs neutral mood ) on actual food intake .\\n',\n",
              " 'OBJECTIVE\\tIt was expected that emotional eating is predictive of elevated attention for food and higher food intake after an experimentally induced sad mood and that attentional maintenance on food predicts food intake during a sad versus a neutral mood .\\n',\n",
              " 'METHODS\\tParticipants ( N = @ ) were randomly assigned to one of the two experimental mood induction conditions ( sad/neutral ) .\\n']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_lines)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCHPz4_Usoqe",
        "outputId": "4f714bdc-8438-413a-f062-247fe4fcc60d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "210040"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_text_with_line_numbers(filename):\n",
        "  input_lines = get_lines(filename)\n",
        "  abstract_lines = \"\"\n",
        "  abstract_samples = []\n",
        "\n",
        "  # Loop through each line in target file\n",
        "  for line in input_lines:\n",
        "    if line.startswith(\"###\"): # check to see if line is an ID line\n",
        "      abstract_id = line\n",
        "      abstract_lines = \"\" # reset abstract string\n",
        "    elif line.isspace(): # check to see if line is a new line\n",
        "      abstract_line_split = abstract_lines.splitlines() # split abstract into separate lines\n",
        "\n",
        "      # Iterate through each line in abstract and count them at the same time\n",
        "      for abstract_line_number, abstract_line in enumerate(abstract_line_split):\n",
        "        line_data = {} # create empty dict to store data from line\n",
        "        target_text_split = abstract_line.split(\"\\t\") # split target label from text\n",
        "        line_data[\"target\"] = target_text_split[0] # get target label\n",
        "        line_data[\"text\"] = target_text_split[1].lower() # get target text and lower it\n",
        "        line_data[\"line_number\"] = abstract_line_number # what number line does the line appear in the abstract?\n",
        "        line_data[\"total_lines\"] = len(abstract_line_split) - 1 # how many total lines are in the abstract? (start from 0)\n",
        "        abstract_samples.append(line_data) # add line data to abstract samples list\n",
        "\n",
        "    else: # if the above conditions aren't fulfilled, the line contains a labelled sentence\n",
        "      abstract_lines += line\n",
        "\n",
        "  return abstract_samples\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "XONBjpCSss7K"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "train_samples = preprocess_text_with_line_numbers(data_dir + \"train.txt\")\n",
        "val_samples = preprocess_text_with_line_numbers(data_dir + \"dev.txt\") # dev is another name for validation set\n",
        "test_samples = preprocess_text_with_line_numbers(data_dir + \"test.txt\")\n",
        "len(train_samples), len(val_samples), len(test_samples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hp4jD57kxsQY",
        "outputId": "9b108cd4-c2ba-42e4-df7c-21efdeecfa30"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 427 ms, sys: 83.7 ms, total: 511 ms\n",
            "Wall time: 513 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180040, 30212, 30135)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_samples[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIcMQw93yOdl",
        "outputId": "cd6e6b67-b2af-4d0c-ea5e-4ba130102ab0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'target': 'OBJECTIVE',\n",
              "  'text': 'to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
              "  'line_number': 0,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
              "  'line_number': 1,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
              "  'line_number': 2,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
              "  'line_number': 3,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .',\n",
              "  'line_number': 4,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'serum levels of interleukin @ ( il-@ ) , il-@ , tumor necrosis factor ( tnf ) - , and high-sensitivity c-reactive protein ( hscrp ) were measured .',\n",
              "  'line_number': 5,\n",
              "  'total_lines': 11},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'there was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , pga , and @mwd at @ weeks .',\n",
              "  'line_number': 6,\n",
              "  'total_lines': 11},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'the mean difference between treatment arms ( @ % ci ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .',\n",
              "  'line_number': 7,\n",
              "  'total_lines': 11},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'further , there was a clinically relevant reduction in the serum levels of il-@ , il-@ , tnf - , and hscrp at @ weeks in the intervention group when compared to the placebo group .',\n",
              "  'line_number': 8,\n",
              "  'total_lines': 11},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'these differences remained significant at @ weeks .',\n",
              "  'line_number': 9,\n",
              "  'total_lines': 11},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'the outcome measures in rheumatology clinical trials-osteoarthritis research society international responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ) .',\n",
              "  'line_number': 10,\n",
              "  'total_lines': 11},\n",
              " {'target': 'CONCLUSIONS',\n",
              "  'text': 'low-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee oa ( clinicaltrials.gov identifier nct@ ) .',\n",
              "  'line_number': 11,\n",
              "  'total_lines': 11},\n",
              " {'target': 'BACKGROUND',\n",
              "  'text': 'emotional eating is associated with overeating and the development of obesity .',\n",
              "  'line_number': 0,\n",
              "  'total_lines': 10},\n",
              " {'target': 'BACKGROUND',\n",
              "  'text': 'yet , empirical evidence for individual ( trait ) differences in emotional eating and cognitive mechanisms that contribute to eating during sad mood remain equivocal .',\n",
              "  'line_number': 1,\n",
              "  'total_lines': 10},\n",
              " {'target': 'OBJECTIVE',\n",
              "  'text': 'the aim of this study was to test if attention bias for food moderates the effect of self-reported emotional eating during sad mood ( vs neutral mood ) on actual food intake .',\n",
              "  'line_number': 2,\n",
              "  'total_lines': 10},\n",
              " {'target': 'OBJECTIVE',\n",
              "  'text': 'it was expected that emotional eating is predictive of elevated attention for food and higher food intake after an experimentally induced sad mood and that attentional maintenance on food predicts food intake during a sad versus a neutral mood .',\n",
              "  'line_number': 3,\n",
              "  'total_lines': 10},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'participants ( n = @ ) were randomly assigned to one of the two experimental mood induction conditions ( sad/neutral ) .',\n",
              "  'line_number': 4,\n",
              "  'total_lines': 10},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'attentional biases for high caloric foods were measured by eye tracking during a visual probe task with pictorial food and neutral stimuli .',\n",
              "  'line_number': 5,\n",
              "  'total_lines': 10},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'self-reported emotional eating was assessed with the dutch eating behavior questionnaire ( debq ) and ad libitum food intake was tested by a disguised food offer .',\n",
              "  'line_number': 6,\n",
              "  'total_lines': 10},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'hierarchical multivariate regression modeling showed that self-reported emotional eating did not account for changes in attention allocation for food or food intake in either condition .',\n",
              "  'line_number': 7,\n",
              "  'total_lines': 10}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "train_df = pd.DataFrame(train_samples)\n",
        "val_df = pd.DataFrame(val_samples)\n",
        "test_df = pd.DataFrame(test_samples)\n",
        "train_df[:14]\n"
      ],
      "metadata": {
        "id": "Yjec0mZ6sXbx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "5eeeab9f-5e68-4404-adb2-1644581c95a7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         target                                               text  \\\n",
              "0     OBJECTIVE  to investigate the efficacy of @ weeks of dail...   \n",
              "1       METHODS  a total of @ patients with primary knee oa wer...   \n",
              "2       METHODS  outcome measures included pain reduction and i...   \n",
              "3       METHODS  pain was assessed using the visual analog pain...   \n",
              "4       METHODS  secondary outcome measures included the wester...   \n",
              "5       METHODS  serum levels of interleukin @ ( il-@ ) , il-@ ...   \n",
              "6       RESULTS  there was a clinically relevant reduction in t...   \n",
              "7       RESULTS  the mean difference between treatment arms ( @...   \n",
              "8       RESULTS  further , there was a clinically relevant redu...   \n",
              "9       RESULTS  these differences remained significant at @ we...   \n",
              "10      RESULTS  the outcome measures in rheumatology clinical ...   \n",
              "11  CONCLUSIONS  low-dose oral prednisolone had both a short-te...   \n",
              "12   BACKGROUND  emotional eating is associated with overeating...   \n",
              "13   BACKGROUND  yet , empirical evidence for individual ( trai...   \n",
              "\n",
              "    line_number  total_lines  \n",
              "0             0           11  \n",
              "1             1           11  \n",
              "2             2           11  \n",
              "3             3           11  \n",
              "4             4           11  \n",
              "5             5           11  \n",
              "6             6           11  \n",
              "7             7           11  \n",
              "8             8           11  \n",
              "9             9           11  \n",
              "10           10           11  \n",
              "11           11           11  \n",
              "12            0           10  \n",
              "13            1           10  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2047f0e0-ec1c-4040-97ac-53542013e993\" 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>target</th>\n",
              "      <th>text</th>\n",
              "      <th>line_number</th>\n",
              "      <th>total_lines</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>to investigate the efficacy of @ weeks of dail...</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>METHODS</td>\n",
              "      <td>a total of @ patients with primary knee oa wer...</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>METHODS</td>\n",
              "      <td>outcome measures included pain reduction and i...</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>METHODS</td>\n",
              "      <td>pain was assessed using the visual analog pain...</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>METHODS</td>\n",
              "      <td>secondary outcome measures included the wester...</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>METHODS</td>\n",
              "      <td>serum levels of interleukin @ ( il-@ ) , il-@ ...</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>there was a clinically relevant reduction in t...</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>the mean difference between treatment arms ( @...</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>further , there was a clinically relevant redu...</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>these differences remained significant at @ we...</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>the outcome measures in rheumatology clinical ...</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>CONCLUSIONS</td>\n",
              "      <td>low-dose oral prednisolone had both a short-te...</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>BACKGROUND</td>\n",
              "      <td>emotional eating is associated with overeating...</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>BACKGROUND</td>\n",
              "      <td>yet , empirical evidence for individual ( trai...</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"train_df[:14]\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"METHODS\",\n          \"BACKGROUND\",\n          \"RESULTS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"these differences remained significant at @ weeks .\",\n          \"low-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee oa ( clinicaltrials.gov identifier nct@ ) .\",\n          \"to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"line_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 11,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          10,\n          9,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_lines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 10,\n        \"max\": 11,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.target.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "baZKK3JQyCTC",
        "outputId": "086d493c-1d61-4e1b-9048-d233e8261808"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "METHODS        59353\n",
              "RESULTS        57953\n",
              "CONCLUSIONS    27168\n",
              "BACKGROUND     21727\n",
              "OBJECTIVE      13839\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.total_lines.plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "YMv4rwHayMWR",
        "outputId": "b045a2c0-5aed-4b23-dd92-68c2d817bb6f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences = train_df[\"text\"].tolist()\n",
        "val_sentences = val_df[\"text\"].tolist()\n",
        "test_sentences = test_df[\"text\"].tolist()\n",
        "train_sentences[:10],len(train_sentences),len(val_sentences),len(test_sentences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lN-wQdLzyZbE",
        "outputId": "188ef77a-5738-4669-a3e1-557471609981"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
              "  'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
              "  'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
              "  'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
              "  'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .',\n",
              "  'serum levels of interleukin @ ( il-@ ) , il-@ , tumor necrosis factor ( tnf ) - , and high-sensitivity c-reactive protein ( hscrp ) were measured .',\n",
              "  'there was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , pga , and @mwd at @ weeks .',\n",
              "  'the mean difference between treatment arms ( @ % ci ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .',\n",
              "  'further , there was a clinically relevant reduction in the serum levels of il-@ , il-@ , tnf - , and hscrp at @ weeks in the intervention group when compared to the placebo group .',\n",
              "  'these differences remained significant at @ weeks .'],\n",
              " 180040,\n",
              " 30212,\n",
              " 30135)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Make Numeric Labels"
      ],
      "metadata": {
        "id": "IhdN7efizTws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#one hot encode labels\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "one_hot_encoder = OneHotEncoder(sparse=False)\n",
        "train_labels_one_hot = one_hot_encoder.fit_transform(train_df[\"target\"].to_numpy().reshape(-1,1))\n",
        "val_labels_one_hot = one_hot_encoder.transform(val_df[\"target\"].to_numpy().reshape(-1,1))\n",
        "test_labels_one_hot = one_hot_encoder.transform(test_df[\"target\"].to_numpy().reshape(-1,1))\n",
        "train_labels_one_hot , val_labels_one_hot , test_labels_one_hot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEptUbzFzphs",
        "outputId": "4376256c-1212-4ca4-eb27-9868458d5c45"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[0., 0., 0., 1., 0.],\n",
              "        [0., 0., 1., 0., 0.],\n",
              "        [0., 0., 1., 0., 0.],\n",
              "        ...,\n",
              "        [0., 0., 0., 0., 1.],\n",
              "        [0., 1., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 0.]]),\n",
              " array([[1., 0., 0., 0., 0.],\n",
              "        [1., 0., 0., 0., 0.],\n",
              "        [0., 0., 0., 1., 0.],\n",
              "        ...,\n",
              "        [0., 0., 0., 0., 1.],\n",
              "        [0., 1., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 0.]]),\n",
              " array([[1., 0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 1.],\n",
              "        [0., 0., 0., 0., 1.],\n",
              "        ...,\n",
              "        [0., 0., 0., 0., 1.],\n",
              "        [0., 0., 0., 0., 1.],\n",
              "        [0., 1., 0., 0., 0.]]))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.constant(train_labels_one_hot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GG8YVJgE0jA3",
        "outputId": "aef33fd1-9aec-4ef9-e1c0-f78f7e0fce6f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(180040, 5), dtype=float64, numpy=\n",
              "array([[0., 0., 0., 1., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., 0., 1.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.]])>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "train_labels_encoded = label_encoder.fit_transform(train_df[\"target\"].to_numpy())\n",
        "val_labels_encoded = label_encoder.transform(val_df[\"target\"].to_numpy())\n",
        "test_labels_encoded = label_encoder.transform(test_df[\"target\"].to_numpy())\n",
        "\n",
        "train_labels_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUjQDMDJ1NJd",
        "outputId": "7c635f1e-1fb4-4023-bca5-a58d713515aa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 2, ..., 4, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get Classes\n",
        "num_classes = len(label_encoder.classes_)\n",
        "class_names = label_encoder.classes_\n",
        "num_classes,class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzhN_w--17Y3",
        "outputId": "1ce8f34f-a9df-49e0-e672-3199f1c56a2f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5,\n",
              " array(['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'],\n",
              "       dtype=object))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##MODEL-0 (Naive-Bayes)"
      ],
      "metadata": {
        "id": "iLk12XoAAwDV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "model_0 = Pipeline([\n",
        "    (\"tf-idf\",TfidfVectorizer()),\n",
        "    (\"clf\",MultinomialNB())\n",
        "])\n",
        "\n",
        "model_0.fit(train_sentences,train_labels_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "i3gfQQwdBes-",
        "outputId": "973b02d9-5ebe-47ac-be9e-4d7aca11e4fd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tf-idf', TfidfVectorizer()), ('clf', MultinomialNB())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tf-idf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tf-idf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.score(val_sentences,val_labels_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxdkWSy9Chyn",
        "outputId": "672a2a2c-ad59-4212-b740-d094250ccae7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7218323844829869"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_preds = model_0.predict(val_sentences)\n",
        "baseline_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NpW1HgPC0_j",
        "outputId": "96c4ec1a-0119-4fd6-8fb7-76b3f2ca98f8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 1, 3, ..., 4, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3bE4wFYC8id",
        "outputId": "c0c3f7a2-d4cb-44b8-b6d2-626c88882c8a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-06-21 08:22:26--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10246 (10K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "\rhelper_functions.py   0%[                    ]       0  --.-KB/s               \rhelper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-06-21 08:22:26 (87.2 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import calculate_results,plot_loss_curves,compare_historys"
      ],
      "metadata": {
        "id": "cAs5ItWHDI3y"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results = calculate_results(val_labels_encoded,baseline_preds)\n",
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDNisfGuDXPj",
        "outputId": "bab895b9-16ce-4fd7-8c43-ef424388a261"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 72.1832384482987,\n",
              " 'precision': 0.7186466952323352,\n",
              " 'recall': 0.7218323844829869,\n",
              " 'f1': 0.6989250353450294}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text-Vectorization and Embedding"
      ],
      "metadata": {
        "id": "FBgKu28IDqHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "YUYNzcnuD2nu"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences[:10]"
      ],
      "metadata": {
        "id": "dXGjFs7FGE4q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1250bd9e-b0d0-4c42-8d48-3f9450386b3f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
              " 'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
              " 'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
              " 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
              " 'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .',\n",
              " 'serum levels of interleukin @ ( il-@ ) , il-@ , tumor necrosis factor ( tnf ) - , and high-sensitivity c-reactive protein ( hscrp ) were measured .',\n",
              " 'there was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , pga , and @mwd at @ weeks .',\n",
              " 'the mean difference between treatment arms ( @ % ci ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .',\n",
              " 'further , there was a clinically relevant reduction in the serum levels of il-@ , il-@ , tnf - , and hscrp at @ weeks in the intervention group when compared to the placebo group .',\n",
              " 'these differences remained significant at @ weeks .']"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sent_lens = [len(sentence.split()) for sentence in train_sentences]\n",
        "avg_sent_len = np.mean(sent_lens)\n",
        "avg_sent_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7yNr3FY2V3v",
        "outputId": "4514464b-fd0c-4241-a9d7-78e602ac236f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.338269273494777"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.hist(sent_lens,bins=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 828
        },
        "id": "VxSOQ9pP2wnZ",
        "outputId": "7d4bf313-8d8b-43bc-f057-0cd6f9db9c7a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([6.4740e+03, 2.9819e+04, 3.6870e+04, 3.9381e+04, 2.4304e+04,\n",
              "        1.8048e+04, 9.0460e+03, 6.4160e+03, 3.3100e+03, 2.3210e+03,\n",
              "        1.4530e+03, 7.9400e+02, 5.9300e+02, 3.8200e+02, 2.6500e+02,\n",
              "        1.5100e+02, 1.2200e+02, 8.1000e+01, 5.8000e+01, 4.0000e+01,\n",
              "        1.8000e+01, 2.5000e+01, 8.0000e+00, 1.2000e+01, 6.0000e+00,\n",
              "        7.0000e+00, 4.0000e+00, 4.0000e+00, 4.0000e+00, 4.0000e+00,\n",
              "        6.0000e+00, 1.0000e+00, 5.0000e+00, 1.0000e+00, 1.0000e+00,\n",
              "        1.0000e+00, 2.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00,\n",
              "        0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
              " array([  1.        ,   7.55555556,  14.11111111,  20.66666667,\n",
              "         27.22222222,  33.77777778,  40.33333333,  46.88888889,\n",
              "         53.44444444,  60.        ,  66.55555556,  73.11111111,\n",
              "         79.66666667,  86.22222222,  92.77777778,  99.33333333,\n",
              "        105.88888889, 112.44444444, 119.        , 125.55555556,\n",
              "        132.11111111, 138.66666667, 145.22222222, 151.77777778,\n",
              "        158.33333333, 164.88888889, 171.44444444, 178.        ,\n",
              "        184.55555556, 191.11111111, 197.66666667, 204.22222222,\n",
              "        210.77777778, 217.33333333, 223.88888889, 230.44444444,\n",
              "        237.        , 243.55555556, 250.11111111, 256.66666667,\n",
              "        263.22222222, 269.77777778, 276.33333333, 282.88888889,\n",
              "        289.44444444, 296.        ]),\n",
              " <BarContainer object of 45 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_seq_len = int(np.percentile(sent_lens,95))\n",
        "output_seq_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vm8aew5V3Gfw",
        "outputId": "587f29b8-ea34-4960-c24f-c06575d662ca"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(sent_lens)\n",
        "max_tokens=68000 ## From paper Table-2"
      ],
      "metadata": {
        "id": "v0B6lK-H3p7e"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import TextVectorization\n",
        "\n",
        "text_vectorizer = TextVectorization(max_tokens=max_tokens,\n",
        "                                    output_sequence_length=output_seq_len)"
      ],
      "metadata": {
        "id": "uwmjYVWt35RX"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_vectorizer.adapt(train_sentences)"
      ],
      "metadata": {
        "id": "IYPlQHo6Pfkg"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "target_sentence = random.choice(train_sentences)\n",
        "print(f\"Text:\\n {target_sentence}\")\n",
        "print(f\"length of Text: {len(target_sentence)}\")\n",
        "print(f\"Vectorized_Text:\\n {text_vectorizer(target_sentence)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgmzrggePmhz",
        "outputId": "e8db8e19-2f0b-4603-988e-97d551a10fc5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text:\n",
            " after a @ year follow-up period involving @ patients who had their submandibular glands transferred for the prevention of xerostomia after radiotherapy for nasopharyngeal carcinoma , we found that clinical efficacy was good .\n",
            "length of Text: 225\n",
            "Vectorized_Text:\n",
            " [   21     8   157    94   173  1267    12    67    55   120 14407  7218\n",
            "  5531    11     2   403     4  8534    21  1053    11  6372  1440    43\n",
            "   208    28    47    79    10   745     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rct_20k_text_vocab = text_vectorizer.get_vocabulary()\n",
        "print(f\"Number of words in vocab: {len(rct_20k_text_vocab)}\")\n",
        "print(f\"Most common words: {rct_20k_text_vocab[:5]}\")\n",
        "print(f\"Least common: {rct_20k_text_vocab[-5:]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufpt7_IyQPl4",
        "outputId": "6a6d8f2f-f403-4fda-d6c2-cf37e31771b5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words in vocab: 64841\n",
            "Most common words: ['', '[UNK]', 'the', 'and', 'of']\n",
            "Least common: ['aainduced', 'aaigroup', 'aachener', 'aachen', 'aaacp']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_vectorizer.get_config()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hY4GQ-PYQ1yz",
        "outputId": "f5c552b4-88cf-4e1c-e628-d5a2471aa1bf"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'text_vectorization',\n",
              " 'trainable': True,\n",
              " 'dtype': 'string',\n",
              " 'batch_input_shape': (None,),\n",
              " 'max_tokens': 68000,\n",
              " 'standardize': 'lower_and_strip_punctuation',\n",
              " 'split': 'whitespace',\n",
              " 'ngrams': None,\n",
              " 'output_mode': 'int',\n",
              " 'output_sequence_length': 55,\n",
              " 'pad_to_max_tokens': False,\n",
              " 'sparse': False,\n",
              " 'ragged': False,\n",
              " 'vocabulary': None,\n",
              " 'idf_weights': None,\n",
              " 'encoding': 'utf-8',\n",
              " 'vocabulary_size': 64841}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Embedding\n",
        "\n",
        "token_embed = Embedding(input_dim=len(rct_20k_text_vocab),\n",
        "                       output_dim = 128,\n",
        "                       mask_zero=True,\n",
        "                       name=\"token_embedding\")\n"
      ],
      "metadata": {
        "id": "r0qWWyfDQ_eN"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Before Vectorization:{target_sentence}\")\n",
        "vectorized_sentence = text_vectorizer(target_sentence)\n",
        "print(f\"after Vectorization :\\n {vectorized_sentence}\")\n",
        "embed_sentence = token_embed(vectorized_sentence)\n",
        "print(f\"after Embedding :\\n {embed_sentence}\")\n",
        "print(f\"Shape of embed sentence: {len(embed_sentence)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bJm6Ec8RoCq",
        "outputId": "7f660a51-5f9d-4436-8ec7-f440216d9ad4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before Vectorization:after a @ year follow-up period involving @ patients who had their submandibular glands transferred for the prevention of xerostomia after radiotherapy for nasopharyngeal carcinoma , we found that clinical efficacy was good .\n",
            "after Vectorization :\n",
            " [   21     8   157    94   173  1267    12    67    55   120 14407  7218\n",
            "  5531    11     2   403     4  8534    21  1053    11  6372  1440    43\n",
            "   208    28    47    79    10   745     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0]\n",
            "after Embedding :\n",
            " [[ 0.02425926  0.01999519  0.02440706 ...  0.01190733 -0.01517247\n",
            "  -0.04215983]\n",
            " [ 0.00568416  0.04418049 -0.04797649 ...  0.01881174  0.02638653\n",
            "   0.02804578]\n",
            " [-0.00573945  0.00092453 -0.0401775  ...  0.02251977 -0.03428044\n",
            "  -0.00820794]\n",
            " ...\n",
            " [-0.03582206  0.04109992 -0.04948744 ... -0.02708081 -0.02646222\n",
            "   0.00157434]\n",
            " [-0.03582206  0.04109992 -0.04948744 ... -0.02708081 -0.02646222\n",
            "   0.00157434]\n",
            " [-0.03582206  0.04109992 -0.04948744 ... -0.02708081 -0.02646222\n",
            "   0.00157434]]\n",
            "Shape of embed sentence: 55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Using TF-DATA API\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((train_sentences,train_labels_one_hot))\n",
        "valid_dataset = tf.data.Dataset.from_tensor_slices((val_sentences,val_labels_one_hot))\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((test_sentences, test_labels_one_hot))\n",
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xD3K-ACTSjk",
        "outputId": "d2347afb-9513-4720-99d8-65a3f21e20fe"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_TensorSliceDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(5,), dtype=tf.float64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels_one_hot.shape , train_labels_one_hot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSqRqvg8VGgH",
        "outputId": "b9dfcc46-3738-41d4-b102-d43bc09ff43a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((180040, 5),\n",
              " array([[0., 0., 0., 1., 0.],\n",
              "        [0., 0., 1., 0., 0.],\n",
              "        [0., 0., 1., 0., 0.],\n",
              "        ...,\n",
              "        [0., 0., 0., 0., 1.],\n",
              "        [0., 1., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 0.]]))"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = train_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "valid_dataset = valid_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "test_dataset = test_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "train_dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m9qtrXw4VSWR",
        "outputId": "61428d6b-c29a-47d1-8d8c-738b39b34720"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = layers.Input(shape=(1,),dtype=tf.string)\n",
        "x = text_vectorizer(inputs)\n",
        "x = token_embed(x)\n",
        "x = layers.Conv1D(64,kernel_size=5,padding=\"same\",activation=\"relu\")(x)\n",
        "x = layers.GlobalAveragePooling1D()(x)\n",
        "outputs = layers.Dense(num_classes,activation=\"softmax\")(x)\n",
        "model_1 = tf.keras.Model(inputs,outputs,name=\"model_1_conv1d\")\n",
        "\n",
        "model_1.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics = [\"accuracy\"])\n"
      ],
      "metadata": {
        "id": "f31h8lRTtPEt"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w971eHHMvqGY",
        "outputId": "7e9df887-2d8d-4f9a-c974-df33a3f4d36d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1_conv1d\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 1)]               0         \n",
            "                                                                 \n",
            " text_vectorization (TextVe  (None, 55)                0         \n",
            " ctorization)                                                    \n",
            "                                                                 \n",
            " token_embedding (Embedding  (None, 55, 128)           8299648   \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 55, 64)            41024     \n",
            "                                                                 \n",
            " global_average_pooling1d (  (None, 64)                0         \n",
            " GlobalAveragePooling1D)                                         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8340997 (31.82 MB)\n",
            "Trainable params: 8340997 (31.82 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_history=model_1.fit(train_dataset,steps_per_epoch=int(0.1*len(train_dataset)),\n",
        "                            epochs=3,validation_data=valid_dataset,\n",
        "                            validation_steps=int(0.1*len(valid_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfEd-_0EvxMA",
        "outputId": "49a70fab-17da-4f20-e5a3-9e44b444f6e2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 20s 30ms/step - loss: 0.9174 - accuracy: 0.6363 - val_loss: 0.6867 - val_accuracy: 0.7374\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 6s 10ms/step - loss: 0.6547 - accuracy: 0.7583 - val_loss: 0.6281 - val_accuracy: 0.7736\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 4s 8ms/step - loss: 0.6150 - accuracy: 0.7766 - val_loss: 0.5964 - val_accuracy: 0.7829\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.evaluate(valid_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOI_O1LoxfXX",
        "outputId": "3a65e0d3-209e-44ca-bda8-7674665ea277"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 3s 3ms/step - loss: 0.5958 - accuracy: 0.7860\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5958164930343628, 0.7859790921211243]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_pred_probs = model_1.predict(valid_dataset)\n",
        "model_1_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Ae-82D3xl0M",
        "outputId": "c47eeb3c-7f64-4c59-d184-f24f5f029eea"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 2s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.2833999e-01, 1.5030429e-01, 7.3464565e-02, 3.2415557e-01,\n",
              "        2.3735618e-02],\n",
              "       [4.4051638e-01, 2.7958858e-01, 1.4013732e-02, 2.5493461e-01,\n",
              "        1.0946692e-02],\n",
              "       [1.5930218e-01, 6.2221247e-03, 1.4259139e-03, 8.3300585e-01,\n",
              "        4.4033048e-05],\n",
              "       ...,\n",
              "       [3.6782128e-06, 6.1428384e-04, 6.6290383e-04, 4.9803489e-06,\n",
              "        9.9871421e-01],\n",
              "       [4.6655640e-02, 4.4584754e-01, 9.9597879e-02, 5.8879327e-02,\n",
              "        3.4901965e-01],\n",
              "       [1.8911988e-01, 7.0544457e-01, 3.6242336e-02, 3.8364153e-02,\n",
              "        3.0829115e-02]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_pred = tf.argmax(model_1_pred_probs,axis=1)\n",
        "model_1_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aks2KBwqxxmW",
        "outputId": "d39f3a89-c6a2-4625-9ad6-ca34c864658e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 0, 3, ..., 4, 1, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_results=calculate_results(val_labels_encoded,model_1_pred)\n",
        "model_1_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0fkZN4GyAj6",
        "outputId": "c6142921-fcbc-428d-8b48-48eb114bb66b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 78.5979081159804,\n",
              " 'precision': 0.7826217394421506,\n",
              " 'recall': 0.7859790811598041,\n",
              " 'f1': 0.7832780087409795}"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msK9oc0wySfJ",
        "outputId": "76167454-3b68-4495-a499-731a9529b948"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 72.1832384482987,\n",
              " 'precision': 0.7186466952323352,\n",
              " 'recall': 0.7218323844829869,\n",
              " 'f1': 0.6989250353450294}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MODEL-2 (FEATURE-EXTRACTION)"
      ],
      "metadata": {
        "id": "ea64i93vytXM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_hub as hub\n",
        "\n",
        "tf_hub = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder/4\",\n",
        "                        trainable=False,\n",
        "                        name=\"USE\")"
      ],
      "metadata": {
        "id": "tDK49dAXyzOh"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_training_sentence = random.choice(train_sentences)\n",
        "print(f\"Random training sentence:\\n{random_training_sentence}\\n\")\n",
        "use_embedded_sentence = tf_hub([random_training_sentence])\n",
        "print(f\"Sentence after embedding:\\n{use_embedded_sentence[0][:30]} (truncated output)...\\n\")\n",
        "print(f\"Length of sentence embedding:\\n{len(use_embedded_sentence[0])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTc4BuAmzn2v",
        "outputId": "b8d109eb-e2e6-47de-854f-929e30d6d1ad"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random training sentence:\n",
            "there is some evidence to suggest that one or two doses of the hpv vaccine provides similar protection to the three-dose regimen .\n",
            "\n",
            "Sentence after embedding:\n",
            "[ 0.02234448  0.03878799 -0.06461541 -0.03770111 -0.07034132  0.024043\n",
            "  0.02644145 -0.01956193  0.0414215   0.06091356  0.08966491 -0.06403675\n",
            "  0.05034352  0.02638619  0.00721967 -0.03100551 -0.0898828  -0.03908058\n",
            "  0.00586319 -0.00552934  0.00170088  0.00825466 -0.07213596  0.05763002\n",
            "  0.05560753  0.07001543 -0.01992217 -0.02104607  0.08191874 -0.0089253 ] (truncated output)...\n",
            "\n",
            "Length of sentence embedding:\n",
            "512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = layers.Input(shape=[],dtype=tf.string)\n",
        "x = tf_hub(inputs)\n",
        "x = layers.Dense(128,activation=\"relu\")(x)\n",
        "outputs = layers.Dense(num_classes,activation=\"softmax\")(x)\n",
        "model_2 = tf.keras.Model(inputs,outputs,name=\"model_2_USE\")\n",
        "\n",
        "\n",
        "model_2.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "txnXmcRMzw40"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TbS2ou_1yYv",
        "outputId": "c6f48c23-06e5-433b-aae2-fe3732534e67"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2_USE\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None,)]                 0         \n",
            "                                                                 \n",
            " USE (KerasLayer)            (None, 512)               256797824 \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 5)                 645       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 256864133 (979.86 MB)\n",
            "Trainable params: 66309 (259.02 KB)\n",
            "Non-trainable params: 256797824 (979.61 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_history = model_2.fit(train_dataset,steps_per_epoch=int(0.1*len(train_dataset)),\n",
        "                              epochs=3,\n",
        "                              validation_data=valid_dataset,\n",
        "                              validation_steps=int(0.1*len(valid_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15ymZASY13le",
        "outputId": "1d7215a0-ee62-45d3-fa73-ce315820cc25"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 11s 15ms/step - loss: 0.9192 - accuracy: 0.6479 - val_loss: 0.8000 - val_accuracy: 0.6892\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 6s 11ms/step - loss: 0.7703 - accuracy: 0.7011 - val_loss: 0.7580 - val_accuracy: 0.7041\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 8s 14ms/step - loss: 0.7546 - accuracy: 0.7124 - val_loss: 0.7412 - val_accuracy: 0.7131\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.evaluate(valid_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZtdA7kG2bCi",
        "outputId": "d3438793-4cf8-43e5-f193-d19774ce9dca"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 10s 11ms/step - loss: 0.7430 - accuracy: 0.7133\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7429887652397156, 0.7132927179336548]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_pred_probs = model_2.predict(valid_dataset)\n",
        "model_2_pred_probs\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PO3Kng7Q2fOf",
        "outputId": "87005641-8368-4c1b-8fb0-e0debcd6bfb8"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 10s 10ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.43869165, 0.35109356, 0.00285744, 0.19839144, 0.00896594],\n",
              "       [0.3543674 , 0.49629474, 0.00399937, 0.14168938, 0.00364915],\n",
              "       [0.224039  , 0.18573804, 0.01569368, 0.5330691 , 0.04146013],\n",
              "       ...,\n",
              "       [0.00192076, 0.00507422, 0.04879864, 0.00108628, 0.94312006],\n",
              "       [0.00459797, 0.0506751 , 0.19877687, 0.00145692, 0.7444932 ],\n",
              "       [0.1752362 , 0.20232871, 0.5551821 , 0.00537849, 0.06187452]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_preds = tf.argmax(model_2_pred_probs,axis=1)\n",
        "model_2_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9OpYyXrt2nT8",
        "outputId": "52c603c2-948c-4ddf-fd63-598a53965c26"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 1, 3, ..., 4, 4, 2])>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_results=calculate_results(val_labels_encoded,model_2_preds)\n",
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkv46XRF2wB9",
        "outputId": "acef9bba-4e1e-4806-a67f-30a2d09d5fd9"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 71.32927313650205,\n",
              " 'precision': 0.7138731192547267,\n",
              " 'recall': 0.7132927313650205,\n",
              " 'f1': 0.7102622791130426}"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##MODEL-3 (CONV1D-CHARCTER-EMBEDDING)"
      ],
      "metadata": {
        "id": "AwD5Lbyr3GH8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6yTnZdY3OQV",
        "outputId": "a9e97bcc-b64a-422f-9b9d-520a1384117b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['to investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( oa ) .',\n",
              " 'a total of @ patients with primary knee oa were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .',\n",
              " 'outcome measures included pain reduction and improvement in function scores and systemic inflammation markers .',\n",
              " 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
              " 'secondary outcome measures included the western ontario and mcmaster universities osteoarthritis index scores , patient global assessment ( pga ) of the severity of knee oa , and @-min walk distance ( @mwd ) .']"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def split_chars(text):\n",
        "  return \" \".join(list(text))"
      ],
      "metadata": {
        "id": "EzQC0ABX4pJ7"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "split_chars(random_training_sentence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "N4Mesd7344Yz",
        "outputId": "fd5408c9-8b23-44ef-ac57-47a0c7803f3c"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'t h e r e   i s   s o m e   e v i d e n c e   t o   s u g g e s t   t h a t   o n e   o r   t w o   d o s e s   o f   t h e   h p v   v a c c i n e   p r o v i d e s   s i m i l a r   p r o t e c t i o n   t o   t h e   t h r e e - d o s e   r e g i m e n   .'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_chars = [split_chars(sentences) for sentences in train_sentences]\n",
        "val_chars = [split_chars(sentences) for sentences in val_sentences]\n",
        "test_chars = [split_chars(sentences) for sentences in test_sentences]\n",
        "train_chars[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2-YC8SY5Ta8",
        "outputId": "46cf380a-f8ec-4863-d1d9-aee8c3020ece"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['t o   i n v e s t i g a t e   t h e   e f f i c a c y   o f   @   w e e k s   o f   d a i l y   l o w - d o s e   o r a l   p r e d n i s o l o n e   i n   i m p r o v i n g   p a i n   ,   m o b i l i t y   ,   a n d   s y s t e m i c   l o w - g r a d e   i n f l a m m a t i o n   i n   t h e   s h o r t   t e r m   a n d   w h e t h e r   t h e   e f f e c t   w o u l d   b e   s u s t a i n e d   a t   @   w e e k s   i n   o l d e r   a d u l t s   w i t h   m o d e r a t e   t o   s e v e r e   k n e e   o s t e o a r t h r i t i s   (   o a   )   .',\n",
              " 'a   t o t a l   o f   @   p a t i e n t s   w i t h   p r i m a r y   k n e e   o a   w e r e   r a n d o m i z e d   @ : @   ;   @   r e c e i v e d   @   m g / d a y   o f   p r e d n i s o l o n e   a n d   @   r e c e i v e d   p l a c e b o   f o r   @   w e e k s   .',\n",
              " 'o u t c o m e   m e a s u r e s   i n c l u d e d   p a i n   r e d u c t i o n   a n d   i m p r o v e m e n t   i n   f u n c t i o n   s c o r e s   a n d   s y s t e m i c   i n f l a m m a t i o n   m a r k e r s   .',\n",
              " 'p a i n   w a s   a s s e s s e d   u s i n g   t h e   v i s u a l   a n a l o g   p a i n   s c a l e   (   @ - @   m m   )   .',\n",
              " 's e c o n d a r y   o u t c o m e   m e a s u r e s   i n c l u d e d   t h e   w e s t e r n   o n t a r i o   a n d   m c m a s t e r   u n i v e r s i t i e s   o s t e o a r t h r i t i s   i n d e x   s c o r e s   ,   p a t i e n t   g l o b a l   a s s e s s m e n t   (   p g a   )   o f   t h e   s e v e r i t y   o f   k n e e   o a   ,   a n d   @ - m i n   w a l k   d i s t a n c e   (   @ m w d   )   .']"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "char_lens = [len(sentences) for sentences in train_sentences]\n",
        "mean_char_len = np.mean(char_lens)\n",
        "mean_char_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1p6eAeDq5xRm",
        "outputId": "2c9da4d9-3bdc-44b2-967f-da3490724d8c"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149.3662574983337"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(char_lens,bins=7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "Zsgf_sRu6HZh",
        "outputId": "532279bd-a807-4d4e-fdf9-db570c2acd2a"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.41175e+05, 3.71110e+04, 1.60000e+03, 1.27000e+02, 2.10000e+01,\n",
              "        5.00000e+00, 1.00000e+00]),\n",
              " array([1.00000000e+00, 1.98857143e+02, 3.96714286e+02, 5.94571429e+02,\n",
              "        7.92428571e+02, 9.90285714e+02, 1.18814286e+03, 1.38600000e+03]),\n",
              " <BarContainer object of 7 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_seq_char_len = int(np.percentile(char_lens,95))\n",
        "output_seq_char_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfJUxfy06TBK",
        "outputId": "a3f47466-88cd-4909-ffc3-3be9693d9020"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "290"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "alphabet = string.ascii_lowercase + string.digits + string.punctuation\n",
        "alphabet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "jKBeGIql6ne2",
        "outputId": "4de8fc2a-7be9-46c6-c328-bc9bfc121536"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'abcdefghijklmnopqrstuvwxyz0123456789!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_CHAR_TOKENS = len(alphabet) + 2 # num characters in alphabet + space + OOV token\n",
        "char_vectorizer = TextVectorization(max_tokens=NUM_CHAR_TOKENS,\n",
        "                                    output_sequence_length=output_seq_char_len,\n",
        "                                    standardize=\"lower_and_strip_punctuation\",\n",
        "                                    name=\"char_vectorizer\")\n",
        "\n",
        "# Adapt character vectorizer to training characters\n",
        "char_vectorizer.adapt(train_chars)"
      ],
      "metadata": {
        "id": "U1Cso2Fd7Wh6"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "char_vocab = char_vectorizer.get_vocabulary()\n",
        "print(f\"Number of different characters in character vocab: {len(char_vocab)}\")\n",
        "print(f\"5 most common characters: {char_vocab[:5]}\")\n",
        "print(f\"5 least common characters: {char_vocab[-5:]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csvLzwqg717b",
        "outputId": "a9c099ee-f2cb-4024-e9b8-c7bf87d9610c"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of different characters in character vocab: 28\n",
            "5 most common characters: ['', '[UNK]', 'e', 't', 'i']\n",
            "5 least common characters: ['k', 'x', 'z', 'q', 'j']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_train_chars = random.choice(train_chars)\n",
        "print(f\"Charified text:\\n{random_train_chars}\")\n",
        "print(f\"\\nLength of chars: {len(random_train_chars.split())}\")\n",
        "vectorized_chars = char_vectorizer([random_train_chars])\n",
        "print(f\"\\nVectorized chars:\\n{vectorized_chars}\")\n",
        "print(f\"\\nLength of vectorized chars: {len(vectorized_chars[0])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dAv2MAog-K1z",
        "outputId": "60211267-91e9-4d2c-fbe0-9dfbe23f9814"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Charified text:\n",
            "n o   w o m e n   r e p o r t e d   a d v e r s e   e v e n t s   r e l a t e d   t o   s e x u a l   d y s f u n c t i o n   .\n",
            "\n",
            "Length of chars: 55\n",
            "\n",
            "Vectorized chars:\n",
            "[[ 6  7 20  7 15  2  6  8  2 14  7  8  3  2 10  5 10 21  2  8  9  2  2 21\n",
            "   2  6  3  9  8  2 12  5  3  2 10  3  7  9  2 24 16  5 12 10 19  9 17 16\n",
            "   6 11  3  4  7  6  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0]]\n",
            "\n",
            "Length of vectorized chars: 290\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "char_embed = layers.Embedding(input_dim=NUM_CHAR_TOKENS, # number of different characters\n",
        "                              output_dim=25, # embedding dimension of each character (same as Figure 1 in https://arxiv.org/pdf/1612.05251.pdf)\n",
        "                              mask_zero=True, # don't use masks (this messes up model_5 if set to True)\n",
        "                              name=\"char_embed\")"
      ],
      "metadata": {
        "id": "TjjVUuDJ-0nR"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Charified text (before vectorization and embedding):\\n{random_train_chars}\\n\")\n",
        "char_embed_example = char_embed(char_vectorizer([random_train_chars]))\n",
        "print(f\"Embedded chars (after vectorization and embedding):\\n{char_embed_example}\\n\")\n",
        "print(f\"Character embedding shape: {char_embed_example.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xw9_0OpuzUKx",
        "outputId": "142cb0ba-8c03-47b0-a578-0f0eac613300"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Charified text (before vectorization and embedding):\n",
            "n o   w o m e n   r e p o r t e d   a d v e r s e   e v e n t s   r e l a t e d   t o   s e x u a l   d y s f u n c t i o n   .\n",
            "\n",
            "Embedded chars (after vectorization and embedding):\n",
            "[[[ 0.01287954  0.01777444  0.02502671 ...  0.02689439 -0.00629449\n",
            "    0.00780978]\n",
            "  [-0.04198031  0.04111845 -0.01126319 ...  0.01967004  0.00622432\n",
            "    0.00098989]\n",
            "  [ 0.01487393  0.04327102 -0.00044736 ... -0.03195987 -0.00907553\n",
            "   -0.01158245]\n",
            "  ...\n",
            "  [ 0.03647672 -0.02974709  0.00117413 ... -0.01293577  0.01626667\n",
            "    0.00882627]\n",
            "  [ 0.03647672 -0.02974709  0.00117413 ... -0.01293577  0.01626667\n",
            "    0.00882627]\n",
            "  [ 0.03647672 -0.02974709  0.00117413 ... -0.01293577  0.01626667\n",
            "    0.00882627]]]\n",
            "\n",
            "Character embedding shape: (1, 290, 25)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = layers.Input(shape=(1,),dtype=tf.string)\n",
        "x = char_vectorizer(inputs)\n",
        "x = char_embed(x)\n",
        "x = layers.Conv1D(64,kernel_size=5,strides=1,padding=\"valid\",activation=\"relu\")(x)\n",
        "x = layers.GlobalMaxPool1D()(x)\n",
        "outputs = layers.Dense(num_classes,activation=\"softmax\")(x)\n",
        "model_3 = tf.keras.Model(inputs,outputs,name=\"model_3_char\")\n",
        "\n",
        "model_3.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "u-bIPzf7zepP"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QO6uXuzq0gxS",
        "outputId": "0fa9a078-9d46-4a47-cd55-a71b0aec0153"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3_char\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 1)]               0         \n",
            "                                                                 \n",
            " char_vectorizer (TextVecto  (None, 290)               0         \n",
            " rization)                                                       \n",
            "                                                                 \n",
            " char_embed (Embedding)      (None, 290, 25)           1750      \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 286, 64)           8064      \n",
            "                                                                 \n",
            " global_max_pooling1d (Glob  (None, 64)                0         \n",
            " alMaxPooling1D)                                                 \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10139 (39.61 KB)\n",
            "Trainable params: 10139 (39.61 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset_char = tf.data.Dataset.from_tensor_slices((train_chars,train_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "val_dataset_char = tf.data.Dataset.from_tensor_slices((val_chars,val_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "train_dataset_char"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-509E250y31",
        "outputId": "1d83e902-f8ba-49cb-8902-808a748e5366"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3_history = model_3.fit(train_dataset_char,steps_per_epoch=int(0.1*len(train_dataset_char)),\n",
        "                              epochs=3,\n",
        "                              validation_data=val_dataset_char,\n",
        "                              validation_steps=int(0.1*len(val_dataset_char)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuGMkYlr1wYg",
        "outputId": "ef1a6f24-ca6e-4b4e-d196-45d3748f3c64"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 6s 7ms/step - loss: 1.2658 - accuracy: 0.4855 - val_loss: 1.0575 - val_accuracy: 0.5841\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 3s 5ms/step - loss: 1.0269 - accuracy: 0.5849 - val_loss: 0.9692 - val_accuracy: 0.6193\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 3s 5ms/step - loss: 0.9525 - accuracy: 0.6208 - val_loss: 0.8928 - val_accuracy: 0.6499\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.evaluate(val_dataset_char)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Icrn2fUM2w9g",
        "outputId": "e57978f5-3163-49a8-975a-41c3a2c62f6f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 3s 3ms/step - loss: 0.9104 - accuracy: 0.6448\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.910420835018158, 0.6447768807411194]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3_pred_probs = model_3.predict(val_dataset_char)\n",
        "model_3_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Etc-dks3JW3",
        "outputId": "719222f4-463a-4289-e14b-5d74bbda2aed"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 4s 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.17632683, 0.385675  , 0.08497303, 0.3240501 , 0.02897515],\n",
              "       [0.07673981, 0.7370536 , 0.01269381, 0.0852275 , 0.08828531],\n",
              "       [0.09440174, 0.1791876 , 0.42512485, 0.2779458 , 0.02334008],\n",
              "       ...,\n",
              "       [0.03090329, 0.08930289, 0.14561827, 0.06799652, 0.666179  ],\n",
              "       [0.03413863, 0.11612435, 0.39443353, 0.04010538, 0.4151981 ],\n",
              "       [0.2807805 , 0.48362154, 0.11811961, 0.09906182, 0.0184165 ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3_pred = tf.argmax(model_3_pred_probs,axis=1)\n",
        "model_3_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9DtRTX-3UA0",
        "outputId": "2352750e-5cee-45b2-aa38-419117842714"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([1, 1, 2, ..., 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3_results = calculate_results(val_labels_encoded,model_3_pred)\n",
        "model_3_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t55jLyNy3ezy",
        "outputId": "99fc54a0-99d9-4b5c-d251-31e6d0e7ea95"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 64.47769098371508,\n",
              " 'precision': 0.6394055333390221,\n",
              " 'recall': 0.6447769098371507,\n",
              " 'f1': 0.6304336897422952}"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Model-4:(Model2+model3)"
      ],
      "metadata": {
        "id": "FJflRRDB5zdg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#token\n",
        "token_inputs = layers.Input(shape=[],dtype=tf.string,name=\"token_inputs\")\n",
        "token_embeded =  tf_hub(token_inputs)\n",
        "token_output = layers.Dense(128,activation=\"relu\")(token_embeded)\n",
        "token_model = tf.keras.Model(token_inputs,token_output,name=\"token_embedding\")\n",
        "\n",
        "#charcter\n",
        "char_inputs = layers.Input(shape=(1,),dtype=tf.string,name=\"char_inputs\")\n",
        "char_vector = char_vectorizer(char_inputs)\n",
        "char_embeded = char_embed(char_vector)\n",
        "char_output = layers.Bidirectional(layers.LSTM(24))(char_embeded)\n",
        "char_model = tf.keras.Model(char_inputs,char_output,name=\"char_biLSTM\")\n",
        "\n",
        "#Cocatenate\n",
        "token_char_concat = layers.Concatenate(name=\"token_char_hybrid\")([token_model.output,\n",
        "                                                                  char_model.output])\n",
        "\n",
        "#Adding in Dropout\n",
        "combined_dropout = layers.Dropout(0.5)(token_char_concat)\n",
        "combined_dense = layers.Dense(128,activation=\"relu\")(combined_dropout)\n",
        "final_dropout = layers.Dropout(0.5)(combined_dense)\n",
        "output_layer = layers.Dense(num_classes,activation=\"softmax\")(final_dropout)\n",
        "\n",
        "\n",
        "model_4 = tf.keras.Model(inputs=[token_model.input,char_model.input],\n",
        "                        outputs=output_layer,name=\"model_token_char_embedding\")"
      ],
      "metadata": {
        "id": "YPMXqh035cxl"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OP1603VN9Odc",
        "outputId": "5fee14c2-6e30-4519-b2f7-7314faa79374"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_token_char_embedding\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " char_inputs (InputLayer)    [(None, 1)]                  0         []                            \n",
            "                                                                                                  \n",
            " token_inputs (InputLayer)   [(None,)]                    0         []                            \n",
            "                                                                                                  \n",
            " char_vectorizer (TextVecto  (None, 290)                  0         ['char_inputs[0][0]']         \n",
            " rization)                                                                                        \n",
            "                                                                                                  \n",
            " USE (KerasLayer)            (None, 512)                  2567978   ['token_inputs[0][0]']        \n",
            "                                                          24                                      \n",
            "                                                                                                  \n",
            " char_embed (Embedding)      (None, 290, 25)              1750      ['char_vectorizer[1][0]']     \n",
            "                                                                                                  \n",
            " dense_4 (Dense)             (None, 128)                  65664     ['USE[1][0]']                 \n",
            "                                                                                                  \n",
            " bidirectional (Bidirection  (None, 48)                   9600      ['char_embed[1][0]']          \n",
            " al)                                                                                              \n",
            "                                                                                                  \n",
            " token_char_hybrid (Concate  (None, 176)                  0         ['dense_4[0][0]',             \n",
            " nate)                                                               'bidirectional[0][0]']       \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, 176)                  0         ['token_char_hybrid[0][0]']   \n",
            "                                                                                                  \n",
            " dense_5 (Dense)             (None, 128)                  22656     ['dropout[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)         (None, 128)                  0         ['dense_5[0][0]']             \n",
            "                                                                                                  \n",
            " dense_6 (Dense)             (None, 5)                    645       ['dropout_1[0][0]']           \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 256898139 (979.99 MB)\n",
            "Trainable params: 100315 (391.86 KB)\n",
            "Non-trainable params: 256797824 (979.61 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotiing model\n",
        "from keras.utils import plot_model\n",
        "plot_model(model_4,show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 876
        },
        "id": "tByBOFuD9jBE",
        "outputId": "95085b54-59f6-4f67-9e0b-35b06ca70b27"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "o219OwBo-PIU"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##TF.DATA\n",
        "train_char_token_data = tf.data.Dataset.from_tensor_slices((train_sentences,train_chars))\n",
        "train_char_token_labels = tf.data.Dataset.from_tensor_slices((train_labels_one_hot))\n",
        "train_char_token_dataset = tf.data.Dataset.zip((train_char_token_data,train_char_token_labels))\n",
        "\n",
        "train_char_token_dataset = train_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "\n",
        "val_char_token_data = tf.data.Dataset.from_tensor_slices((val_sentences,val_chars))\n",
        "val_char_token_labels = tf.data.Dataset.from_tensor_slices((val_labels_one_hot))\n",
        "val_char_token_dataset = tf.data.Dataset.zip((val_char_token_data,val_char_token_labels))\n",
        "\n",
        "val_char_token_dataset=val_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "My0h1AZJ_c7S"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_char_token_dataset,val_char_token_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfMmQD5wBVBW",
        "outputId": "c87548fc-050d-4b4b-c5f0-d67cee0b9a33"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<_PrefetchDataset element_spec=((TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>,\n",
              " <_PrefetchDataset element_spec=((TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4_history = model_4.fit(train_char_token_dataset, # train on dataset of token and characters\n",
        "                              steps_per_epoch=int(0.1 * len(train_char_token_dataset)),\n",
        "                              epochs=3,\n",
        "                              validation_data=val_char_token_dataset,\n",
        "                              validation_steps=int(0.1 * len(val_char_token_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjU7P7EEBhaD",
        "outputId": "7a07bc0a-addd-41f1-fd32-6ddb7a91bf17"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 39s 49ms/step - loss: 0.9789 - accuracy: 0.6115 - val_loss: 0.7824 - val_accuracy: 0.7018\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 25s 44ms/step - loss: 0.8049 - accuracy: 0.6906 - val_loss: 0.7159 - val_accuracy: 0.7277\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 23s 40ms/step - loss: 0.7784 - accuracy: 0.7026 - val_loss: 0.6918 - val_accuracy: 0.7377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4.evaluate(val_char_token_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPgIV3DHDEwk",
        "outputId": "3accf9ec-4d36-4e75-d7b2-db503fc38a29"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 18s 19ms/step - loss: 0.6937 - accuracy: 0.7354\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6937406063079834, 0.7353700399398804]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4_pred_probs = model_4.predict(val_char_token_dataset)\n",
        "model_4_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWDv4ZYsDIJR",
        "outputId": "b8109456-2e41-4cf7-ec10-b16d430ab93c"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 20s 18ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.64591891e-01, 3.68378788e-01, 5.24709793e-03, 1.53292775e-01,\n",
              "        8.48942157e-03],\n",
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              "        2.90003829e-02],\n",
              "       ...,\n",
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              "       [5.15116146e-03, 5.54981194e-02, 2.50754029e-01, 2.32579187e-03,\n",
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              "       [1.71832800e-01, 3.82270962e-01, 3.20302814e-01, 2.98017859e-02,\n",
              "        9.57916901e-02]], dtype=float32)"
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          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4_preds = tf.argmax(model_4_pred_probs, axis=1)\n",
        "model_4_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "kU6GsYJsDMPW",
        "outputId": "8b793e20-2390-4972-f34f-b4160e2d2baf"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 1, 3, ..., 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                    y_pred=model_4_preds)\n",
        "model_4_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "npbU_oNKDPVi",
        "outputId": "9c277c0c-03ac-4d4a-bec2-157d88cfe956"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 73.53700516351118,\n",
              " 'precision': 0.7365694052323898,\n",
              " 'recall': 0.7353700516351118,\n",
              " 'f1': 0.7321505178216376}"
            ]
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          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
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        "colab": {
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        "id": "oDKWmZVZEWI1",
        "outputId": "b1b258e3-9772-4326-909e-e5a826af53d1"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 72.1832384482987,\n",
              " 'precision': 0.7186466952323352,\n",
              " 'recall': 0.7218323844829869,\n",
              " 'f1': 0.6989250353450294}"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdbc9xCnEZFe",
        "outputId": "84a2409d-7215-4c34-efcd-cbba23c9cded"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 78.5979081159804,\n",
              " 'precision': 0.7826217394421506,\n",
              " 'recall': 0.7859790811598041,\n",
              " 'f1': 0.7832780087409795}"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Model-5 (Pretraine_token_embeding + char + token + Postional Level Embedding)"
      ],
      "metadata": {
        "id": "YfgC_wq4EYxc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
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          "height": 293
        },
        "id": "aS525Zd6EpKg",
        "outputId": "241d5e64-2bee-4fa6-f82e-fb917e1b1cd9"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      target                                               text  line_number  \\\n",
              "0  OBJECTIVE  to investigate the efficacy of @ weeks of dail...            0   \n",
              "1    METHODS  a total of @ patients with primary knee oa wer...            1   \n",
              "2    METHODS  outcome measures included pain reduction and i...            2   \n",
              "3    METHODS  pain was assessed using the visual analog pain...            3   \n",
              "4    METHODS  secondary outcome measures included the wester...            4   \n",
              "\n",
              "   total_lines  \n",
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            "text/plain": [
              "<bound method IndexOpsMixin.value_counts of 0          0\n",
              "1          1\n",
              "2          2\n",
              "3          3\n",
              "4          4\n",
              "          ..\n",
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              "180039    11\n",
              "Name: line_number, Length: 180040, dtype: int64>"
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
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              "      }\n",
              "\n",
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.base.IndexOpsMixin.value_counts</b><br/>def value_counts(normalize: bool=False, sort: bool=True, ascending: bool=False, bins=None, dropna: bool=True) -&gt; Series</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/base.py</a>Return a Series containing counts of unique values.\n",
              "\n",
              "The resulting object will be in descending order so that the\n",
              "first element is the most frequently-occurring element.\n",
              "Excludes NA values by default.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "normalize : bool, default False\n",
              "    If True then the object returned will contain the relative\n",
              "    frequencies of the unique values.\n",
              "sort : bool, default True\n",
              "    Sort by frequencies.\n",
              "ascending : bool, default False\n",
              "    Sort in ascending order.\n",
              "bins : int, optional\n",
              "    Rather than count values, group them into half-open bins,\n",
              "    a convenience for ``pd.cut``, only works with numeric data.\n",
              "dropna : bool, default True\n",
              "    Don&#x27;t include counts of NaN.\n",
              "\n",
              "Returns\n",
              "-------\n",
              "Series\n",
              "\n",
              "See Also\n",
              "--------\n",
              "Series.count: Number of non-NA elements in a Series.\n",
              "DataFrame.count: Number of non-NA elements in a DataFrame.\n",
              "DataFrame.value_counts: Equivalent method on DataFrames.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "&gt;&gt;&gt; index = pd.Index([3, 1, 2, 3, 4, np.nan])\n",
              "&gt;&gt;&gt; index.value_counts()\n",
              "3.0    2\n",
              "1.0    1\n",
              "2.0    1\n",
              "4.0    1\n",
              "Name: count, dtype: int64\n",
              "\n",
              "With `normalize` set to `True`, returns the relative frequency by\n",
              "dividing all values by the sum of values.\n",
              "\n",
              "&gt;&gt;&gt; s = pd.Series([3, 1, 2, 3, 4, np.nan])\n",
              "&gt;&gt;&gt; s.value_counts(normalize=True)\n",
              "3.0    0.4\n",
              "1.0    0.2\n",
              "2.0    0.2\n",
              "4.0    0.2\n",
              "Name: proportion, dtype: float64\n",
              "\n",
              "**bins**\n",
              "\n",
              "Bins can be useful for going from a continuous variable to a\n",
              "categorical variable; instead of counting unique\n",
              "apparitions of values, divide the index in the specified\n",
              "number of half-open bins.\n",
              "\n",
              "&gt;&gt;&gt; s.value_counts(bins=3)\n",
              "(0.996, 2.0]    2\n",
              "(2.0, 3.0]      2\n",
              "(3.0, 4.0]      1\n",
              "Name: count, dtype: int64\n",
              "\n",
              "**dropna**\n",
              "\n",
              "With `dropna` set to `False` we can also see NaN index values.\n",
              "\n",
              "&gt;&gt;&gt; s.value_counts(dropna=False)\n",
              "3.0    2\n",
              "1.0    1\n",
              "2.0    1\n",
              "4.0    1\n",
              "NaN    1\n",
              "Name: count, dtype: int64</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 928);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.line_number.plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "b3nJ1t_l5uI4",
        "outputId": "2b96ba3c-998a-4736-b1db-d3027cb5df00"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_line_numbers_one_hot = tf.one_hot(train_df[\"line_number\"].to_numpy(),depth=15)\n",
        "val_line_numbers_one_hot = tf.one_hot(val_df[\"line_number\"].to_numpy(),depth=15)\n",
        "test_line_numbers_one_hot = tf.one_hot(test_df[\"line_number\"].to_numpy(),depth=15)\n",
        "train_line_numbers_one_hot[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57xZLBGi6UZP",
        "outputId": "0279b822-4dd1-4e5f-86dc-2fa8a0fdd593"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 15), dtype=float32, numpy=\n",
              "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df[\"total_lines\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Yzv_QUc61Mc",
        "outputId": "4e9add66-c1f9-4f2a-e434-ecd3d59039ca"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "total_lines\n",
              "11    24468\n",
              "10    23639\n",
              "12    22113\n",
              "9     19400\n",
              "13    18438\n",
              "14    14610\n",
              "8     12285\n",
              "15    10768\n",
              "7      7464\n",
              "16     7429\n",
              "17     5202\n",
              "6      3353\n",
              "18     3344\n",
              "19     2480\n",
              "20     1281\n",
              "5      1146\n",
              "21      770\n",
              "22      759\n",
              "23      264\n",
              "4       215\n",
              "24      200\n",
              "25      182\n",
              "26       81\n",
              "28       58\n",
              "3        32\n",
              "30       31\n",
              "27       28\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.total_lines.plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "E03hqhTy68l4",
        "outputId": "3363e28f-41fa-4bb7-d28f-cd982be95a58"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.percentile(train_df.total_lines,98)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unlJ-M2g7FG7",
        "outputId": "9fa4ef9f-6ac1-4f28-b4d8-ba9d6c3f8943"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.0"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_total_lines_one_hot = tf.one_hot(train_df[\"total_lines\"].to_numpy(),depth=20)\n",
        "val_total_lines_one_hot = tf.one_hot(val_df[\"total_lines\"].to_numpy(),depth=20)\n",
        "test_total_lines_one_hot = tf.one_hot(test_df[\"total_lines\"].to_numpy(),depth=20)\n",
        "\n",
        "train_total_lines_one_hot[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZ8-oRDv7S7X",
        "outputId": "43914169-e2dd-4d44-e015-f51ae46476d6"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Token inputs\n",
        "token_inputs = layers.Input(shape=[], dtype=\"string\", name=\"token_inputs\")\n",
        "token_embeddings = tf_hub(token_inputs)\n",
        "token_outputs = layers.Dense(128, activation=\"relu\")(token_embeddings)\n",
        "token_model = tf.keras.Model(inputs=token_inputs,\n",
        "                             outputs=token_outputs)\n",
        "\n",
        "# 2. Char inputs\n",
        "char_inputs = layers.Input(shape=(1,), dtype=\"string\", name=\"char_inputs\")\n",
        "char_vectors = char_vectorizer(char_inputs)\n",
        "char_embeddings = char_embed(char_vectors)\n",
        "char_bi_lstm = layers.Bidirectional(layers.LSTM(32))(char_embeddings)\n",
        "char_model = tf.keras.Model(inputs=char_inputs,\n",
        "                            outputs=char_bi_lstm)\n",
        "\n",
        "# 3. Line numbers inputs\n",
        "line_number_inputs = layers.Input(shape=(15,), dtype=tf.int32, name=\"line_number_input\")\n",
        "x = layers.Dense(32, activation=\"relu\")(line_number_inputs)\n",
        "line_number_model = tf.keras.Model(inputs=line_number_inputs,\n",
        "                                   outputs=x)\n",
        "\n",
        "# 4. Total lines inputs\n",
        "total_lines_inputs = layers.Input(shape=(20,), dtype=tf.int32, name=\"total_lines_input\")\n",
        "y = layers.Dense(32, activation=\"relu\")(total_lines_inputs)\n",
        "total_line_model = tf.keras.Model(inputs=total_lines_inputs,\n",
        "                                  outputs=y)\n",
        "\n",
        "# 5. Combine token and char embeddings into a hybrid embedding\n",
        "combined_embeddings = layers.Concatenate(name=\"token_char_hybrid_embedding\")([token_model.output,\n",
        "                                                                              char_model.output])\n",
        "z = layers.Dense(256, activation=\"relu\")(combined_embeddings)\n",
        "z = layers.Dropout(0.5)(z)\n",
        "\n",
        "# 6. Combine positional embeddings with combined token and char embeddings into a tribrid embedding\n",
        "z = layers.Concatenate(name=\"token_char_positional_embedding\")([line_number_model.output,\n",
        "                                                                total_line_model.output,\n",
        "                                                                z])\n",
        "\n",
        "# 7. Create output layer\n",
        "output_layer = layers.Dense(5, activation=\"softmax\", name=\"output_layer\")(z)\n",
        "\n",
        "# 8. Put together model\n",
        "model_5 = tf.keras.Model(inputs=[line_number_model.input,\n",
        "                                 total_line_model.input,\n",
        "                                 token_model.input,\n",
        "                                 char_model.input],\n",
        "                         outputs=output_layer)"
      ],
      "metadata": {
        "id": "v865rNU379C_"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVEWPm7GBMMw",
        "outputId": "ee1ad8d5-1912-41d6-b741-50e3ba1c6e44"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_7\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " char_inputs (InputLayer)    [(None, 1)]                  0         []                            \n",
            "                                                                                                  \n",
            " token_inputs (InputLayer)   [(None,)]                    0         []                            \n",
            "                                                                                                  \n",
            " char_vectorizer (TextVecto  (None, 290)                  0         ['char_inputs[0][0]']         \n",
            " rization)                                                                                        \n",
            "                                                                                                  \n",
            " USE (KerasLayer)            (None, 512)                  2567978   ['token_inputs[0][0]']        \n",
            "                                                          24                                      \n",
            "                                                                                                  \n",
            " char_embed (Embedding)      (None, 290, 25)              1750      ['char_vectorizer[4][0]']     \n",
            "                                                                                                  \n",
            " dense_15 (Dense)            (None, 128)                  65664     ['USE[4][0]']                 \n",
            "                                                                                                  \n",
            " bidirectional_3 (Bidirecti  (None, 64)                   14848     ['char_embed[4][0]']          \n",
            " onal)                                                                                            \n",
            "                                                                                                  \n",
            " token_char_hybrid_embeddin  (None, 192)                  0         ['dense_15[0][0]',            \n",
            " g (Concatenate)                                                     'bidirectional_3[0][0]']     \n",
            "                                                                                                  \n",
            " line_number_input (InputLa  [(None, 15)]                 0         []                            \n",
            " yer)                                                                                             \n",
            "                                                                                                  \n",
            " total_lines_input (InputLa  [(None, 20)]                 0         []                            \n",
            " yer)                                                                                             \n",
            "                                                                                                  \n",
            " dense_18 (Dense)            (None, 256)                  49408     ['token_char_hybrid_embedding[\n",
            "                                                                    0][0]']                       \n",
            "                                                                                                  \n",
            " dense_16 (Dense)            (None, 32)                   512       ['line_number_input[0][0]']   \n",
            "                                                                                                  \n",
            " dense_17 (Dense)            (None, 32)                   672       ['total_lines_input[0][0]']   \n",
            "                                                                                                  \n",
            " dropout_4 (Dropout)         (None, 256)                  0         ['dense_18[0][0]']            \n",
            "                                                                                                  \n",
            " token_char_positional_embe  (None, 320)                  0         ['dense_16[0][0]',            \n",
            " dding (Concatenate)                                                 'dense_17[0][0]',            \n",
            "                                                                     'dropout_4[0][0]']           \n",
            "                                                                                                  \n",
            " output_layer (Dense)        (None, 5)                    1605      ['token_char_positional_embedd\n",
            "                                                                    ing[0][0]']                   \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 256932283 (980.12 MB)\n",
            "Trainable params: 134459 (525.23 KB)\n",
            "Non-trainable params: 256797824 (979.61 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model_5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "hD52gKCuBQRn",
        "outputId": "06a69308-1874-43a0-b9b6-774eae3cee76"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in model_5.layers:\n",
        "  print(layer, layer.trainable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pfo_WLV6BeuI",
        "outputId": "d4774efc-3fb6-4974-bb07-8f878bd085aa"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<keras.src.engine.input_layer.InputLayer object at 0x7aa45026b280> True\n",
            "<keras.src.engine.input_layer.InputLayer object at 0x7aa4e48f8490> True\n",
            "<keras.src.layers.preprocessing.text_vectorization.TextVectorization object at 0x7aa45b4382e0> True\n",
            "<tensorflow_hub.keras_layer.KerasLayer object at 0x7aa4e0e6c280> False\n",
            "<keras.src.layers.core.embedding.Embedding object at 0x7aa4e09ab9a0> True\n",
            "<keras.src.layers.core.dense.Dense object at 0x7aa4e42e70a0> True\n",
            "<keras.src.layers.rnn.bidirectional.Bidirectional object at 0x7aa4502dcbe0> True\n",
            "<keras.src.layers.merging.concatenate.Concatenate object at 0x7aa449e40d30> True\n",
            "<keras.src.engine.input_layer.InputLayer object at 0x7aa4512c8f40> True\n",
            "<keras.src.engine.input_layer.InputLayer object at 0x7aa4502df100> True\n",
            "<keras.src.layers.core.dense.Dense object at 0x7aa449caba60> True\n",
            "<keras.src.layers.core.dense.Dense object at 0x7aa45013d5d0> True\n",
            "<keras.src.layers.core.dense.Dense object at 0x7aa449c14b50> True\n",
            "<keras.src.layers.regularization.dropout.Dropout object at 0x7aa449e94100> True\n",
            "<keras.src.layers.merging.concatenate.Concatenate object at 0x7aa4502dfe80> True\n",
            "<keras.src.layers.core.dense.Dense object at 0x7aa449cab130> True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.compile(loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.2), # add label smoothing (examples which are really confident get smoothed a little)\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "qgWuttA1Bsx-"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_pos_char_token_data = tf.data.Dataset.from_tensor_slices((train_line_numbers_one_hot, # line numbers\n",
        "                                                                train_total_lines_one_hot, # total lines\n",
        "                                                                train_sentences, # train tokens\n",
        "                                                                train_chars))\n",
        "train_pos_char_token_labels = tf.data.Dataset.from_tensor_slices((train_labels_one_hot))\n",
        "train_pos_char_token_dataset = tf.data.Dataset.zip((train_pos_char_token_data,train_pos_char_token_labels))\n",
        "train_pos_char_token_dataset = train_pos_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "val_pos_char_token_data = tf.data.Dataset.from_tensor_slices((val_line_numbers_one_hot,\n",
        "                                                              val_total_lines_one_hot,\n",
        "                                                              val_sentences,\n",
        "                                                              val_chars))\n",
        "val_pos_char_token_labels = tf.data.Dataset.from_tensor_slices(val_labels_one_hot)\n",
        "val_pos_char_token_dataset = tf.data.Dataset.zip((val_pos_char_token_data, val_pos_char_token_labels))\n",
        "val_pos_char_token_dataset = val_pos_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "# Check input shapes\n",
        "train_pos_char_token_dataset, val_pos_char_token_dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GcdynHkwB0HF",
        "outputId": "c8973ef5-be70-45d5-812f-19227afd37fb"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<_PrefetchDataset element_spec=((TensorSpec(shape=(None, 15), dtype=tf.float32, name=None), TensorSpec(shape=(None, 20), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>,\n",
              " <_PrefetchDataset element_spec=((TensorSpec(shape=(None, 15), dtype=tf.float32, name=None), TensorSpec(shape=(None, 20), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>)"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5_history = model_5.fit(train_pos_char_token_dataset,steps_per_epoch=int(0.1*len(train_pos_char_token_dataset)),\n",
        "                              epochs=3,\n",
        "                              validation_data=val_pos_char_token_dataset,\n",
        "                              validation_steps=int(0.1*len(val_pos_char_token_dataset))\n",
        "                              )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSoxdoZ5Df8c",
        "outputId": "1869ced0-62a9-4337-cb86-b83e84a7d1eb"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 26s 47ms/step - loss: 0.9472 - accuracy: 0.8283 - val_loss: 0.9372 - val_accuracy: 0.8324\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 26s 46ms/step - loss: 0.9265 - accuracy: 0.8449 - val_loss: 0.9267 - val_accuracy: 0.8411\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 23s 41ms/step - loss: 0.9309 - accuracy: 0.8372 - val_loss: 0.9266 - val_accuracy: 0.8388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.evaluate(val_pos_char_token_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vQpLWtREHtG",
        "outputId": "ad172416-ea5a-4893-9c09-1e241e4d5cfe"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 18s 19ms/step - loss: 0.9246 - accuracy: 0.8412\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.924556314945221, 0.841188907623291]"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5_pred_probs = model_5.predict(val_pos_char_token_dataset)\n",
        "model_5_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXhcrT9OEoE9",
        "outputId": "7cb609a2-e1d6-4101-9b14-bd30751c0da2"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 21s 19ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.51010627, 0.09126199, 0.00934137, 0.37150213, 0.01778828],\n",
              "       [0.5174157 , 0.10907301, 0.05063577, 0.3099314 , 0.01294413],\n",
              "       [0.2575314 , 0.10807069, 0.12089662, 0.4526061 , 0.06089513],\n",
              "       ...,\n",
              "       [0.03501378, 0.08337948, 0.03006984, 0.02808645, 0.82345045],\n",
              "       [0.03325105, 0.36760908, 0.08398581, 0.0283522 , 0.48680195],\n",
              "       [0.1398805 , 0.7322857 , 0.06116954, 0.03160829, 0.03505593]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5_pred = tf.argmax(model_5_pred_probs,axis=1)\n",
        "model_5_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1iJCbPXExKx",
        "outputId": "e13a3bc3-9bd1-4a69-a0f6-3d4b562c8cbd"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 0, 3, ..., 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5_results = calculate_results(val_labels_encoded,model_5_pred)\n",
        "model_5_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wcNsf7lFE4hq",
        "outputId": "fefeae95-5703-43de-904e-349da476fa81"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 84.11889315503774,\n",
              " 'precision': 0.8404113789101524,\n",
              " 'recall': 0.8411889315503773,\n",
              " 'f1': 0.8403198504996063}"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Comparing all Models"
      ],
      "metadata": {
        "id": "S1XvUqpfFNQW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "all_model_results = pd.DataFrame({\"baseline\": baseline_results,\n",
        "                                  \"custom_token_embed_conv1d\": model_1_results,\n",
        "                                  \"pretrained_token_embed\": model_2_results,\n",
        "                                  \"custom_char_embed_conv1d\": model_3_results,\n",
        "                                  \"hybrid_char_token_embed\": model_4_results,\n",
        "                                  \"tribrid_pos_char_token_embed\": model_5_results})\n",
        "all_model_results = all_model_results.transpose()\n",
        "all_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "At01uXXMFUwV",
        "outputId": "0b2573b9-d9cb-49ab-8b74-7baad187c5e0"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               accuracy  precision    recall        f1\n",
              "baseline                      72.183238   0.718647  0.721832  0.698925\n",
              "custom_token_embed_conv1d     78.597908   0.782622  0.785979  0.783278\n",
              "pretrained_token_embed        71.329273   0.713873  0.713293  0.710262\n",
              "custom_char_embed_conv1d      64.477691   0.639406  0.644777  0.630434\n",
              "hybrid_char_token_embed       73.537005   0.736569  0.735370  0.732151\n",
              "tribrid_pos_char_token_embed  84.118893   0.840411  0.841189  0.840320"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>baseline</th>\n",
              "      <td>72.183238</td>\n",
              "      <td>0.718647</td>\n",
              "      <td>0.721832</td>\n",
              "      <td>0.698925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>custom_token_embed_conv1d</th>\n",
              "      <td>78.597908</td>\n",
              "      <td>0.782622</td>\n",
              "      <td>0.785979</td>\n",
              "      <td>0.783278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0.713293</td>\n",
              "      <td>0.710262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>custom_char_embed_conv1d</th>\n",
              "      <td>64.477691</td>\n",
              "      <td>0.639406</td>\n",
              "      <td>0.644777</td>\n",
              "      <td>0.630434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hybrid_char_token_embed</th>\n",
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              "      <td>0.732151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tribrid_pos_char_token_embed</th>\n",
              "      <td>84.118893</td>\n",
              "      <td>0.840411</td>\n",
              "      <td>0.841189</td>\n",
              "      <td>0.840320</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"all_model_results\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.705892514237405,\n        \"min\": 64.47769098371508,\n        \"max\": 84.11889315503774,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          72.1832384482987,\n          78.5979081159804,\n          84.11889315503774\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06805393168042345,\n        \"min\": 0.6394055333390221,\n        \"max\": 0.8404113789101524,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7186466952323352,\n          0.7826217394421506,\n          0.8404113789101524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06705892514237406,\n        \"min\": 0.6447769098371507,\n        \"max\": 0.8411889315503773,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7218323844829869,\n          0.7859790811598041,\n          0.8411889315503773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07244715577892721,\n        \"min\": 0.6304336897422952,\n        \"max\": 0.8403198504996063,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6989250353450294,\n          0.7832780087409795,\n          0.8403198504996063\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results[\"accuracy\"] = all_model_results[\"accuracy\"]/100"
      ],
      "metadata": {
        "id": "N0p3p_3OGQyf"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "xcGX5ZI0GZbA",
        "outputId": "5525dba4-d3be-4851-e66b-3709a8788db6"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              accuracy  precision    recall        f1\n",
              "baseline                      0.721832   0.718647  0.721832  0.698925\n",
              "custom_token_embed_conv1d     0.785979   0.782622  0.785979  0.783278\n",
              "pretrained_token_embed        0.713293   0.713873  0.713293  0.710262\n",
              "custom_char_embed_conv1d      0.644777   0.639406  0.644777  0.630434\n",
              "hybrid_char_token_embed       0.735370   0.736569  0.735370  0.732151\n",
              "tribrid_pos_char_token_embed  0.841189   0.840411  0.841189  0.840320"
            ],
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              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
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              "      <th>f1</th>\n",
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              "      <th>baseline</th>\n",
              "      <td>0.721832</td>\n",
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              "      <td>0.785979</td>\n",
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              "      <td>0.783278</td>\n",
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              "      <td>0.644777</td>\n",
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              "      <td>0.841189</td>\n",
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              "type": "dataframe",
              "variable_name": "all_model_results",
              "summary": "{\n  \"name\": \"all_model_results\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06705892514237408,\n        \"min\": 0.6447769098371507,\n        \"max\": 0.8411889315503774,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7218323844829869,\n          0.785979081159804,\n          0.8411889315503774\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06805393168042345,\n        \"min\": 0.6394055333390221,\n        \"max\": 0.8404113789101524,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7186466952323352,\n          0.7826217394421506,\n          0.8404113789101524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06705892514237406,\n        \"min\": 0.6447769098371507,\n        \"max\": 0.8411889315503773,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7218323844829869,\n          0.7859790811598041,\n          0.8411889315503773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07244715577892721,\n        \"min\": 0.6304336897422952,\n        \"max\": 0.8403198504996063,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6989250353450294,\n          0.7832780087409795,\n          0.8403198504996063\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results.plot(kind=\"bar\",figsize=(10,7)).legend(bbox_to_anchor=(1.0,1.0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "UjP7y0wnGcnx",
        "outputId": "323961a8-202f-4094-b0f1-3fec73ec73f6"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7aa448141e40>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results.sort_values(\"f1\", ascending=False)[\"f1\"].plot(kind=\"bar\", figsize=(10, 7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "BPbuLgCUGtLB",
        "outputId": "21dbeaa2-933c-477f-e2e8-2e6e7df6fc45"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Save and Load Model"
      ],
      "metadata": {
        "id": "NC1Nb1nwG_SA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.save(\"skimlit_tribrid_model\")"
      ],
      "metadata": {
        "id": "V1J_CYN6HIis"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_hub as hub\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import TextVectorization\n",
        "\n",
        "model_path = \"skimlit_tribrid_model\"\n",
        "\n",
        "# Load downloaded model from Google Storage\n",
        "loaded_model = tf.keras.models.load_model(model_path)"
      ],
      "metadata": {
        "id": "lH6iQ_xCH-4l"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_pred_probs = loaded_model.predict(val_pos_char_token_dataset, verbose=1)\n",
        "loaded_preds = tf.argmax(loaded_pred_probs, axis=1)\n",
        "loaded_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4p8jz8tI-wA",
        "outputId": "74582f95-df59-4b4b-c437-b4d538bd1b0f"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 21s 19ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int64, numpy=array([0, 0, 3, 2, 2, 4, 4, 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model_results = calculate_results(val_labels_encoded,\n",
        "                                         loaded_preds)\n",
        "loaded_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CEP9TBLJDLq",
        "outputId": "0e93c110-76df-464d-e7c1-d4cb1ccf4039"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 84.11889315503774,\n",
              " 'precision': 0.8404113789101524,\n",
              " 'recall': 0.8411889315503773,\n",
              " 'f1': 0.8403198504996063}"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0fti2qcJyRr",
        "outputId": "1bac2acb-4685-43df-b873-1d83eb823033"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 84.11889315503774,\n",
              " 'precision': 0.8404113789101524,\n",
              " 'recall': 0.8411889315503773,\n",
              " 'f1': 0.8403198504996063}"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.isclose(list(model_5_results.values()), list(loaded_model_results.values()), rtol=1e-02)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYJbFHfDJ4lw",
        "outputId": "abf89e14-b79c-4315-ca85-4f693ce18733"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(loaded_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "Ekm96INZJ9ri",
        "outputId": "2a26cfb0-18d5-4300-a556-bdd29dad59b8"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_pos_char_token_data = tf.data.Dataset.from_tensor_slices((test_line_numbers_one_hot,\n",
        "                                                               test_total_lines_one_hot,\n",
        "                                                               test_sentences,\n",
        "                                                               test_chars))\n",
        "test_pos_char_token_labels = tf.data.Dataset.from_tensor_slices(test_labels_one_hot)\n",
        "test_pos_char_token_dataset = tf.data.Dataset.zip((test_pos_char_token_data, test_pos_char_token_labels))\n",
        "test_pos_char_token_dataset = test_pos_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "# Check shapes\n",
        "test_pos_char_token_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wAnjGuXKC-u",
        "outputId": "924ca4cf-d1b4-4a0c-f853-38dca4bab4f2"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_PrefetchDataset element_spec=((TensorSpec(shape=(None, 15), dtype=tf.float32, name=None), TensorSpec(shape=(None, 20), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 5), dtype=tf.float64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_pred_probs = loaded_model.predict(test_pos_char_token_dataset,\n",
        "                                       verbose=1)\n",
        "test_preds = tf.argmax(test_pred_probs, axis=1)\n",
        "test_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pNljo-tKZZ0",
        "outputId": "32c683f8-1321-4554-dce7-4ffa6fa18e9a"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "942/942 [==============================] - 19s 21ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int64, numpy=array([3, 0, 2, 2, 4, 4, 4, 1, 1, 0])>"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model_test_results = calculate_results(y_true=test_labels_encoded,\n",
        "                                              y_pred=test_preds)\n",
        "loaded_model_test_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nH-oeQTWKjEL",
        "outputId": "8ec5abb7-7556-41c5-ede8-9c3a30bff0d7"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 83.61041977766716,\n",
              " 'precision': 0.8347771542818602,\n",
              " 'recall': 0.8361041977766717,\n",
              " 'f1': 0.8350889398069855}"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# Get list of class names of test predictions\n",
        "test_pred_classes = [label_encoder.classes_[pred] for pred in test_preds]\n",
        "test_pred_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ge1j7y_cK8Tr",
        "outputId": "a852e716-b7d3-40e5-d8e0-ec0cf8770334"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 14.6 s, sys: 720 ms, total: 15.4 s\n",
            "Wall time: 14.7 s\n"
          ]
        },
        {
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    {
      "cell_type": "code",
      "source": [
        "test_df[\"prediction\"] = test_pred_classes # create column with test prediction class names\n",
        "test_df[\"pred_prob\"] = tf.reduce_max(test_pred_probs, axis=1).numpy() # get the maximum prediction probability\n",
        "test_df[\"correct\"] = test_df[\"prediction\"] == test_df[\"target\"] # create binary column for whether the prediction is right or not\n",
        "test_df.head(20)"
      ],
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          "height": 1000
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        "id": "U3DYJL1MLfNs",
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      "execution_count": 141,
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              "         target                                               text  \\\n",
              "0    BACKGROUND  this study analyzed liver function abnormaliti...   \n",
              "1       RESULTS  a post hoc analysis was conducted with the use...   \n",
              "2       RESULTS  liver function tests ( lfts ) were measured at...   \n",
              "3       RESULTS  survival analyses were used to assess the asso...   \n",
              "4       RESULTS  the percentage of patients with abnormal lfts ...   \n",
              "5       RESULTS  when mean hemodynamic profiles were compared i...   \n",
              "6       RESULTS  multivariable analyses revealed that patients ...   \n",
              "7   CONCLUSIONS  abnormal lfts are common in the adhf populatio...   \n",
              "8   CONCLUSIONS  elevated meld-xi scores are associated with po...   \n",
              "9    BACKGROUND  minimally invasive endovascular aneurysm repai...   \n",
              "10   BACKGROUND  the aim of this study was to analyse the cost-...   \n",
              "11      METHODS  resource use was determined from the amsterdam...   \n",
              "12      METHODS  the analysis was performed from a provider per...   \n",
              "13      METHODS  all costs were calculated as if all patients h...   \n",
              "14      RESULTS            a total of @ patients were randomized .   \n",
              "15      RESULTS  the @-day mortality rate was @ per cent after ...   \n",
              "16      RESULTS  at @months , the total mortality rate for evar...   \n",
              "17      RESULTS  the mean cost difference between evar and or w...   \n",
              "18      RESULTS  the incremental cost-effectiveness ratio per p...   \n",
              "19      RESULTS  there was no significant difference in quality...   \n",
              "\n",
              "    line_number  total_lines   prediction  pred_prob  correct  \n",
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              "2             2            8      METHODS   0.757324    False  \n",
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              "4             4            8      RESULTS   0.744003     True  \n",
              "5             5            8      RESULTS   0.872728     True  \n",
              "6             6            8      RESULTS   0.494830     True  \n",
              "7             7            8  CONCLUSIONS   0.578323     True  \n",
              "8             8            8  CONCLUSIONS   0.475721     True  \n",
              "9             0           12   BACKGROUND   0.580334     True  \n",
              "10            1           12    OBJECTIVE   0.473014    False  \n",
              "11            2           12      METHODS   0.611619     True  \n",
              "12            3           12      METHODS   0.837449     True  \n",
              "13            4           12      METHODS   0.577983     True  \n",
              "14            5           12      RESULTS   0.736939     True  \n",
              "15            6           12      RESULTS   0.707284     True  \n",
              "16            7           12      RESULTS   0.882538     True  \n",
              "17            8           12      RESULTS   0.880708     True  \n",
              "18            9           12      RESULTS   0.783282     True  \n",
              "19           10           12      RESULTS   0.758453     True  "
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45e6fb00-21dc-41f0-963b-09e7bdf5a73f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df",
              "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 30135,\n  \"fields\": [\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"RESULTS\",\n          \"OBJECTIVE\",\n          \"CONCLUSIONS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29735,\n        \"samples\": [\n          \"in this secondary study of an open-label , @-week , randomized , controlled trial conducted between april @ and august @ , clinically stable patients with schizophrenia ( dsm-iv ) treated with risperidone or olanzapine were randomly assigned to the reduction group ( dose reduced by @ % ) or maintenance group ( dose kept constant ) .\",\n          \"between july , @ , and june , @ , we did a multinational double-blind , randomised , phase @ study comparing vacc-@x with placebo .\",\n          \"a sample of @ participants was interviewed over the telephone for possible cognitive and background moderators .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"line_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 27,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          9,\n          25,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_lines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 3,\n        \"max\": 27,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          24,\n          16,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"BACKGROUND\",\n          \"CONCLUSIONS\",\n          \"METHODS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_prob\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 29916,\n        \"samples\": [\n          0.6023309826850891,\n          0.7383320331573486,\n          0.5403207540512085\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"correct\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_100_wrong = test_df[test_df[\"correct\"] == False].sort_values(\"pred_prob\", ascending=False)[:100]\n",
        "top_100_wrong"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "id": "eEXJZWIxLiIn",
        "outputId": "4213c418-94a8-4900-d97b-cb9bad0c08a2"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            target                                               text  \\\n",
              "16347   BACKGROUND  to evaluate the effects of the lactic acid bac...   \n",
              "8545       METHODS                                 pretest-posttest .   \n",
              "2388       RESULTS  the primary endpoint is the cumulative three-y...   \n",
              "1827   CONCLUSIONS                      nct@ ( clinicaltrials.gov ) .   \n",
              "4391   CONCLUSIONS  -lsb- netherlands trial register ( http://www....   \n",
              "...            ...                                                ...   \n",
              "3692       RESULTS  the primary end point was in-stent late loss a...   \n",
              "8358   CONCLUSIONS                                             nct@ .   \n",
              "12459      RESULTS              adherence to follow-up was measured .   \n",
              "29007      RESULTS  control group patients received a wallet card ...   \n",
              "29006      RESULTS  intervention group patients received an assess...   \n",
              "\n",
              "       line_number  total_lines  prediction  pred_prob  correct  \n",
              "16347            0           12   OBJECTIVE   0.959068    False  \n",
              "8545             1           11  BACKGROUND   0.936227    False  \n",
              "2388             4           13     METHODS   0.935947    False  \n",
              "1827            18           18  BACKGROUND   0.933830    False  \n",
              "4391            15           15  BACKGROUND   0.932960    False  \n",
              "...            ...          ...         ...        ...      ...  \n",
              "3692             3           11     METHODS   0.838135    False  \n",
              "8358            11           11  BACKGROUND   0.836722    False  \n",
              "12459            4           13     METHODS   0.836630    False  \n",
              "29007            5           14     METHODS   0.835781    False  \n",
              "29006            4           14     METHODS   0.835738    False  \n",
              "\n",
              "[100 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec55fe48-6790-4400-abd5-40df6270ad46\" 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>target</th>\n",
              "      <th>text</th>\n",
              "      <th>line_number</th>\n",
              "      <th>total_lines</th>\n",
              "      <th>prediction</th>\n",
              "      <th>pred_prob</th>\n",
              "      <th>correct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16347</th>\n",
              "      <td>BACKGROUND</td>\n",
              "      <td>to evaluate the effects of the lactic acid bac...</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>0.959068</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8545</th>\n",
              "      <td>METHODS</td>\n",
              "      <td>pretest-posttest .</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>BACKGROUND</td>\n",
              "      <td>0.936227</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2388</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>the primary endpoint is the cumulative three-y...</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>METHODS</td>\n",
              "      <td>0.935947</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1827</th>\n",
              "      <td>CONCLUSIONS</td>\n",
              "      <td>nct@ ( clinicaltrials.gov ) .</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>BACKGROUND</td>\n",
              "      <td>0.933830</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4391</th>\n",
              "      <td>CONCLUSIONS</td>\n",
              "      <td>-lsb- netherlands trial register ( http://www....</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>BACKGROUND</td>\n",
              "      <td>0.932960</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3692</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>the primary end point was in-stent late loss a...</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>METHODS</td>\n",
              "      <td>0.838135</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8358</th>\n",
              "      <td>CONCLUSIONS</td>\n",
              "      <td>nct@ .</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>BACKGROUND</td>\n",
              "      <td>0.836722</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12459</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>adherence to follow-up was measured .</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>METHODS</td>\n",
              "      <td>0.836630</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29007</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>control group patients received a wallet card ...</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>METHODS</td>\n",
              "      <td>0.835781</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29006</th>\n",
              "      <td>RESULTS</td>\n",
              "      <td>intervention group patients received an assess...</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>METHODS</td>\n",
              "      <td>0.835738</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec55fe48-6790-4400-abd5-40df6270ad46')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ec55fe48-6790-4400-abd5-40df6270ad46 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-ec55fe48-6790-4400-abd5-40df6270ad46');\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",
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              "\n",
              "\n",
              "<div id=\"df-22a154f0-af70-48af-b7c3-f68b29149697\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-22a154f0-af70-48af-b7c3-f68b29149697')\"\n",
              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_100_wrong",
              "summary": "{\n  \"name\": \"top_100_wrong\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"METHODS\",\n          \"OBJECTIVE\",\n          \"RESULTS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 97,\n        \"samples\": [\n          \"to test the hypothesis that this electronic acute kidney injury alert could improve patient outcome , we designed a randomized controlled trial to test the effectiveness of this alert in hospitalized patients .\",\n          \"of the @ dogs receiving placebo , @ ( @ % ) vomited and @ ( @ % ) developed signs of nausea ; overall , @ of @ ( @ % ) dogs in the placebo treatment group vomited or developed signs of nausea .\",\n          \"the primary end point was in-stent late loss at @ months .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"line_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0,\n          1,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_lines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 6,\n        \"max\": 27,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          12,\n          11,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"BACKGROUND\",\n          \"RESULTS\",\n          \"METHODS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_prob\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.8463584184646606,\n          0.8646395802497864,\n          0.854164183139801\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"correct\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for row in top_100_wrong[0:10].itertuples(): # adjust indexes to view different samples\n",
        "  _, target, text, line_number, total_lines, prediction, pred_prob, _ = row\n",
        "  print(f\"Target: {target}, Pred: {prediction}, Prob: {pred_prob}, Line number: {line_number}, Total lines: {total_lines}\\n\")\n",
        "  print(f\"Text:\\n{text}\\n\")\n",
        "  print(\"-----\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nMLG-I8aLnKP",
        "outputId": "75007303-d064-4148-90c9-b5554f183e58"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target: BACKGROUND, Pred: OBJECTIVE, Prob: 0.9590675234794617, Line number: 0, Total lines: 12\n",
            "\n",
            "Text:\n",
            "to evaluate the effects of the lactic acid bacterium lactobacillus salivarius on caries risk factors .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: METHODS, Pred: BACKGROUND, Prob: 0.9362266659736633, Line number: 1, Total lines: 11\n",
            "\n",
            "Text:\n",
            "pretest-posttest .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: RESULTS, Pred: METHODS, Prob: 0.935947060585022, Line number: 4, Total lines: 13\n",
            "\n",
            "Text:\n",
            "the primary endpoint is the cumulative three-year hiv incidence .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9338304996490479, Line number: 18, Total lines: 18\n",
            "\n",
            "Text:\n",
            "nct@ ( clinicaltrials.gov ) .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9329603910446167, Line number: 15, Total lines: 15\n",
            "\n",
            "Text:\n",
            "-lsb- netherlands trial register ( http://www.trialregister.nl/trialreg/index.asp ) , nr @ , date of registration @ december @ . -rsb-\n",
            "\n",
            "-----\n",
            "\n",
            "Target: CONCLUSIONS, Pred: METHODS, Prob: 0.9250435829162598, Line number: 4, Total lines: 6\n",
            "\n",
            "Text:\n",
            "symptom outcomes will be assessed and estimates of cost-effectiveness made .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: BACKGROUND, Pred: OBJECTIVE, Prob: 0.9234973788261414, Line number: 0, Total lines: 11\n",
            "\n",
            "Text:\n",
            "to assess the temporal patterns of late gastrointestinal ( gi ) and genitourinary ( gu ) radiotherapy toxicity and resolution rates in a randomised controlled trial ( all-ireland cooperative oncology research group @-@ ) assessing duration of neo-adjuvant ( na ) hormone therapy for localised prostate cancer .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9209522604942322, Line number: 13, Total lines: 13\n",
            "\n",
            "Text:\n",
            "( clinicaltrials.gov : nct@ ) .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: CONCLUSIONS, Pred: BACKGROUND, Prob: 0.9181675314903259, Line number: 19, Total lines: 19\n",
            "\n",
            "Text:\n",
            "clinicaltrials.gov identifier : nct@ .\n",
            "\n",
            "-----\n",
            "\n",
            "Target: BACKGROUND, Pred: OBJECTIVE, Prob: 0.9146292209625244, Line number: 0, Total lines: 10\n",
            "\n",
            "Text:\n",
            "to validate the association between accommodation and visual asthenopia by measuring objective accommodative amplitude with the optical quality analysis system ( oqas , visiometrics , terrassa , spain ) , and to investigate associations among accommodation , ocular surface instability , and visual asthenopia while viewing @d displays .\n",
            "\n",
            "-----\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "# Download and open example abstracts\n",
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/skimlit_example_abstracts.json\n",
        "\n",
        "with open(\"skimlit_example_abstracts.json\", \"r\") as f:\n",
        "  example_abstracts = json.load(f)\n",
        "\n",
        "example_abstracts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpdKKqGlLses",
        "outputId": "3bbd9193-6ec3-481f-d3ec-faa787f61705"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-06-21 09:44:48--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/skimlit_example_abstracts.json\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6737 (6.6K) [text/plain]\n",
            "Saving to: ‘skimlit_example_abstracts.json’\n",
            "\n",
            "\r          skimlit_e   0%[                    ]       0  --.-KB/s               \rskimlit_example_abs 100%[===================>]   6.58K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-06-21 09:44:48 (95.0 MB/s) - ‘skimlit_example_abstracts.json’ saved [6737/6737]\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'abstract': 'This RCT examined the efficacy of a manualized social intervention for children with HFASDs. Participants were randomly assigned to treatment or wait-list conditions. Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language. A response-cost program was applied to reduce problem behaviors and foster skills acquisition. Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures). Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents. High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity. Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.',\n",
              "  'source': 'https://pubmed.ncbi.nlm.nih.gov/20232240/',\n",
              "  'details': 'RCT of a manualized social treatment for high-functioning autism spectrum disorders'},\n",
              " {'abstract': \"Postpartum depression (PPD) is the most prevalent mood disorder associated with childbirth. No single cause of PPD has been identified, however the increased risk of nutritional deficiencies incurred through the high nutritional requirements of pregnancy may play a role in the pathology of depressive symptoms. Three nutritional interventions have drawn particular interest as possible non-invasive and cost-effective prevention and/or treatment strategies for PPD; omega-3 (n-3) long chain polyunsaturated fatty acids (LCPUFA), vitamin D and overall diet. We searched for meta-analyses of randomised controlled trials (RCT's) of nutritional interventions during the perinatal period with PPD as an outcome, and checked for any trials published subsequently to the meta-analyses. Fish oil: Eleven RCT's of prenatal fish oil supplementation RCT's show null and positive effects on PPD symptoms. Vitamin D: no relevant RCT's were identified, however seven observational studies of maternal vitamin D levels with PPD outcomes showed inconsistent associations. Diet: Two Australian RCT's with dietary advice interventions in pregnancy had a positive and null result on PPD. With the exception of fish oil, few RCT's with nutritional interventions during pregnancy assess PPD. Further research is needed to determine whether nutritional intervention strategies during pregnancy can protect against symptoms of PPD. Given the prevalence of PPD and ease of administering PPD measures, we recommend future prenatal nutritional RCT's include PPD as an outcome.\",\n",
              "  'source': 'https://pubmed.ncbi.nlm.nih.gov/28012571/',\n",
              "  'details': 'Formatting removed (can be used to compare model to actual example)'},\n",
              " {'abstract': 'Mental illness, including depression, anxiety and bipolar disorder, accounts for a significant proportion of global disability and poses a substantial social, economic and heath burden. Treatment is presently dominated by pharmacotherapy, such as antidepressants, and psychotherapy, such as cognitive behavioural therapy; however, such treatments avert less than half of the disease burden, suggesting that additional strategies are needed to prevent and treat mental disorders. There are now consistent mechanistic, observational and interventional data to suggest diet quality may be a modifiable risk factor for mental illness. This review provides an overview of the nutritional psychiatry field. It includes a discussion of the neurobiological mechanisms likely modulated by diet, the use of dietary and nutraceutical interventions in mental disorders, and recommendations for further research. Potential biological pathways related to mental disorders include inflammation, oxidative stress, the gut microbiome, epigenetic modifications and neuroplasticity. Consistent epidemiological evidence, particularly for depression, suggests an association between measures of diet quality and mental health, across multiple populations and age groups; these do not appear to be explained by other demographic, lifestyle factors or reverse causality. Our recently published intervention trial provides preliminary clinical evidence that dietary interventions in clinically diagnosed populations are feasible and can provide significant clinical benefit. Furthermore, nutraceuticals including n-3 fatty acids, folate, S-adenosylmethionine, N-acetyl cysteine and probiotics, among others, are promising avenues for future research. Continued research is now required to investigate the efficacy of intervention studies in large cohorts and within clinically relevant populations, particularly in patients with schizophrenia, bipolar and anxiety disorders.',\n",
              "  'source': 'https://pubmed.ncbi.nlm.nih.gov/28942748/',\n",
              "  'details': 'Effect of nutrition on mental health'},\n",
              " {'abstract': \"Hepatitis C virus (HCV) and alcoholic liver disease (ALD), either alone or in combination, count for more than two thirds of all liver diseases in the Western world. There is no safe level of drinking in HCV-infected patients and the most effective goal for these patients is total abstinence. Baclofen, a GABA(B) receptor agonist, represents a promising pharmacotherapy for alcohol dependence (AD). Previously, we performed a randomized clinical trial (RCT), which demonstrated the safety and efficacy of baclofen in patients affected by AD and cirrhosis. The goal of this post-hoc analysis was to explore baclofen's effect in a subgroup of alcohol-dependent HCV-infected cirrhotic patients. Any patient with HCV infection was selected for this analysis. Among the 84 subjects randomized in the main trial, 24 alcohol-dependent cirrhotic patients had a HCV infection; 12 received baclofen 10mg t.i.d. and 12 received placebo for 12-weeks. With respect to the placebo group (3/12, 25.0%), a significantly higher number of patients who achieved and maintained total alcohol abstinence was found in the baclofen group (10/12, 83.3%; p=0.0123). Furthermore, in the baclofen group, compared to placebo, there was a significantly higher increase in albumin values from baseline (p=0.0132) and a trend toward a significant reduction in INR levels from baseline (p=0.0716). In conclusion, baclofen was safe and significantly more effective than placebo in promoting alcohol abstinence, and improving some Liver Function Tests (LFTs) (i.e. albumin, INR) in alcohol-dependent HCV-infected cirrhotic patients. Baclofen may represent a clinically relevant alcohol pharmacotherapy for these patients.\",\n",
              "  'source': 'https://pubmed.ncbi.nlm.nih.gov/22244707/',\n",
              "  'details': 'Baclofen promotes alcohol abstinence in alcohol dependent cirrhotic patients with hepatitis C virus (HCV) infection'}]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abstracts = pd.DataFrame(example_abstracts)\n",
        "abstracts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "gIQy2SM_Lztv",
        "outputId": "b38112c4-e7fa-4822-da4d-f465e8ed6dee"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                            abstract  \\\n",
              "0  This RCT examined the efficacy of a manualized...   \n",
              "1  Postpartum depression (PPD) is the most preval...   \n",
              "2  Mental illness, including depression, anxiety ...   \n",
              "3  Hepatitis C virus (HCV) and alcoholic liver di...   \n",
              "\n",
              "                                      source  \\\n",
              "0  https://pubmed.ncbi.nlm.nih.gov/20232240/   \n",
              "1  https://pubmed.ncbi.nlm.nih.gov/28012571/   \n",
              "2  https://pubmed.ncbi.nlm.nih.gov/28942748/   \n",
              "3  https://pubmed.ncbi.nlm.nih.gov/22244707/   \n",
              "\n",
              "                                             details  \n",
              "0  RCT of a manualized social treatment for high-...  \n",
              "1  Formatting removed (can be used to compare mod...  \n",
              "2               Effect of nutrition on mental health  \n",
              "3  Baclofen promotes alcohol abstinence in alcoho...  "
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              "      <th></th>\n",
              "      <th>abstract</th>\n",
              "      <th>source</th>\n",
              "      <th>details</th>\n",
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              "      <td>Formatting removed (can be used to compare mod...</td>\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          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",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('abstracts')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "        google.colab.notebook.generateWithVariable('abstracts');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "abstracts",
              "summary": "{\n  \"name\": \"abstracts\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"abstract\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Postpartum depression (PPD) is the most prevalent mood disorder associated with childbirth. No single cause of PPD has been identified, however the increased risk of nutritional deficiencies incurred through the high nutritional requirements of pregnancy may play a role in the pathology of depressive symptoms. Three nutritional interventions have drawn particular interest as possible non-invasive and cost-effective prevention and/or treatment strategies for PPD; omega-3 (n-3) long chain polyunsaturated fatty acids (LCPUFA), vitamin D and overall diet. We searched for meta-analyses of randomised controlled trials (RCT's) of nutritional interventions during the perinatal period with PPD as an outcome, and checked for any trials published subsequently to the meta-analyses. Fish oil: Eleven RCT's of prenatal fish oil supplementation RCT's show null and positive effects on PPD symptoms. Vitamin D: no relevant RCT's were identified, however seven observational studies of maternal vitamin D levels with PPD outcomes showed inconsistent associations. Diet: Two Australian RCT's with dietary advice interventions in pregnancy had a positive and null result on PPD. With the exception of fish oil, few RCT's with nutritional interventions during pregnancy assess PPD. Further research is needed to determine whether nutritional intervention strategies during pregnancy can protect against symptoms of PPD. Given the prevalence of PPD and ease of administering PPD measures, we recommend future prenatal nutritional RCT's include PPD as an outcome.\",\n          \"Hepatitis C virus (HCV) and alcoholic liver disease (ALD), either alone or in combination, count for more than two thirds of all liver diseases in the Western world. There is no safe level of drinking in HCV-infected patients and the most effective goal for these patients is total abstinence. Baclofen, a GABA(B) receptor agonist, represents a promising pharmacotherapy for alcohol dependence (AD). Previously, we performed a randomized clinical trial (RCT), which demonstrated the safety and efficacy of baclofen in patients affected by AD and cirrhosis. The goal of this post-hoc analysis was to explore baclofen's effect in a subgroup of alcohol-dependent HCV-infected cirrhotic patients. Any patient with HCV infection was selected for this analysis. Among the 84 subjects randomized in the main trial, 24 alcohol-dependent cirrhotic patients had a HCV infection; 12 received baclofen 10mg t.i.d. and 12 received placebo for 12-weeks. With respect to the placebo group (3/12, 25.0%), a significantly higher number of patients who achieved and maintained total alcohol abstinence was found in the baclofen group (10/12, 83.3%; p=0.0123). Furthermore, in the baclofen group, compared to placebo, there was a significantly higher increase in albumin values from baseline (p=0.0132) and a trend toward a significant reduction in INR levels from baseline (p=0.0716). In conclusion, baclofen was safe and significantly more effective than placebo in promoting alcohol abstinence, and improving some Liver Function Tests (LFTs) (i.e. albumin, INR) in alcohol-dependent HCV-infected cirrhotic patients. Baclofen may represent a clinically relevant alcohol pharmacotherapy for these patients.\",\n          \"This RCT examined the efficacy of a manualized social intervention for children with HFASDs. Participants were randomly assigned to treatment or wait-list conditions. Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language. A response-cost program was applied to reduce problem behaviors and foster skills acquisition. Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures). Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents. High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity. Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"https://pubmed.ncbi.nlm.nih.gov/28012571/\",\n          \"https://pubmed.ncbi.nlm.nih.gov/22244707/\",\n          \"https://pubmed.ncbi.nlm.nih.gov/20232240/\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"details\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Formatting removed (can be used to compare model to actual example)\",\n          \"Baclofen promotes alcohol abstinence in alcohol dependent cirrhotic patients with hepatitis C virus (HCV) infection\",\n          \"RCT of a manualized social treatment for high-functioning autism spectrum disorders\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from spacy.lang.en import English\n",
        "nlp = English() # setup English sentence parser\n",
        "\n",
        "# New version of spaCy\n",
        "sentencizer = nlp.add_pipe(\"sentencizer\")\n",
        "doc = nlp(example_abstracts[0][\"abstract\"])\n",
        "abstract_lines = [str(sent) for sent in list(doc.sents)]\n",
        "abstract_lines"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNBrnRB9L4r7",
        "outputId": "87a304bf-5ed7-4958-afb2-ed7845d3ef0c"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This RCT examined the efficacy of a manualized social intervention for children with HFASDs.',\n",
              " 'Participants were randomly assigned to treatment or wait-list conditions.',\n",
              " 'Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language.',\n",
              " 'A response-cost program was applied to reduce problem behaviors and foster skills acquisition.',\n",
              " 'Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures).',\n",
              " 'Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents.',\n",
              " 'High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity.',\n",
              " 'Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.']"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_lines_in_sample = len(abstract_lines)\n",
        "\n",
        "# Going through each line in abstract and create a list of dictionaries containing features for each line\n",
        "sample_lines = []\n",
        "for i, line in enumerate(abstract_lines):\n",
        "  sample_dict = {}\n",
        "  sample_dict[\"text\"] = str(line)\n",
        "  sample_dict[\"line_number\"] = i\n",
        "  sample_dict[\"total_lines\"] = total_lines_in_sample - 1\n",
        "  sample_lines.append(sample_dict)\n",
        "sample_lines"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apEgfz6VMGqF",
        "outputId": "d4d4e863-457d-4d52-8040-d25d70a5d001"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'text': 'This RCT examined the efficacy of a manualized social intervention for children with HFASDs.',\n",
              "  'line_number': 0,\n",
              "  'total_lines': 7},\n",
              " {'text': 'Participants were randomly assigned to treatment or wait-list conditions.',\n",
              "  'line_number': 1,\n",
              "  'total_lines': 7},\n",
              " {'text': 'Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language.',\n",
              "  'line_number': 2,\n",
              "  'total_lines': 7},\n",
              " {'text': 'A response-cost program was applied to reduce problem behaviors and foster skills acquisition.',\n",
              "  'line_number': 3,\n",
              "  'total_lines': 7},\n",
              " {'text': 'Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures).',\n",
              "  'line_number': 4,\n",
              "  'total_lines': 7},\n",
              " {'text': 'Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents.',\n",
              "  'line_number': 5,\n",
              "  'total_lines': 7},\n",
              " {'text': 'High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity.',\n",
              "  'line_number': 6,\n",
              "  'total_lines': 7},\n",
              " {'text': 'Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.',\n",
              "  'line_number': 7,\n",
              "  'total_lines': 7}]"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all line_number values from sample abstract\n",
        "test_abstract_line_numbers = [line[\"line_number\"] for line in sample_lines]\n",
        "# One-hot encode to same depth as training data, so model accepts right input shape\n",
        "test_abstract_line_numbers_one_hot = tf.one_hot(test_abstract_line_numbers, depth=15)\n",
        "test_abstract_line_numbers_one_hot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2WU5kOkMP7B",
        "outputId": "56e6f051-7cc6-4592-ee41-bcacbb52dd98"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(8, 15), dtype=float32, numpy=\n",
              "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_abstract_total_lines = [line[\"total_lines\"] for line in sample_lines]\n",
        "# One-hot encode to same depth as training data, so model accepts right input shape\n",
        "test_abstract_total_lines_one_hot = tf.one_hot(test_abstract_total_lines, depth=20)\n",
        "test_abstract_total_lines_one_hot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d149ZMf_MUIt",
        "outputId": "5f6a8765-5a35-4916-c106-5527a724892b"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(8, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abstract_chars = [split_chars(sentence) for sentence in abstract_lines]\n",
        "abstract_chars"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQQaQdcgMXlT",
        "outputId": "0a47c2af-343f-452d-fd97-c83de13d7d50"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T h i s   R C T   e x a m i n e d   t h e   e f f i c a c y   o f   a   m a n u a l i z e d   s o c i a l   i n t e r v e n t i o n   f o r   c h i l d r e n   w i t h   H F A S D s .',\n",
              " 'P a r t i c i p a n t s   w e r e   r a n d o m l y   a s s i g n e d   t o   t r e a t m e n t   o r   w a i t - l i s t   c o n d i t i o n s .',\n",
              " 'T r e a t m e n t   i n c l u d e d   i n s t r u c t i o n   a n d   t h e r a p e u t i c   a c t i v i t i e s   t a r g e t i n g   s o c i a l   s k i l l s ,   f a c e - e m o t i o n   r e c o g n i t i o n ,   i n t e r e s t   e x p a n s i o n ,   a n d   i n t e r p r e t a t i o n   o f   n o n - l i t e r a l   l a n g u a g e .',\n",
              " 'A   r e s p o n s e - c o s t   p r o g r a m   w a s   a p p l i e d   t o   r e d u c e   p r o b l e m   b e h a v i o r s   a n d   f o s t e r   s k i l l s   a c q u i s i t i o n .',\n",
              " 'S i g n i f i c a n t   t r e a t m e n t   e f f e c t s   w e r e   f o u n d   f o r   f i v e   o f   s e v e n   p r i m a r y   o u t c o m e   m e a s u r e s   ( p a r e n t   r a t i n g s   a n d   d i r e c t   c h i l d   m e a s u r e s ) .',\n",
              " 'S e c o n d a r y   m e a s u r e s   b a s e d   o n   s t a f f   r a t i n g s   ( t r e a t m e n t   g r o u p   o n l y )   c o r r o b o r a t e d   g a i n s   r e p o r t e d   b y   p a r e n t s .',\n",
              " 'H i g h   l e v e l s   o f   p a r e n t ,   c h i l d   a n d   s t a f f   s a t i s f a c t i o n   w e r e   r e p o r t e d ,   a l o n g   w i t h   h i g h   l e v e l s   o f   t r e a t m e n t   f i d e l i t y .',\n",
              " 'S t a n d a r d i z e d   e f f e c t   s i z e   e s t i m a t e s   w e r e   p r i m a r i l y   i n   t h e   m e d i u m   a n d   l a r g e   r a n g e s   a n d   f a v o r e d   t h e   t r e a t m e n t   g r o u p .']"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "test_abstract_pred_probs = loaded_model.predict(x=(test_abstract_line_numbers_one_hot,\n",
        "                                                   test_abstract_total_lines_one_hot,\n",
        "                                                   tf.constant(abstract_lines),\n",
        "                                                   tf.constant(abstract_chars)))\n",
        "test_abstract_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKUnnEphMdRZ",
        "outputId": "f9a87c07-e202-4254-b7d5-fa2538b79606"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 3s 3s/step\n",
            "CPU times: user 3.28 s, sys: 40.7 ms, total: 3.32 s\n",
            "Wall time: 3.38 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.28690514, 0.10211611, 0.0174861 , 0.5552143 , 0.03827835],\n",
              "       [0.09694309, 0.03847259, 0.6550533 , 0.13190189, 0.0776291 ],\n",
              "       [0.14318798, 0.06731336, 0.513035  , 0.15959392, 0.11686973],\n",
              "       [0.10451848, 0.15988418, 0.537608  , 0.07666254, 0.12132671],\n",
              "       [0.05492591, 0.09129702, 0.35582656, 0.05322538, 0.44472516],\n",
              "       [0.03451477, 0.09731475, 0.53833646, 0.04602934, 0.28380468],\n",
              "       [0.02481512, 0.12711091, 0.06801536, 0.02557305, 0.7544856 ],\n",
              "       [0.01728837, 0.10049945, 0.13822778, 0.03018529, 0.71379906]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_abstract_preds = tf.argmax(test_abstract_pred_probs, axis=1)\n",
        "test_abstract_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UyUol90sMgpV",
        "outputId": "b8327dc7-e01a-4bb6-e1f5-778bba940139"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(8,), dtype=int64, numpy=array([3, 2, 2, 2, 4, 2, 4, 4])>"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_abstract_pred_classes = [label_encoder.classes_[i] for i in test_abstract_preds]\n",
        "test_abstract_pred_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVzQe57SMkmg",
        "outputId": "243fa46a-4c85-4f77-8242-0a264872db5e"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['OBJECTIVE',\n",
              " 'METHODS',\n",
              " 'METHODS',\n",
              " 'METHODS',\n",
              " 'RESULTS',\n",
              " 'METHODS',\n",
              " 'RESULTS',\n",
              " 'RESULTS']"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, line in enumerate(abstract_lines):\n",
        "  print(f\"{test_abstract_pred_classes[i]}: {line}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zw3TfkeLMqR3",
        "outputId": "445c9779-5e5e-4bec-a35d-41b2c34d7e46"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OBJECTIVE: This RCT examined the efficacy of a manualized social intervention for children with HFASDs.\n",
            "METHODS: Participants were randomly assigned to treatment or wait-list conditions.\n",
            "METHODS: Treatment included instruction and therapeutic activities targeting social skills, face-emotion recognition, interest expansion, and interpretation of non-literal language.\n",
            "METHODS: A response-cost program was applied to reduce problem behaviors and foster skills acquisition.\n",
            "RESULTS: Significant treatment effects were found for five of seven primary outcome measures (parent ratings and direct child measures).\n",
            "METHODS: Secondary measures based on staff ratings (treatment group only) corroborated gains reported by parents.\n",
            "RESULTS: High levels of parent, child and staff satisfaction were reported, along with high levels of treatment fidelity.\n",
            "RESULTS: Standardized effect size estimates were primarily in the medium and large ranges and favored the treatment group.\n"
          ]
        }
      ]
    }
  ]
}