09_SkimLit_NLP_milestone_project_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyNSVi7+fadGwU4qnsuFX75I",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/varmatilak22/learning_tensorflow/blob/main/09_SkimLit_NLP_milestone_project_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MileStone Project 2: SkimLit 📑🔥\n",
        "\n",
        "The purpose is to build a nlp model to make reading medical abstracts easier.\n",
        "\n",
        "The paper we're replicating (the source of the dataset that we'll be using) is available here: https://arxiv.org/abs/1710.06071\n",
        "\n",
        "And reading through the above paper above, we see that the model architecture that they use a achieve their best results is available here: https://arxiv.org/abs/1710.06071\n",
        "\n",
        "📖 **Resources:** If you want to find the ground truth for this notebook (with lots of diagram and text annotations) see the  GitHub: https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/09_SkimLit_nlp_milestone_project_2.ipynb"
      ],
      "metadata": {
        "id": "Gk3BEOFaS6NY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confirm access to a GPU"
      ],
      "metadata": {
        "id": "JcdplzydTIOC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6sPkqL6VH2u",
        "outputId": "7f7e82e2-d11f-4cf3-e19a-b3679852d8e9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Dec 31 10:41:35 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get dataset\n",
        "\n",
        "Since we'll replicating the research paper above (PubMed 200K RCT), lets download the dataset they used.\n",
        "\n",
        "We can do so from the authors GitHub : https://github.com/Franck-Dernoncourt/pubmed-rct"
      ],
      "metadata": {
        "id": "qOJXEMASVKAa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/Franck-Dernoncourt/pubmed-rct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BKNtEM-SVs61",
        "outputId": "92aa105e-9085-4c81-c388-60409273c4a7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'pubmed-rct' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls pubmed-rct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uaa0hcfzWN2Z",
        "outputId": "d272f4fd-fb40-4ac0-b34b-bfe20708fc3e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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": [
        "#Check what files are in the PubMed 20k Dataset\n",
        "!ls pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZoBPzkRWRgN",
        "outputId": "b86932f3-80c3-4e3b-daa2-9d3edde8fc9d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dev.txt  test.txt  train.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls pubmed-rct/PubMed_20k_RCT/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNIs4umMXAtB",
        "outputId": "52b259c5-0eeb-4978-8ca8-a2c84741bf36"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dev.txt  test.txt  train.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Start our experiments using te 20K dataset with numbers replaced by \"@\" Sign\n",
        "data_dir='/content/pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/'"
      ],
      "metadata": {
        "id": "vxN8tWKPXU8_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check all the file names in the target directory\n",
        "import os\n",
        "\n",
        "filenames=[data_dir+filename for filename in os.listdir(data_dir)]\n",
        "filenames"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5R3TIKR8YkX7",
        "outputId": "635fd199-3a15-455d-bb35-9e5481183f88"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/dev.txt',\n",
              " '/content/pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/test.txt',\n",
              " '/content/pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/train.txt']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocess data\n",
        "Now we've got some data, its time to become one with it...\n",
        "\n",
        "And one of the best ways to become one with the data is to...\n",
        "\n",
        "> Visualize, Visualise , Visualise\n",
        "\n",
        "So with that in mind lets write a function to read in all of the lines of a target text file.\n"
      ],
      "metadata": {
        "id": "Py5XtXvEYuHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create function to read the lines of document\n",
        "def get_lines(filename):\n",
        "  \"\"\"\n",
        "  Reads filename(a text filename) and returns the lines of text as a list.\n",
        "  Args:\n",
        "     filename: a string containing the target filepath.\n",
        "\n",
        "  Returns:\n",
        "     A list of strings with one string per line from the target file.\n",
        "  \"\"\"\n",
        "  with open(filename,'r') as file:\n",
        "    return file.readlines()\n"
      ],
      "metadata": {
        "id": "4CeRFACiZtP9"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets read in the training lines\n",
        "train_lines=get_lines(data_dir+'train.txt') #read the lines with the training file\n",
        "train_lines[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "485KMpuaaouC",
        "outputId": "898fef0b-bcb5-4175-a757-2b088d69b10e"
      },
      "execution_count": 10,
      "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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_lines)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DY6e77Mcary8",
        "outputId": "e4a60699-28b3-4e69-e75c-fb374fdd9cea"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "210040"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets think about how we want our data to look...\n",
        "\n",
        "How I think our data would be best represented...\n",
        "\n",
        "```\n",
        "[{'line_number':0,\n",
        "   'target': BACKGROUND,\n",
        "   'text':Emotional eating is associated with overeating and the development of obesity .\\n'\n",
        "   'total_lines':11},\n",
        "   ...]\n",
        "```"
      ],
      "metadata": {
        "id": "6541iJVubidq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets write a function which turns each of our datasets into the above formats so we can continue to prepare"
      ],
      "metadata": {
        "id": "wZIQZfkzbvU9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_text_with_line_numbers(filename):\n",
        "  \"\"\"\n",
        "  Returns a list of dictionaries of abstract line data.\n",
        "\n",
        "  Takes in filename, reads it contents and sorts through  each line,\n",
        "  extracting things like the target label, the text of the sentence ,\n",
        "  how many sentences are in the current abstarct and what sentence number the target line is.\n",
        "  \"\"\"\n",
        "  input_lines=get_lines(filename) #get all the lines from filename\n",
        "  abstract_lines='' #create empty abstract\n",
        "  abstract_samples=[] #empty list of abstracts\n",
        "\n",
        "  #loop throug each line in the target file\n",
        "  for line in input_lines:\n",
        "    if line.startswith('###'): #Check to see if the is an ID line\n",
        "      abstract_id=line\n",
        "      abstract_lines=\"\" #reset the abstract string if the line is an ID line\n",
        "    elif line.isspace(): #check to see if the line is a new line\n",
        "      abstract_line_split=abstract_lines.splitlines() # split abstract into separate lines\n",
        "      # iterate through each line in a single abstract and count them at the same time\n",
        "      for abstract_line_num,abstract_line in enumerate(abstract_line_split):\n",
        "        line_data={} #Create an empty dictionary for each 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_num #WHat num line appear in the abstract\n",
        "        line_data['total_lines']=len(abstract_line_split)-1 #how many total lines are there in the target abstract ?(start from 0)\n",
        "        abstract_samples.append(line_data) #add line data to abstract samples list\n",
        "\n",
        "\n",
        "    else: #if the above condtions aren't fulfilled , the lines contains a labelled sentences\n",
        "        abstract_lines +=line\n",
        "\n",
        "  return abstract_samples"
      ],
      "metadata": {
        "id": "9VYhVu-KeWEg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get data from file and preprocess it\n",
        "%%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 val_set\n",
        "test_samples=preprocess_text_with_line_numbers(data_dir+'test.txt')\n",
        "print(len(train_samples),len(val_samples),len(test_samples))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_IhqvCij7Al",
        "outputId": "f26bb2a3-19af-4cf7-fe54-479302154175"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "180040 30212 30135\n",
            "CPU times: user 576 ms, sys: 120 ms, total: 696 ms\n",
            "Wall time: 1.28 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check first abstract of training data\n",
        "train_samples[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gi3ro_H9k56-",
        "outputId": "84d13e37-46ac-4a0f-aee1-c50cfb362a79"
      },
      "execution_count": 14,
      "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}]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that our data is the format of a list of dictionaries, how about we turn it into a dataframe to further visualise it."
      ],
      "metadata": {
        "id": "N1rMRtz1o4nl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df=pd.DataFrame(train_samples)\n",
        "val_df=pd.DataFrame(val_samples)\n",
        "test_df=pd.DataFrame(test_samples)\n",
        "\n",
        "train_df.head(14)\n"
      ],
      "metadata": {
        "id": "Qg3gkWyLlRfv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "de96ecf1-5fac-4dcd-a3f1-ce43ba09452a"
      },
      "execution_count": 15,
      "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-b8599f2a-8caf-42ac-8620-a2f175b64901\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <th></th>\n",
              "      <th>target</th>\n",
              "      <th>text</th>\n",
              "      <th>line_number</th>\n",
              "      <th>total_lines</th>\n",
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              "      <th>0</th>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>to investigate the efficacy of @ weeks of dail...</td>\n",
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              "      <td>11</td>\n",
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              "      <td>1</td>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>METHODS</td>\n",
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              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>pain was assessed using the visual analog pain...</td>\n",
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              "      <th>4</th>\n",
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              "    <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",
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              "    <tr>\n",
              "      <th>10</th>\n",
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              "      <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",
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              "  @keyframes spin {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Distrbution of labels\n",
        "train_df.target.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "Qfrj08w8pU0i",
        "outputId": "bbee5bc5-000d-41db-a316-0e5b647ebebb"
      },
      "execution_count": 16,
      "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"
            ],
            "text/html": [
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              "      <th>count</th>\n",
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              "      <th></th>\n",
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              "      <th>METHODS</th>\n",
              "      <td>59353</td>\n",
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              "      <th>RESULTS</th>\n",
              "      <td>57953</td>\n",
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              "      <th>CONCLUSIONS</th>\n",
              "      <td>27168</td>\n",
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              "      <td>13839</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets length of different lines\n",
        "train_df.total_lines.plot.hist();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "tNB-UzhepuPb",
        "outputId": "e3e12bc7-3d45-4053-cfb7-b51b0764e076"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Get list of sentences"
      ],
      "metadata": {
        "id": "Q77rvYVmp-wq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert abstract text lines into list\n",
        "train_sentences=train_df.text.to_list()\n",
        "val_sentences=val_df.text.to_list()\n",
        "test_sentences=test_df.text.to_list()\n",
        "len(train_sentences),len(val_sentences),len(test_sentences),"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9vWGH5AqMSP",
        "outputId": "613d484c-d577-4ec5-8b9a-26442d1a8918"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180040, 30212, 30135)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#View 10 lines of training sentences\n",
        "train_sentences[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JydCLHnqcir",
        "outputId": "d867fe86-f538-4d98-c81a-4db0f0909be2"
      },
      "execution_count": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make Numeric labels (ML Models also require numeric labels)"
      ],
      "metadata": {
        "id": "86fD-YdgquCA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# One Hot Encoding labels\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "one_hot_encoder=OneHotEncoder(sparse_output=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",
        "\n",
        "#What one hot encoded target labels looks like?\n",
        "train_labels_one_hot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAdpZCFBrVDN",
        "outputId": "528e6e94-69aa-4be5-dc68-06ea391dd904"
      },
      "execution_count": 20,
      "outputs": [
        {
          "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.]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label Encoded Labels"
      ],
      "metadata": {
        "id": "JpQ6laF8tOcj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Extract labels ('target columns') and encode into integers\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder=LabelEncoder()\n",
        "train_label_encoded=label_encoder.fit_transform(train_df['target'])\n",
        "val_label_encoded=label_encoder.transform(val_df['target'])\n",
        "test_label_encoded=label_encoder.transform(test_df['target'])\n",
        "\n",
        "#train label encoded looks like?\n",
        "train_label_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saGLSKvhr0Ub",
        "outputId": "891d1b1c-635f-431b-f284-fbe101678d6e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 2, ..., 4, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get class names and number of classes from labelEncoder instance\n",
        "num_classes=len(label_encoder.classes_)\n",
        "class_names=label_encoder.classes_\n",
        "num_classes,class_names\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KoUJuVrr3Wl",
        "outputId": "f33005c9-28ce-45a8-8550-4fa0fba13cb0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5,\n",
              " array(['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'],\n",
              "       dtype=object))"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Starting a series of modelling experiments...\n",
        "\n",
        "As usual, we're going to be trying out a bunch of different models and seeing which one works best.\n",
        "\n",
        "And as always, we're going to start with a baseline (TF-IDF Multinomial Navie Bayes Classifier)"
      ],
      "metadata": {
        "id": "6e_eDmiAvZuH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 0: Getting a Baseline Model"
      ],
      "metadata": {
        "id": "n0avitfHuq1i"
      }
    },
    {
      "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",
        "#Create a pipeline\n",
        "model_0=Pipeline([\n",
        "    ('TF-IDF',TfidfVectorizer()),\n",
        "    ('Classifier',MultinomialNB())\n",
        "])\n",
        "\n",
        "#Fit the pipeline to the training data\n",
        "model_0.fit(X=train_sentences,y=train_label_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "id": "TiIUXH9pv_y-",
        "outputId": "06f77b77-febf-442b-da27-1526f1b6e11d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('TF-IDF', TfidfVectorizer()), ('Classifier', MultinomialNB())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</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;Classifier&#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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;TF-IDF&#x27;, TfidfVectorizer()), (&#x27;Classifier&#x27;, MultinomialNB())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow\"><div><div>TfidfVectorizer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow\"><div><div>MultinomialNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate baseline model on validation dataset\n",
        "model_0.score(X=val_sentences,y=val_label_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CH6YZPdYxAfD",
        "outputId": "03546f84-c959-4802-8a91-0c36f1f1f206"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7218323844829869"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make prediction using our baseline model\n",
        "baseline_preds=model_0.predict(val_sentences)\n",
        "baseline_preds[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPqO9Oj-xXfa",
        "outputId": "aed70f62-450f-4351-88b9-259da69c0dd5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 1, 3, 2, 2, 2, 2, 2, 4, 4, 0, 4, 1, 1, 2, 2, 4, 1, 4, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download Helper functions scripts\n",
        "\n",
        "In the previous module , we wrote a function to compare predictions across different metrics (accu,preci,f1,recall) and rather than rewriting it here, lets download it from a our helper function script.\n",
        "\n",
        "https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/refs/heads/main/extras/helper_functions.py"
      ],
      "metadata": {
        "id": "aRL-yHq2xrRQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/refs/heads/main/extras/helper_functions.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQoHXRh1yLrL",
        "outputId": "2334beb8-1d64-45e6-84ca-8d69c8a13e24"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-12-31 10:41:41--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/refs/heads/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.1’\n",
            "\n",
            "\rhelper_functions.py   0%[                    ]       0  --.-KB/s               \rhelper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2024-12-31 10:41:41 (14.8 MB/s) - ‘helper_functions.py.1’ saved [10246/10246]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import calculate_results"
      ],
      "metadata": {
        "id": "-5-_MsLTySbZ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results=calculate_results(y_true=val_label_encoded,\n",
        "                                   y_pred=baseline_preds)\n",
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sT03uflyjv3",
        "outputId": "edf62929-2526-4491-b056-a95b67e99f46"
      },
      "execution_count": 28,
      "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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X98jQjWZzPMc",
        "outputId": "734277cd-d6d5-4039-a0d3-2958829dccef"
      },
      "execution_count": 29,
      "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": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing our data(the text) for deep sequence models\n",
        "\n",
        "Before we start building deeper models, we've got to create vectorization and embedding layers."
      ],
      "metadata": {
        "id": "qNFdDuFYznvm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "-tYPNsOM0M-K"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# how long is each sentence on average?\n",
        "sent_lens=[len(sentence.split()) for sentence in train_sentences]\n",
        "sent_lens[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQf5Vsah0XAv",
        "outputId": "49cc6413-024d-4f34-bd89-ca1d5ac1e7a9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[49, 27, 15, 14, 35]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "avg_sent_len=np.mean(sent_lens)\n",
        "max_sent_len=np.max(sent_lens)\n",
        "min_sent_len=np.min(sent_lens)\n",
        "\n",
        "avg_sent_len,max_sent_len,min_sent_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0K8Ccpe0x6B",
        "outputId": "0fea9d03-c60c-438b-ee41-58b98eb14845"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26.338269273494777, 296, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#What distribution looks like?\n",
        "import matplotlib.pyplot as plt\n",
        "plt.hist(sent_lens,bins=7);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "QS6O5vOE1Npu",
        "outputId": "93caab49-9f78-4a46-8ea9-eab150000d62"
      },
      "execution_count": 33,
      "outputs": [
        {
          "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": [
        "# How long a sentence lengths overs 95% of examples?\n",
        "output_seq_len=int(np.percentile(sent_lens,95))\n",
        "output_seq_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLVDH3ZT4j5v",
        "outputId": "00acd986-4378-4b63-8a49-3ff5b24e190d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Maximum sequence length in training sen\n",
        "max(sent_lens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrbMilGX5DG3",
        "outputId": "025ac5fc-c93a-496d-cecb-9509f6e28081"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "296"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create Text vectorizer\n",
        " We want to make a layer which maps our transform words into numbers"
      ],
      "metadata": {
        "id": "ftgCB6Dr5yKq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# How many words are in our vocabulary?(taken table-2 from research paper)\n",
        "max_tokens=68000\n"
      ],
      "metadata": {
        "id": "T9tivwbr6W9A"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create text vectorizer\n",
        "from tensorflow.keras.layers  import TextVectorization\n",
        "\n",
        "text_vectorizer=TextVectorization(max_tokens=max_tokens,# number of words in vocabulary\n",
        "                                  output_sequence_length=output_seq_len) #desired output length of vectorized  sequence"
      ],
      "metadata": {
        "id": "FWhLFmV27QHw"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Adapt text vectorizer to training sentences\n",
        "text_vectorizer.adapt(train_sentences)"
      ],
      "metadata": {
        "id": "vElQ4kIz8A8G"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out text vectorizer on random sentences\n",
        "import random\n",
        "target_sentence=random.choice(train_sentences)\n",
        "print(f\"Text:\\n{target_sentence}\")\n",
        "print(f\"\\nLength of Text:{len(target_sentence.split())}\")\n",
        "print(f\"\\nVectorized Text:\\n{text_vectorizer([target_sentence])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5S4A1VAh8d40",
        "outputId": "eb0c9367-293d-4a89-97cc-2bd7b6815115"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text:\n",
            "this suggests that the genetic vulnerability may be mitigated by altering environmental factors .\n",
            "\n",
            "Length of Text:14\n",
            "\n",
            "Vectorized Text:\n",
            "[[   23   908    28     2  1585  8779    91    36 10257    22  8320  2810\n",
            "    254     0     0     0     0     0     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     0     0     0     0     0\n",
            "      0     0     0     0     0     0     0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How many words in our training vocabulary?\n",
        "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 in the vocab:{rct_20k_text_vocab[:5]}\")\n",
        "print(f\"Least Common words in the vocab:{rct_20k_text_vocab[-5:]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "if7qqVZU81hR",
        "outputId": "d1218588-1d48-4473-db73-16a6d5720314"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words in vocab:64841\n",
            "Most Common words in the vocab:['', '[UNK]', 'the', 'and', 'of']\n",
            "Least Common words in the vocab:['aainduced', 'aaigroup', 'aachener', 'aachen', 'aaacp']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a Config of our text vectorizer\n",
        "text_vectorizer.get_config()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBXMEekP-Vi6",
        "outputId": "380c72e3-e09b-42bd-892e-274fc9ea62bc"
      },
      "execution_count": 41,
      "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": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create Custom text embeddings"
      ],
      "metadata": {
        "id": "NHJtChTE_iY4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "token_embed=layers.Embedding(input_dim=len(rct_20k_text_vocab), #length of vocabulary\n",
        "                             output_dim=128, #note: different embedding sizes result in drastically different number of parameters\n",
        "                             mask_zero=True, # use masking to handle variable sequence length (save space)\n",
        "                             name='Token_embedding')"
      ],
      "metadata": {
        "id": "o2aKnfCOqAXA"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Show example embedding\n",
        "print(f\"Sentence before Vectorization:\\n{target_sentence}\\n\")\n",
        "vectorized_sentence=text_vectorizer([target_sentence])\n",
        "print(f\"Sentence after Vectorization:\\n{vectorized_sentence}\\n\")\n",
        "embedded_sentence=token_embed(vectorized_sentence)\n",
        "print(f\"Sentence after embedding:\\n{embedded_sentence}\")\n",
        "print(f\"Embedded Sentence Shape:{embedded_sentence.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErWxzL6zrk0A",
        "outputId": "2d60481b-7270-4997-8e12-b287261ec688"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence before Vectorization:\n",
            "this suggests that the genetic vulnerability may be mitigated by altering environmental factors .\n",
            "\n",
            "Sentence after Vectorization:\n",
            "[[   23   908    28     2  1585  8779    91    36 10257    22  8320  2810\n",
            "    254     0     0     0     0     0     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     0     0     0     0     0\n",
            "      0     0     0     0     0     0     0]]\n",
            "\n",
            "Sentence after embedding:\n",
            "[[[ 0.02278353 -0.01678926  0.01852168 ... -0.04625212 -0.0323707\n",
            "   -0.01984109]\n",
            "  [-0.0311354  -0.04685639  0.007951   ... -0.03000546 -0.02932243\n",
            "   -0.03785968]\n",
            "  [ 0.02610003 -0.04321218  0.04136833 ... -0.00293349  0.01021707\n",
            "    0.02524679]\n",
            "  ...\n",
            "  [-0.02944266  0.04310201 -0.03319885 ... -0.01906626 -0.01239919\n",
            "    0.02674221]\n",
            "  [-0.02944266  0.04310201 -0.03319885 ... -0.01906626 -0.01239919\n",
            "    0.02674221]\n",
            "  [-0.02944266  0.04310201 -0.03319885 ... -0.01906626 -0.01239919\n",
            "    0.02674221]]]\n",
            "Embedded Sentence Shape:(1, 55, 128)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating datasets (making sure our data loads fast as possible)\n",
        "\n",
        "We're going to setup our data to run as fast possible with the Tensorflow  tf.data API, many of the steps here are discussed at length in the these two resources:\n",
        "* https://www.tensorflow.org/guide/data_performance\n",
        "* https://www.tensorflow.org/guide/data"
      ],
      "metadata": {
        "id": "28BoSaQErodV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn our data into Tensorflow Datasets\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",
        "\n",
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLmf_u5quhYo",
        "outputId": "e55e6067-ffd0-4241-e336-1fc01ac322ed"
      },
      "execution_count": 44,
      "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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels_one_hot.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_TqkzvNvT1q",
        "outputId": "48c00c40-d133-4dba-c7e5-3de189fb09a4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180040, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Takes the TensorSliceDatasets and turn them into prefetched datasets\n",
        "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)"
      ],
      "metadata": {
        "id": "8vDWwYRkvvH1"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nogbNfow2yR",
        "outputId": "9c9589ba-341f-44d2-8cdb-c54cc4007d73"
      },
      "execution_count": 47,
      "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": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 1: Conv1D with Token Embeddings"
      ],
      "metadata": {
        "id": "vOzDBBpuxKpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create 1D Conv Model to process sequences\n",
        "inputs=layers.Input(shape=(1,),dtype=tf.string)\n",
        "text_vectors=text_vectorizer(inputs) # Vectorize text inputs\n",
        "token_embedding=token_embed(text_vectors) # Create embedding\n",
        "x=layers.Conv1D(filters=63,kernel_size=5,activation='relu',padding='same')(token_embedding)\n",
        "x=layers.GlobalAveragePooling1D()(x) #condense the output of our feature vector from Conv layer\n",
        "outputs=layers.Dense(num_classes,activation='softmax')(x)\n",
        "model_1=tf.keras.Model(inputs,outputs)\n",
        "\n",
        "\n",
        "#Compile the model\n",
        "model_1.compile(loss='categorical_crossentropy',\n",
        "               optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "RwopHR8Mx5Lf"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Summary\n",
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lj5BfTrczrPU",
        "outputId": "910e12ab-ed6b-4a35-e97d-92351a94bb55"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\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, 63)            40383     \n",
            "                                                                 \n",
            " global_average_pooling1d (  (None, 63)                0         \n",
            " GlobalAveragePooling1D)                                         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 5)                 320       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8340351 (31.82 MB)\n",
            "Trainable params: 8340351 (31.82 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit the model\n",
        "history_model_1=model_1.fit(train_dataset,\n",
        "                            steps_per_epoch=int(0.1*len(train_dataset)),\n",
        "                            epochs=5,\n",
        "                            validation_data=valid_dataset,\n",
        "                            validation_steps=int(0.1*len(valid_dataset)) # only validate on 10% of batches\n",
        "                            )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QsFudUgHz4gw",
        "outputId": "b1887497-65ce-4e79-f9d8-c102fd951f3d"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "562/562 [==============================] - 26s 39ms/step - loss: 0.9013 - accuracy: 0.6441 - val_loss: 0.6843 - val_accuracy: 0.7387\n",
            "Epoch 2/5\n",
            "562/562 [==============================] - 8s 14ms/step - loss: 0.6599 - accuracy: 0.7543 - val_loss: 0.6304 - val_accuracy: 0.7726\n",
            "Epoch 3/5\n",
            "562/562 [==============================] - 7s 12ms/step - loss: 0.6187 - accuracy: 0.7736 - val_loss: 0.5981 - val_accuracy: 0.7852\n",
            "Epoch 4/5\n",
            "562/562 [==============================] - 5s 9ms/step - loss: 0.5896 - accuracy: 0.7878 - val_loss: 0.5785 - val_accuracy: 0.7882\n",
            "Epoch 5/5\n",
            "562/562 [==============================] - 5s 9ms/step - loss: 0.5915 - accuracy: 0.7929 - val_loss: 0.5601 - val_accuracy: 0.7989\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate on whole validation dataset\n",
        "model_1.evaluate(valid_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujJVe2CQ0sH4",
        "outputId": "3761f6ad-7fce-463e-fbed-132c32f16563"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 4s 4ms/step - loss: 0.5608 - accuracy: 0.7998\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5607657432556152, 0.7997815608978271]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions (our model predicts prediction probabilities for each class)\n",
        "model_1_pred_probs=model_1.predict(valid_dataset)\n",
        "model_1_pred_probs,model_1_pred_probs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_S-L6jiV2GVI",
        "outputId": "2a1d06a9-04b4-4f65-bcfa-dcc873d75846"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 5s 5ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[5.1175064e-01, 1.9795731e-01, 4.0519062e-02, 1.9235511e-01,\n",
              "         5.7417858e-02],\n",
              "        [4.3962646e-01, 3.4497595e-01, 2.8384706e-02, 1.4336364e-01,\n",
              "         4.3649249e-02],\n",
              "        [1.7597984e-01, 5.3021898e-03, 1.7073706e-03, 8.1695193e-01,\n",
              "         5.8661859e-05],\n",
              "        ...,\n",
              "        [3.7430240e-05, 7.2674477e-04, 4.1297777e-03, 3.2305310e-05,\n",
              "         9.9507374e-01],\n",
              "        [9.4461828e-02, 2.4250561e-01, 1.3697694e-01, 7.2147086e-02,\n",
              "         4.5390853e-01],\n",
              "        [1.5997081e-01, 7.4444604e-01, 6.1096426e-02, 1.3542381e-02,\n",
              "         2.0944295e-02]], dtype=float32),\n",
              " (30212, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert pred probs to classes\n",
        "model_1_preds=tf.argmax(model_1_pred_probs,axis=1)\n",
        "model_1_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Nzrxd_V2-v9",
        "outputId": "ae5d8335-aa8b-4395-db60-e32101432f14"
      },
      "execution_count": 53,
      "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": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jy6Xbm705Z0y",
        "outputId": "c9022f79-d41b-43f0-e829-ccfcab708615"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(val_label_encoded))\n",
        "print(type(model_1_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1a61wV9O7MCh",
        "outputId": "043b5f8c-7b19-4384-dec1-694631656ee1"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "<class 'tensorflow.python.framework.ops.EagerTensor'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate model_1 results\n",
        "model_1_results=calculate_results(y_true=val_label_encoded,\n",
        "                                  y_pred=model_1_preds.numpy())\n",
        "model_1_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJpUnC0_5jN3",
        "outputId": "ca94a5cd-73b4-4725-8923-87c6030cdae5"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 79.97815437574474,\n",
              " 'precision': 0.7994520094532116,\n",
              " 'recall': 0.7997815437574474,\n",
              " 'f1': 0.7967205841771905}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yif0Muhj5yOk",
        "outputId": "3c6dbb4d-5bf7-46b8-fa7a-0544cefec5c9"
      },
      "execution_count": 57,
      "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": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2: Feature Extraction  with Pretrained token embeddings\n",
        "\n",
        "Now Lets use pretrained word embeddings from tensorflow hub,\n",
        "Universal Sentence encoder:\n",
        "[USE](https://tfhub.dev/google/universal-sentence-encoder/4)\n",
        "\n",
        "The Paper originally used GloVe Embeddings, however we're going to stick with the later created USE Pretrained embeddings."
      ],
      "metadata": {
        "id": "sAk-Cw8i7bMY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow==2.15.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8RYKA8o7R3G3",
        "outputId": "5cbbee21-ebfe-40b8-967f-3188378cea89"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow==2.15.0 in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (3.12.1)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (0.2.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (1.26.4)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0) (24.2)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download pretrained tensorflow hub USE\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "tf_hub_embedding_layer=hub.KerasLayer('https://tfhub.dev/google/universal-sentence-encoder/4',\n",
        "                                      trainable=False,\n",
        "                                      input_shape=[],\n",
        "                                      dtype=tf.string,\n",
        "                                      name='universal_sentence_encoder')"
      ],
      "metadata": {
        "id": "VEOPcmxz-UVj"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out the pretrained embeddings on a random sentences\n",
        "random_train_sentences=random.choice(train_sentences)\n",
        "print(f\"Train Sentences:\\n{random_train_sentences}\")\n",
        "use_embedded_sentence=tf_hub_embedding_layer([random_train_sentences])\n",
        "print(f\"Sentence after embedding:\\n{use_embedded_sentence}\")\n",
        "print(f\"Length of sentences embedding:{len(use_embedded_sentence[0])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Il10z2r58RO_",
        "outputId": "d38ef277-479f-4a6c-f625-f75736f56b7a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Sentences:\n",
            "frequent skin cleaning fulfils the definition of occupational ` wet work ' .\n",
            "Sentence after embedding:\n",
            "[[ 1.09426398e-02  1.15161333e-02  1.22639192e-02  5.92359416e-02\n",
            "   4.50236276e-02 -1.25315301e-02 -2.70660091e-02 -3.13124247e-02\n",
            "   5.35281375e-02  3.00839152e-02 -2.82516400e-03 -1.14080571e-02\n",
            "  -5.11426032e-02 -6.68316483e-02  2.37204526e-02 -8.19745660e-02\n",
            "  -2.22676266e-02  4.02946323e-02  4.53338437e-02  4.87268679e-02\n",
            "  -7.79759511e-02  1.50191709e-02  2.70062219e-02 -2.95653427e-03\n",
            "   6.86976537e-02  2.32043639e-02 -7.42231160e-02  5.95669076e-03\n",
            "  -5.66173568e-02 -4.22403216e-02 -9.97727457e-03  1.00539625e-02\n",
            "   3.40497233e-02  9.75481048e-03 -7.86748230e-02 -3.18326056e-02\n",
            "  -7.06635416e-02 -8.53730664e-02 -1.59744453e-02  7.93431029e-02\n",
            "  -3.72342765e-02  1.67067293e-02 -1.21353185e-02 -1.16672693e-03\n",
            "   3.40883285e-02  2.15929300e-02 -4.90175039e-02  1.57374144e-02\n",
            "  -6.28596246e-02 -5.94458915e-03 -8.50859936e-03  3.67984325e-02\n",
            "   5.08944951e-02  2.63775536e-03 -2.83817668e-03 -7.84111395e-03\n",
            "   3.03547271e-02  8.95002391e-03  7.43643641e-02 -6.69055283e-02\n",
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            "   6.90922588e-02 -3.89623940e-02  4.94608395e-02 -5.51503077e-02\n",
            "   1.30100455e-03  5.19561069e-03  4.16560844e-02 -4.77484651e-02\n",
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            "  -3.28079760e-02  4.48071659e-02  7.40248933e-02 -2.16401052e-02\n",
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            "   2.34149466e-03  2.67530624e-02  4.57403772e-02  3.94846387e-02\n",
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            "   5.43762036e-02 -4.18920182e-02  8.75117257e-02 -7.98967388e-03\n",
            "   4.47782651e-02 -2.91831903e-02 -1.96298119e-03  1.47879859e-02\n",
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            "  -1.45401955e-02  8.75686668e-03  2.30459813e-02 -2.51463987e-02\n",
            "   5.87672926e-02 -6.75691217e-02  1.93778378e-05 -6.92667738e-02\n",
            "  -6.85663298e-02 -1.97411049e-02 -2.03380603e-02 -2.91942656e-02\n",
            "   4.22370955e-02 -4.24082950e-02 -2.41557490e-02  3.62418368e-02\n",
            "   3.19165587e-02  3.22760455e-02 -8.10970832e-03  1.60947107e-02\n",
            "   2.65031178e-02  4.31810990e-02  4.72346246e-02  2.00605690e-02\n",
            "  -7.19304197e-03  1.52761610e-02  5.41956251e-05 -4.16251132e-03\n",
            "   3.65833491e-02  5.72625287e-02  1.49409557e-02  5.68734072e-02\n",
            "   7.69819170e-02 -5.83475158e-02  2.66078655e-02  3.21772648e-03\n",
            "   1.88343320e-02 -1.92715954e-02  3.29938973e-03  4.55049537e-02]]\n",
            "Length of sentences embedding:512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building and fitting an NLP feature extraction model using pretrained embeddings Tensorflow HUb"
      ],
      "metadata": {
        "id": "PvoR4mvS_XsS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.__version__)\n",
        "print(hub.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnPwLkEMIl6o",
        "outputId": "7e83c19e-06d1-4ed6-e2c4-ca3570ae6e90"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.15.0\n",
            "0.16.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the custom layer to warp the UNIVERSAL SENTENCE ENCODER\n",
        "from tensorflow.keras.layers import Layer\n",
        "\n",
        "\n",
        "class USELayer(Layer):\n",
        "  def __init__(self,url='https://tfhub.dev/google/universal-sentence-encoder/4',**kargs):\n",
        "    super(USELayer,self).__init__(**kargs)\n",
        "    self.use_model=hub.load(url)\n",
        "\n",
        "  def call(self,inputs):\n",
        "    return self.use_model(inputs)\n"
      ],
      "metadata": {
        "id": "aa2Mv2z6CMIO"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define feature extraction model using TF hub layers\n",
        "inputs=layers.Input(shape=[],dtype=tf.string)\n",
        "pretrained_embedding=USELayer()(inputs) # tokenize the text and create embedding of each sequence (512 long vectors)\n",
        "x=layers.Dense(128,activation='relu')(pretrained_embedding)\n",
        "#Note: u could add more layers here it you wanted to\n",
        "outputs=layers.Dense(num_classes,activation='softmax')(x) #create the output layer\n",
        "\n",
        "#Create a model\n",
        "model_2=tf.keras.Model(inputs,\n",
        "                       outputs,\n",
        "                       name='model_2_USE_feature_extractor')\n",
        "\n",
        "#Compile the model\n",
        "model_2.compile(loss='categorical_crossentropy',\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "#Struture of model_2\n",
        "model_2.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMk_WRzEALPz",
        "outputId": "9a2620f9-d318-4b61-bcce-a8ffe38f05c7"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2_USE_feature_extractor\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None,)]                 0         \n",
            "                                                                 \n",
            " use_layer (USELayer)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 5)                 645       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 66309 (259.02 KB)\n",
            "Trainable params: 66309 (259.02 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoTlBdXZP6Ib",
        "outputId": "861cc2f2-8642-4c6d-fbc8-14949afd593c"
      },
      "execution_count": 64,
      "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": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for example in train_dataset.take(1):\n",
        "    print(type(example[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ed_ILyv7QwRn",
        "outputId": "0d4e6555-8971-4ee7-9e0c-2a695ab049da"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'tensorflow.python.framework.ops.EagerTensor'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model_2.compile(loss='categorical_crossentropy',\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "#Fit the model\n",
        "history_model_2=model_2.fit(train_dataset,\n",
        "            steps_per_epoch=int(0.1*len(train_dataset)),\n",
        "            epochs=5,\n",
        "            validation_data=valid_dataset,\n",
        "            validation_steps=int(0.1*len(valid_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8yoGe56FDaa",
        "outputId": "4f387228-f9c4-4d7a-c4e1-72d4b152a439"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "562/562 [==============================] - 19s 27ms/step - loss: 0.9147 - accuracy: 0.6519 - val_loss: 0.7958 - val_accuracy: 0.6862\n",
            "Epoch 2/5\n",
            "562/562 [==============================] - 16s 29ms/step - loss: 0.7688 - accuracy: 0.7020 - val_loss: 0.7547 - val_accuracy: 0.7021\n",
            "Epoch 3/5\n",
            "562/562 [==============================] - 15s 26ms/step - loss: 0.7528 - accuracy: 0.7120 - val_loss: 0.7382 - val_accuracy: 0.7144\n",
            "Epoch 4/5\n",
            "562/562 [==============================] - 14s 26ms/step - loss: 0.7196 - accuracy: 0.7231 - val_loss: 0.7117 - val_accuracy: 0.7267\n",
            "Epoch 5/5\n",
            "562/562 [==============================] - 13s 23ms/step - loss: 0.7257 - accuracy: 0.7203 - val_loss: 0.6912 - val_accuracy: 0.7357\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.evaluate(valid_dataset)"
      ],
      "metadata": {
        "id": "OMyN8YpoFtcM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6d803e9-f6f3-4b56-d123-374ff27bd09b"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 19s 20ms/step - loss: 0.6978 - accuracy: 0.7343\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6977660655975342, 0.734343945980072]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make predictions with feature extraction model\n",
        "model_2_pred_probs=model_2.predict(valid_dataset)\n",
        "model_2_pred_probs[:10]"
      ],
      "metadata": {
        "id": "jygegShGGI_k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0ac730d-fb35-4a45-9067-04345bf6cdad"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 13s 14ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.18922949e-01, 2.46137947e-01, 2.75360444e-03, 1.22428477e-01,\n",
              "        9.75701399e-03],\n",
              "       [3.85650963e-01, 5.05059421e-01, 5.45476889e-03, 1.01096839e-01,\n",
              "        2.73800548e-03],\n",
              "       [3.60212386e-01, 9.28177983e-02, 5.30634634e-02, 4.48422760e-01,\n",
              "        4.54835668e-02],\n",
              "       [7.17419805e-03, 1.54744545e-02, 9.25609589e-01, 3.47000989e-03,\n",
              "        4.82717417e-02],\n",
              "       [1.19298687e-02, 1.46220967e-01, 4.65363234e-01, 5.07986872e-03,\n",
              "        3.71406049e-01],\n",
              "       [4.65796969e-04, 9.06857022e-04, 7.52344906e-01, 5.20675458e-05,\n",
              "        2.46230304e-01],\n",
              "       [1.09526468e-03, 2.61678826e-03, 6.17229104e-01, 7.04336970e-04,\n",
              "        3.78354520e-01],\n",
              "       [2.35970342e-03, 1.31986123e-02, 6.94029808e-01, 6.55941200e-04,\n",
              "        2.89755970e-01],\n",
              "       [5.81795815e-04, 4.88966890e-03, 1.96429864e-02, 8.48358672e-04,\n",
              "        9.74037170e-01],\n",
              "       [1.70134932e-01, 5.61169147e-01, 6.74825832e-02, 5.16006351e-02,\n",
              "        1.49612695e-01]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the prediction probabilities with feature extraction model to labels\n",
        "model_2_preds=np.argmax(model_2_pred_probs,axis=1)\n",
        "model_2_preds[:10]"
      ],
      "metadata": {
        "id": "ajg3PcvRGnQW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfb54791-527a-49e4-dc24-19506dc1fc8e"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 3, 2, 2, 2, 2, 2, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate results from tf hub pretrained embeddings  results on val set\n",
        "model_2_results=calculate_results(y_true=val_label_encoded,\n",
        "                                  y_pred=model_2_preds)\n",
        "model_2_results"
      ],
      "metadata": {
        "id": "pC2o0X9YG6vR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75871c69-3af2-4106-8247-32f5da2f1b92"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 73.43439692837283,\n",
              " 'precision': 0.7309195808887494,\n",
              " 'recall': 0.7343439692837284,\n",
              " 'f1': 0.7287600341244503}"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
      ],
      "metadata": {
        "id": "C8cpV5ECHSb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db58d1d0-e338-4d3d-ba4a-3733d3774200"
      },
      "execution_count": 71,
      "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": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 3: Conv1D with Character Embeddings\n",
        "\n",
        "The paper we've replicating states they used a combination of token and character-level embeddings.\n",
        "\n",
        "Previously, we;ve made token level embeddings but we need similar for characters if we want to use char-level embeddings"
      ],
      "metadata": {
        "id": "o17MrdwDHTcd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a character-level tokenizer"
      ],
      "metadata": {
        "id": "nxxlmZfeIaeX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences[:5]"
      ],
      "metadata": {
        "id": "l4lKxoi1JDFC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46ac546b-82ce-48a9-fddd-12963b19b119"
      },
      "execution_count": 72,
      "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": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make function to split sentences into characters\n",
        "def split_text_into_chars(text):\n",
        "  return \" \".join(list(text))\n",
        "\n",
        "#Text splitting non-character level sequence into characters\n",
        "split_text_into_chars(random_train_sentences)"
      ],
      "metadata": {
        "id": "X4v2h8UqJHXZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "903f93b5-90b7-4580-b10a-57fdd670ea68"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"f r e q u e n t   s k i n   c l e a n i n g   f u l f i l s   t h e   d e f i n i t i o n   o f   o c c u p a t i o n a l   `   w e t   w o r k   '   .\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split sequences-level data splits into character-level data splits\n",
        "train_chars=[split_text_into_chars(sentence) for sentence in train_sentences]\n",
        "val_chars=[split_text_into_chars(sentence) for sentence in val_sentences]\n",
        "test_chars=[split_text_into_chars(sentence) for sentence in test_sentences]\n",
        "train_chars[:3]"
      ],
      "metadata": {
        "id": "EMauLzbJJZAM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60354fc0-a426-41f6-f5d5-a67e31de2087"
      },
      "execution_count": 74,
      "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   .']"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Whats the average character length?\n",
        "chars_lens=[len(sentence) for sentence in train_sentences]\n",
        "mean_char_len=np.mean(chars_lens)\n",
        "mean_char_len"
      ],
      "metadata": {
        "id": "IhRwA3QfKWqj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bb274b4-e86b-47b0-950a-fc7e678da6d5"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149.3662574983337"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check the distribution of our sequences at a character-level\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(chars_lens,bins=7);"
      ],
      "metadata": {
        "id": "45sujWjaLDJT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "d2cd012b-9dfe-4771-c977-757fdb70373c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "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": [
        "# Find the character lengths covers 95% of sequences\n",
        "output_seq_char_len=int(np.percentile(chars_lens,95))\n",
        "output_seq_char_len"
      ],
      "metadata": {
        "id": "mb3lceOpLxvq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffd8f6eb-7824-41e6-908c-54351d385bc1"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "290"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all the keyboard characters\n",
        "import string\n",
        "alphabet=string.ascii_lowercase+string.digits+string.punctuation\n",
        "alphabet"
      ],
      "metadata": {
        "id": "jU84ICfkMKCy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "4d32e6dd-d205-4579-a2d3-e52a23b080fe"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'abcdefghijklmnopqrstuvwxyz0123456789!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a character-level token vectorizer instance\n",
        "NUM_CHARS_TOKENS=len(alphabet) +2 # add 2 for space and OOV token(oov-out of vocab,'[UNK]')\n",
        "char_vectorizer=TextVectorization(max_tokens=NUM_CHARS_TOKENS,\n",
        "                                  output_sequence_length=output_seq_char_len,\n",
        "                                  standardize=None,\n",
        "                                  name='char_vectorizer')"
      ],
      "metadata": {
        "id": "AfMP7d1xMtcZ"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Adapt the character vectorizer to training characters\n",
        "char_vectorizer.adapt(train_chars)"
      ],
      "metadata": {
        "id": "e8L-_Jk5NSxO"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check characters vocab stats\n",
        "char_vocab=char_vectorizer.get_vocabulary()\n",
        "print(f\"Number of characters in 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": {
        "id": "De3HMHQxP_m6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30252f96-a61b-4b45-cec4-3c70558326b5"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of characters in vocab:57\n",
            "5 Most Common Characters :['', '[UNK]', 'e', 't', 'i']\n",
            "5 Least Common Characters :['|', '\"', ']', '\\\\', '[']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out character vectorizer\n",
        "random_train_chars=random.choice(train_chars)\n",
        "print(f\"Text:\\n{random_train_chars}\\n\")\n",
        "print(f\"Length of random_train_chars:{len(random_train_chars.split())}\")\n",
        "vectorized_chars=char_vectorizer([random_train_chars])\n",
        "print(f\"Vectorized Text:\\n{vectorized_chars}\")\n",
        "print(f\"Length of Vectorized chars :{len(vectorized_chars[0])}\")"
      ],
      "metadata": {
        "id": "CvtR2TzUPHGm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "496ec318-2d53-4b3b-ff80-0a95537c9d40"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text:\n",
            "t h e   d i f f e r e n c e   w a s   s t a t i s t i c a l l y   s i g n i f i c a n t   (   m a n n - w h i t n e y   u   ;   p   <   @   )   .\n",
            "\n",
            "Length of random_train_chars:60\n",
            "Vectorized Text:\n",
            "[[ 3 13  2 10  4 17 17  2  8  2  6 11  2 21  5  9  9  3  5  3  4  9  3  4\n",
            "  11  5 12 12 20  9  4 18  6  4 17  4 11  5  6  3 28 15  5  6  6 26 21 13\n",
            "   4  3  6  2 20 16 36 14 39 19 27 25  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",
            "Length of Vectorized chars :290\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a character-level embedding"
      ],
      "metadata": {
        "id": "LM-H2ewEi8ds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create char embedding layer\n",
        "char_embed=layers.Embedding(input_dim=len(char_vocab),# number of different characters\n",
        "                            output_dim=25, # this is the size of the char embedding in the paper:pub_med_rct_200k\n",
        "                            mask_zero=True,\n",
        "                            name='char_embed')"
      ],
      "metadata": {
        "id": "vscd3oebkyCY"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test our character embedding layer\n",
        "print(f\"Charified text:\\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": {
        "id": "1X1bFJh5nfDy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8b06a22-d013-48ca-fb6b-5847e7251ad3"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Charified text:\n",
            "t h e   d i f f e r e n c e   w a s   s t a t i s t i c a l l y   s i g n i f i c a n t   (   m a n n - w h i t n e y   u   ;   p   <   @   )   .\n",
            "\n",
            "Embedded Chars. (after vectorization and embedding):\n",
            "[[[-0.0193695   0.00569115 -0.01896566 ...  0.04683322  0.03999624\n",
            "    0.00092506]\n",
            "  [ 0.01098211 -0.04070263 -0.04495462 ...  0.04696185 -0.02470088\n",
            "    0.01728198]\n",
            "  [-0.01929606 -0.01450209 -0.02752687 ... -0.03230262 -0.00741043\n",
            "   -0.04480592]\n",
            "  ...\n",
            "  [ 0.00281656 -0.01633454  0.01455985 ... -0.02126675 -0.04881934\n",
            "   -0.02277192]\n",
            "  [ 0.00281656 -0.01633454  0.01455985 ... -0.02126675 -0.04881934\n",
            "   -0.02277192]\n",
            "  [ 0.00281656 -0.01633454  0.01455985 ... -0.02126675 -0.04881934\n",
            "   -0.02277192]]]\n",
            "\n",
            "Character embedding shape:(1, 290, 25)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(random_train_chars)"
      ],
      "metadata": {
        "id": "P2NZK_O_oZwA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5850f1e8-da47-423f-a01b-01800f1453a5"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "145"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Building a Conv1D model to fit on character embeddings"
      ],
      "metadata": {
        "id": "8WY4Yl3qo98I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make Conv1D on chars only\n",
        "inputs=layers.Input(shape=(1,),dtype=tf.string)\n",
        "char_vectors=char_vectorizer(inputs)\n",
        "char_embeddings=char_embed(char_vectors)\n",
        "x=layers.Conv1D(filters=128,kernel_size=5,activation='relu',padding='same')(char_embeddings)\n",
        "x=layers.GlobalMaxPooling1D()(x)\n",
        "outputs=layers.Dense(num_classes,activation='softmax')(x)\n",
        "\n",
        "model_3=tf.keras.Model(inputs,outputs)\n",
        "\n",
        "#Compile the model\n",
        "model_3.compile(loss='categorical_crossentropy',\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "#Summary of Model\n",
        "model_3.summary()"
      ],
      "metadata": {
        "id": "NeI0bHTq666D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d2ee406-7d29-4431-974c-eb9d90f34388"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\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)           1425      \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 290, 128)          16128     \n",
            "                                                                 \n",
            " global_max_pooling1d (Glob  (None, 128)               0         \n",
            " alMaxPooling1D)                                                 \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 5)                 645       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18198 (71.09 KB)\n",
            "Trainable params: 18198 (71.09 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create char level datasets\n",
        "train_char_dataset=tf.data.Dataset.from_tensor_slices((train_chars,train_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "valid_char_dataset=tf.data.Dataset.from_tensor_slices((val_chars,val_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "test_char_dataset=tf.data.Dataset.from_tensor_slices((test_chars,test_labels_one_hot)).batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "train_char_dataset"
      ],
      "metadata": {
        "id": "qIYqstso-d-z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfee4bd2-d5c7-4832-f1e6-be0da13cd521"
      },
      "execution_count": 87,
      "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": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit the model on chars only\n",
        "model_3_history=model_3.fit(train_char_dataset,\n",
        "                            steps_per_epoch=int(0.1*len(train_char_dataset)),\n",
        "                            epochs=5,\n",
        "                            validation_data=valid_char_dataset,\n",
        "                            validation_steps=int(0.1*len(valid_char_dataset)))"
      ],
      "metadata": {
        "id": "v-rPYH5u_w7h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22f1cc9e-f147-4284-a03b-b45fd922dc2a"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "562/562 [==============================] - 10s 15ms/step - loss: 1.1242 - accuracy: 0.5518 - val_loss: 0.8997 - val_accuracy: 0.6536\n",
            "Epoch 2/5\n",
            "562/562 [==============================] - 4s 8ms/step - loss: 0.8554 - accuracy: 0.6644 - val_loss: 0.8117 - val_accuracy: 0.6825\n",
            "Epoch 3/5\n",
            "562/562 [==============================] - 3s 6ms/step - loss: 0.7865 - accuracy: 0.7004 - val_loss: 0.7485 - val_accuracy: 0.7141\n",
            "Epoch 4/5\n",
            "562/562 [==============================] - 3s 5ms/step - loss: 0.7427 - accuracy: 0.7173 - val_loss: 0.7159 - val_accuracy: 0.7227\n",
            "Epoch 5/5\n",
            "562/562 [==============================] - 4s 7ms/step - loss: 0.7238 - accuracy: 0.7213 - val_loss: 0.6934 - val_accuracy: 0.7467\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make predictions with character embeddings\n",
        "model_3_pred_probs=model_3.predict(valid_char_dataset)\n",
        "model_3_pred_probs"
      ],
      "metadata": {
        "id": "MDXTNMbfAU9E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21c440b8-da29-4751-bb8d-3978c82fe88e"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 2s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.15764539, 0.5156594 , 0.04617917, 0.27113694, 0.0093791 ],\n",
              "       [0.27971306, 0.4986292 , 0.01002904, 0.1728632 , 0.0387654 ],\n",
              "       [0.14028437, 0.25459832, 0.17430808, 0.3911971 , 0.03961211],\n",
              "       ...,\n",
              "       [0.00913505, 0.00658718, 0.0302573 , 0.01309994, 0.9409206 ],\n",
              "       [0.06830031, 0.20290802, 0.298898  , 0.03948237, 0.39041132],\n",
              "       [0.1464841 , 0.7559282 , 0.0827901 , 0.01173741, 0.00306016]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert the prediction probabilities to class labels\n",
        "model_3_pred=np.argmax(model_3_pred_probs,axis=1)\n",
        "model_3_pred"
      ],
      "metadata": {
        "id": "qsL1jZf4BJ-m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4abe938a-b9fe-4760-cbd3-325ed7d61672"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 3, ..., 4, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Experiments\n",
        "1. standardize remove(punctuation and trucate)  - 63%\n",
        "2. standardize include above punctuation aand truncation - 71%\n",
        "3. Conv1D kernel=3 with 1 same - 63%\n",
        "4. Conv1D filters=128 - 70%\n",
        "\n",
        "\n",
        "I am considering standardize to none as final conclusion for model_3 performance."
      ],
      "metadata": {
        "id": "LMLTCVYPCbNw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the results for Conv1D chars only\n",
        "model_3_results=calculate_results(y_true=val_label_encoded,\n",
        "                                  y_pred=model_3_pred)\n",
        "model_3_results"
      ],
      "metadata": {
        "id": "-Mv9GAQfBZXw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dd713b1-1185-4b84-9e68-837cf065c2a7"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 73.50059578975242,\n",
              " 'precision': 0.7415546935368772,\n",
              " 'recall': 0.7350059578975242,\n",
              " 'f1': 0.7330283413538634}"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
      ],
      "metadata": {
        "id": "fSQ6aCJxB6P4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "997d5b58-490a-41ed-d19b-b3ae0f990650"
      },
      "execution_count": 92,
      "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": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 4: Combining pretrained token embeddings + character embeddings (bybrid embedding layer)\n",
        "\n",
        "1. Create a token-level embedding model (similar `model_1`)\n",
        "2. Create a character-level model (similar to `model_3` with a slight modification)\n",
        "3. Combine 1 & 2 with a concatenate (`layers.concatenate`) layer\n",
        "4. Build a series of output layer on top of 3 similar to Figure 1 and section 4.2 of research paper of pub_med_rct_200k\n",
        "5. Construct a model which takes token and character-level sequences as input and produces sequence label probabilities as output|"
      ],
      "metadata": {
        "id": "--uGiuEpCE5_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.setup token inputs/model\n",
        "token_inputs = layers.Input(shape=[], dtype=tf.string, name=\"token_input\")\n",
        "token_embeddings = USELayer()(token_inputs)\n",
        "token_output = layers.Dense(128, activation=\"relu\")(token_embeddings)\n",
        "token_model = tf.keras.Model(inputs=token_inputs,\n",
        "                             outputs=token_output)\n",
        "\n",
        "# 2. Setup char inputs/model\n",
        "char_inputs = layers.Input(shape=(1,), dtype=tf.string, name=\"char_input\")\n",
        "char_vectors = char_vectorizer(char_inputs)\n",
        "char_embeddings = char_embed(char_vectors)\n",
        "char_bi_lstm = layers.Bidirectional(layers.LSTM(25))(char_embeddings) # bi-LSTM shown in Figure 1 of https://arxiv.org/pdf/1612.05251.pdf\n",
        "char_model = tf.keras.Model(inputs=char_inputs,\n",
        "                            outputs=char_bi_lstm)\n",
        "\n",
        "# 3. Concatenate token and char inputs (create hybrid token embedding)\n",
        "token_char_concat = layers.Concatenate(name=\"token_char_hybrid\")([token_model.output,\n",
        "                                                                  char_model.output])\n",
        "\n",
        "# 4. Create output layers - addition of dropout discussed in 4.2 of https://arxiv.org/pdf/1612.05251.pdf\n",
        "combined_dropout = layers.Dropout(0.5)(token_char_concat)\n",
        "combined_dense = layers.Dense(128, activation=\"relu\")(combined_dropout) # slightly different to Figure 1 due to different shapes of token/char embedding layers\n",
        "final_dropout = layers.Dropout(0.5)(combined_dense)\n",
        "output_layer = layers.Dense(num_classes, activation=\"softmax\")(final_dropout)\n",
        "\n",
        "# 5. Construct model with char and token inputs\n",
        "model_4 = tf.keras.Model(inputs=[token_model.input, char_model.input],\n",
        "                         outputs=output_layer,\n",
        "                         name=\"model_4_token_and_char_embeddings\")"
      ],
      "metadata": {
        "id": "VWyCr2dEQnmR"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get A summary of our model\n",
        "model_4.summary()"
      ],
      "metadata": {
        "id": "NoQBCmx1SDBk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02de8c44-f9cf-4fdb-b210-2ff076455664"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4_token_and_char_embeddings\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " char_input (InputLayer)     [(None, 1)]                  0         []                            \n",
            "                                                                                                  \n",
            " token_input (InputLayer)    [(None,)]                    0         []                            \n",
            "                                                                                                  \n",
            " char_vectorizer (TextVecto  (None, 290)                  0         ['char_input[0][0]']          \n",
            " rization)                                                                                        \n",
            "                                                                                                  \n",
            " use_layer_1 (USELayer)      (None, 512)                  0         ['token_input[0][0]']         \n",
            "                                                                                                  \n",
            " char_embed (Embedding)      (None, 290, 25)              1425      ['char_vectorizer[1][0]']     \n",
            "                                                                                                  \n",
            " dense_4 (Dense)             (None, 128)                  65664     ['use_layer_1[0][0]']         \n",
            "                                                                                                  \n",
            " bidirectional (Bidirection  (None, 50)                   10200     ['char_embed[1][0]']          \n",
            " al)                                                                                              \n",
            "                                                                                                  \n",
            " token_char_hybrid (Concate  (None, 178)                  0         ['dense_4[0][0]',             \n",
            " nate)                                                               'bidirectional[0][0]']       \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, 178)                  0         ['token_char_hybrid[0][0]']   \n",
            "                                                                                                  \n",
            " dense_5 (Dense)             (None, 128)                  22912     ['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: 100846 (393.93 KB)\n",
            "Trainable params: 100846 (393.93 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot hybrid token and character model\n",
        "from keras.utils import plot_model\n",
        "plot_model(model_4,show_shapes=True)"
      ],
      "metadata": {
        "id": "8kMKus-yrMnH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "outputId": "32aaa2c8-4a4c-4ce4-85b0-ddcf96306e2f"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile the token char\n",
        "model_4.compile(loss='categorical_crossentropy',\n",
        "                optimizer=tf.keras.optimizers.Adam(), #section 4.3 of the paper says that use SGD , you might want to try this\n",
        "                metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "PBeUPVx2rvjb"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Combining token and character data into tf.data Dataset"
      ],
      "metadata": {
        "id": "JgLzrFtrvrCk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine chars and token into a dataset\n",
        "train_char_token_data=tf.data.Dataset.from_tensor_slices((train_sentences,train_chars)) #make data\n",
        "train_char_token_labels=tf.data.Dataset.from_tensor_slices(train_labels_one_hot) # make labels\n",
        "train_char_token_dataset=tf.data.Dataset.zip((train_char_token_data,train_char_token_labels)) #Combine data with labels\n",
        "\n",
        "#Prefetch and Batch train dataset\n",
        "train_char_token_dataset=train_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "DkxlQSl-vwqX"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine chars and token into a dataset\n",
        "val_char_token_data=tf.data.Dataset.from_tensor_slices((val_sentences,val_chars)) #make data\n",
        "val_char_token_labels=tf.data.Dataset.from_tensor_slices(val_labels_one_hot) # make labels\n",
        "val_char_token_dataset=tf.data.Dataset.zip((val_char_token_data,val_char_token_labels)) #Combine data with labels\n",
        "\n",
        "#Prefetch and Batch train dataset\n",
        "val_char_token_dataset=val_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "dyuqqXUkyW1N"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out our training char and token embedding dataset\n",
        "train_char_token_dataset,val_char_token_dataset"
      ],
      "metadata": {
        "id": "voerBAKbyZoU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91774478-6a0a-4e1d-cb1b-d07057fadb99"
      },
      "execution_count": 99,
      "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": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fitting a model on token and character-level sequences"
      ],
      "metadata": {
        "id": "QQ4ODqOwyoTw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model on tokens and chars\n",
        "history_model_4=model_4.fit(train_char_token_dataset,\n",
        "                            steps_per_epoch=int(0.1*len(train_char_token_dataset)),\n",
        "                            epochs=5,\n",
        "                            validation_data=val_char_token_dataset,\n",
        "                            validation_steps=int(0.1*len(val_char_token_dataset)))"
      ],
      "metadata": {
        "id": "XGdvJlyj4cjk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa85cc7b-1f5a-4931-b531-333a406f0fbc"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "562/562 [==============================] - 48s 59ms/step - loss: 0.9699 - accuracy: 0.6153 - val_loss: 0.7524 - val_accuracy: 0.7071\n",
            "Epoch 2/5\n",
            "562/562 [==============================] - 30s 53ms/step - loss: 0.7930 - accuracy: 0.6917 - val_loss: 0.7043 - val_accuracy: 0.7364\n",
            "Epoch 3/5\n",
            "562/562 [==============================] - 21s 37ms/step - loss: 0.7737 - accuracy: 0.7043 - val_loss: 0.6845 - val_accuracy: 0.7477\n",
            "Epoch 4/5\n",
            "562/562 [==============================] - 23s 40ms/step - loss: 0.7318 - accuracy: 0.7256 - val_loss: 0.6646 - val_accuracy: 0.7540\n",
            "Epoch 5/5\n",
            "562/562 [==============================] - 21s 37ms/step - loss: 0.7344 - accuracy: 0.7221 - val_loss: 0.6472 - val_accuracy: 0.7580\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate on the whole validation dataset\n",
        "model_4.evaluate(val_char_token_dataset)"
      ],
      "metadata": {
        "id": "gviXxBas4wRq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a287e06-279b-4fe8-c9b7-12f77940f660"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 19s 21ms/step - loss: 0.6436 - accuracy: 0.7571\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6435931324958801, 0.7570501565933228]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make predictions using the token-character model hybrid\n",
        "model_4_pred_probs=model_4.predict(val_char_token_dataset)\n",
        "model_4_pred_probs"
      ],
      "metadata": {
        "id": "pdjb7aZ058Ag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cc841fc-60a3-4fd6-d273-286f7d93704a"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 19s 18ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.7545173e-01, 2.8523079e-01, 2.9484066e-03, 1.3111232e-01,\n",
              "        5.2567874e-03],\n",
              "       [4.1870835e-01, 4.7644949e-01, 4.5043598e-03, 9.6743673e-02,\n",
              "        3.5940944e-03],\n",
              "       [3.8971379e-01, 9.0613537e-02, 7.3910616e-02, 4.0985081e-01,\n",
              "        3.5911225e-02],\n",
              "       ...,\n",
              "       [2.7882927e-05, 2.2202969e-04, 9.1333613e-03, 6.7467545e-06,\n",
              "        9.9060994e-01],\n",
              "       [9.8470747e-03, 4.7789667e-02, 4.6330616e-01, 4.0343562e-03,\n",
              "        4.7502279e-01],\n",
              "       [2.9792368e-01, 4.8206800e-01, 1.5981716e-01, 2.5242716e-02,\n",
              "        3.4948394e-02]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Format pred probs into pred labels\n",
        "model_4_pred=np.argmax(model_4_pred_probs,axis=1)\n",
        "model_4_pred"
      ],
      "metadata": {
        "id": "IhRuwfOh6N6y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22f74a34-440c-4fa7-9cb1-71bd099df92f"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 3, ..., 4, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_label_encoded"
      ],
      "metadata": {
        "id": "VSNhwcD3_0eT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83e08683-a768-4539-c003-026362c1eb36"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 3, ..., 4, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4_pred"
      ],
      "metadata": {
        "id": "fzs58oyq_9A5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aabf3a0b-9e28-4274-a4f2-8bc5817a0bc5"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 3, ..., 4, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get results of token-char-hybrid model\n",
        "model_4_results=calculate_results(y_true=val_label_encoded,\n",
        "                                  y_pred=model_4_pred)\n",
        "model_4_results"
      ],
      "metadata": {
        "id": "m0UdFnup6mNn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39129e98-91f1-4eb8-ee42-d6982bc1bbd1"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 75.70501787369257,\n",
              " 'precision': 0.7552760259533422,\n",
              " 'recall': 0.7570501787369257,\n",
              " 'f1': 0.7527693432003358}"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_results"
      ],
      "metadata": {
        "id": "2tyf9wia7oWV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21dfab23-5ad3-4fa7-affc-1430e3536262"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 79.97815437574474,\n",
              " 'precision': 0.7994520094532116,\n",
              " 'recall': 0.7997815437574474,\n",
              " 'f1': 0.7967205841771905}"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 5 :Transfer Learning with pretrained token embeddings + Character embeddings + positional embeddings"
      ],
      "metadata": {
        "id": "LVzOdNqmACEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "id": "tR2jOv_hCiD-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ac9e06f1-bf85-4283-e3d1-41d00c272c09"
      },
      "execution_count": 108,
      "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",
              "0           11  \n",
              "1           11  \n",
              "2           11  \n",
              "3           11  \n",
              "4           11  "
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df"
            }
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> 🔑**Notes:** Any engineered features used to train a model need to be available at test time. In our case, line numbers and total lines are available."
      ],
      "metadata": {
        "id": "S_yCkZr8CjYa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create positional embeddings"
      ],
      "metadata": {
        "id": "Pdd8SSZvWVsn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# How many different line numbers are there?\n",
        "train_df['line_number'].value_counts()"
      ],
      "metadata": {
        "id": "ehIc7TQqWtZT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4d79c97c-e880-4131-cb24-7a32d929bc49"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "line_number\n",
              "0     15000\n",
              "1     15000\n",
              "2     15000\n",
              "3     15000\n",
              "4     14992\n",
              "5     14949\n",
              "6     14758\n",
              "7     14279\n",
              "8     13346\n",
              "9     11981\n",
              "10    10041\n",
              "11     7892\n",
              "12     5853\n",
              "13     4152\n",
              "14     2835\n",
              "15     1861\n",
              "16     1188\n",
              "17      751\n",
              "18      462\n",
              "19      286\n",
              "20      162\n",
              "21      101\n",
              "22       66\n",
              "23       33\n",
              "24       22\n",
              "25       14\n",
              "26        7\n",
              "27        4\n",
              "28        3\n",
              "29        1\n",
              "30        1\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>line_number</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>14758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>13346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>11981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the distribution of \"line_number\" column\n",
        "train_df.line_number.plot.hist()"
      ],
      "metadata": {
        "id": "Toboi8YzW60n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "e9d31d56-59dc-42a3-abe8-1af0351cd700"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "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": [
        "#Use tensorflow to create a one-hot-encoded tensors of our \"line_number\" column\n",
        "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],train_line_numbers_one_hot.shape"
      ],
      "metadata": {
        "id": "1ifgLK9ZXbZu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1206697-d7e5-49e9-956d-a5bcb9433148"
      },
      "execution_count": 111,
      "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)>,\n",
              " TensorShape([180040, 15]))"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we;ve encoded our line numbers features, lets do the same for our total line features..."
      ],
      "metadata": {
        "id": "wd43cGP59PUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#How many different numbers of lines are theere?\n",
        "train_df['total_lines'].value_counts()"
      ],
      "metadata": {
        "id": "Bfg4PWIjYQfi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 961
        },
        "outputId": "ebb3ecdf-9603-484e-df06-5ddbe88d2e05"
      },
      "execution_count": 112,
      "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"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_lines</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>24468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>23639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>22113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>19400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>18438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>12285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>10768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>7429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualise the total lines\n",
        "train_df['total_lines'].plot.hist()"
      ],
      "metadata": {
        "id": "tWfJMntI9veb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "073cc0ae-78a8-4b92-9a41-fd43251dd724"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 113
        },
        {
          "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": [
        "# Check the coverage of a 'total_line' value of 20\n",
        "np.percentile(train_df['total_lines'],98)"
      ],
      "metadata": {
        "id": "bdrFJFol95VC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6f80e5d-3773-450c-c016-f86d339dfd06"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.0"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#USe Tensorflow to create one_hot_encoded tensors of our 'total_lines' features\n",
        "train_total_lines_one_hot=tf.one_hot(train_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",
        "val_total_lines_one_hot=tf.one_hot(val_df['total_lines'].to_numpy(),depth=20)\n",
        "train_total_lines_one_hot.shape,train_total_lines_one_hot[:10]"
      ],
      "metadata": {
        "id": "DbmbBN9X-eFS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ce0d7c7-31b0-48c6-fa16-089db3f73ad8"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([180040, 20]),\n",
              " <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": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Building a tribrid embedding model\n",
        "\n",
        "1. Creating a token-level model\n",
        "2. Create a character-level model\n",
        "3. Create a model for the `line_number` feature\n",
        "4. Create a model for the `total_lines` feature\n",
        "5. Combine the outputs of 1 & 2 using `tf.keras.layers.Concatenate`\n",
        "6. Combine the outputs of 3 ,4 & 5 using `tf.keras.layers.Concatenate`\n",
        "7. Create an output layer to accept the tribrid embedding and output label probabilities\n",
        "8. Combine the inputs of the 1,2,3,4 and outputs of the into a `tf.keras.Model`"
      ],
      "metadata": {
        "id": "3Rediv32_Srg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Token inputs\n",
        "token_inputs=layers.Input(shape=[],dtype=tf.string,name='token_inputs')\n",
        "token_embeddings=USELayer()(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=tf.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(24))(char_embeddings)\n",
        "char_model=tf.keras.Model(inputs=char_inputs,\n",
        "                          outputs=char_bi_lstm)\n",
        "\n",
        "# 3. Line numbers model\n",
        "# inputs,shape=?\n",
        "line_number_inputs=layers.Input(shape=[15,],dtype=tf.float32,name='line_number_inputs')\n",
        "#Dense layer with 32 units and relu activation\n",
        "line_number_dense=layers.Dense(32,activation='relu')(line_number_inputs)\n",
        "#Combine inputs and dense layer into model\n",
        "line_number_model=tf.keras.Model(inputs=line_number_inputs,\n",
        "                                 outputs=line_number_dense)\n",
        "\n",
        "# 4. total line numbers\n",
        "total_lines_inputs=layers.Input(shape=[20,],dtype=tf.float32,)\n",
        "total_line_dense=layers.Dense(32,activation='relu')(total_lines_inputs)\n",
        "total_lines_model=tf.keras.Model(inputs=total_lines_inputs,\n",
        "                                 outputs=total_line_dense)\n",
        "\n",
        "# 5. Combine the token and char embeddings into a hybrid embeddings\n",
        "combined_embeddings=layers.Concatenate(name='char_token_hybrid_embeddings')([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 the positional embeddings with combined token and char embeddings\n",
        "tribrid_embeddings=layers.Concatenate(name='char_token_postional_embeddings')([line_number_model.output,\n",
        "                                                                               total_lines_model.output,\n",
        "                                                                               z])\n",
        "\n",
        "# 7. Create output layer\n",
        "output_layer=layers.Dense(num_classes,activation='softmax')(tribrid_embeddings)\n",
        "\n",
        "# 8. Put together model with all kinds of inputs\n",
        "model_5=tf.keras.Model(inputs=[line_number_model.input,\n",
        "                               total_lines_model.input,\n",
        "                               token_model.input,\n",
        "                               char_model.input],\n",
        "                       outputs=output_layer,name='model_5_trbrid_embedding_model')"
      ],
      "metadata": {
        "id": "u6vp8KWpBmOu"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Get a summary of tribrid embedding model\n",
        "model_5.summary()"
      ],
      "metadata": {
        "id": "rtXfz6mILCcy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45450fa0-8490-4d68-8848-66027e9abeff"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5_trbrid_embedding_model\"\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_layer_2 (USELayer)      (None, 512)                  0         ['token_inputs[0][0]']        \n",
            "                                                                                                  \n",
            " char_embed (Embedding)      (None, 290, 25)              1425      ['char_vectorizer[2][0]']     \n",
            "                                                                                                  \n",
            " dense_7 (Dense)             (None, 128)                  65664     ['use_layer_2[0][0]']         \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirecti  (None, 48)                   9600      ['char_embed[2][0]']          \n",
            " onal)                                                                                            \n",
            "                                                                                                  \n",
            " char_token_hybrid_embeddin  (None, 176)                  0         ['dense_7[0][0]',             \n",
            " gs (Concatenate)                                                    'bidirectional_1[0][0]']     \n",
            "                                                                                                  \n",
            " line_number_inputs (InputL  [(None, 15)]                 0         []                            \n",
            " ayer)                                                                                            \n",
            "                                                                                                  \n",
            " input_4 (InputLayer)        [(None, 20)]                 0         []                            \n",
            "                                                                                                  \n",
            " dense_10 (Dense)            (None, 256)                  45312     ['char_token_hybrid_embeddings\n",
            "                                                                    [0][0]']                      \n",
            "                                                                                                  \n",
            " dense_8 (Dense)             (None, 32)                   512       ['line_number_inputs[0][0]']  \n",
            "                                                                                                  \n",
            " dense_9 (Dense)             (None, 32)                   672       ['input_4[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_2 (Dropout)         (None, 256)                  0         ['dense_10[0][0]']            \n",
            "                                                                                                  \n",
            " char_token_postional_embed  (None, 320)                  0         ['dense_8[0][0]',             \n",
            " dings (Concatenate)                                                 'dense_9[0][0]',             \n",
            "                                                                     'dropout_2[0][0]']           \n",
            "                                                                                                  \n",
            " dense_11 (Dense)            (None, 5)                    1605      ['char_token_postional_embeddi\n",
            "                                                                    ngs[0][0]']                   \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 124790 (487.46 KB)\n",
            "Trainable params: 124790 (487.46 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the model_5 to visually...\n",
        "from keras.utils import plot_model\n",
        "plot_model(model_5,show_shapes=True)"
      ],
      "metadata": {
        "id": "QxIsPh-PLtv9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "c131e5bf-e986-4672-c777-10c8cef754c4"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is Label_Smoothing?\n",
        "\n",
        "Ans. For example if our model gets too confident on single class.\n",
        "(e.g. its prediction probability is really high) , it may be stuck on that class and not consider other classes...\n",
        "\n",
        "Really Confident : `[0.0,0.0,1.0,0.0,0.1]`\n",
        "\n",
        "What label smoothing does is it assigns some of the value from the highest pred prob to other classes, in turn hopefully proving generalization: `[0.01,0.01,0.96,0.01,0.01]`"
      ],
      "metadata": {
        "id": "cjMmplKQNb9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile token, char, and positional embedding model\n",
        "model_5.compile(loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.2), #Helps to prevent overfitting\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "TOgH7Ns8MPbl"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create tribrid embedding datasets using tf.data"
      ],
      "metadata": {
        "id": "PLeZBz-cPW3H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create training and validation dataset(with all 4 kinds of input data)\n",
        "train_char_token_pos_dataset=tf.data.Dataset.from_tensor_slices((train_line_numbers_one_hot,\n",
        "                                                                train_total_lines_one_hot,\n",
        "                                                                train_sentences,\n",
        "                                                                train_chars))\n",
        "train_char_token_pos_labels=tf.data.Dataset.from_tensor_slices(train_labels_one_hot)\n",
        "train_char_token_pos_dataset=tf.data.Dataset.zip((train_char_token_pos_dataset,\n",
        "                                                  train_char_token_pos_labels))\n",
        "train_char_token_pos_dataset=train_char_token_pos_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "#Do the same for the validation dataset\n",
        "val_char_token_pos_dataset=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_char_token_pos_labels=tf.data.Dataset.from_tensor_slices(val_labels_one_hot)\n",
        "val_char_token_pos_dataset=tf.data.Dataset.zip((val_char_token_pos_dataset,\n",
        "                                                  val_char_token_pos_labels))\n",
        "val_char_token_pos_dataset=val_char_token_pos_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "Gcm8BhhzQKDZ"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check input shapes\n",
        "train_char_token_pos_dataset,val_char_token_pos_dataset"
      ],
      "metadata": {
        "id": "iQzzNKY7SXQ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d646bc20-e2aa-4743-b711-fc76094b7c0e"
      },
      "execution_count": 121,
      "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": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fitting, evaluating and making predictions with our tribrid model"
      ],
      "metadata": {
        "id": "5Xdro-6fS9uL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.fit(train_char_token_pos_dataset,\n",
        "            steps_per_epoch=int(0.1*len(train_char_token_pos_dataset)),\n",
        "            epochs=5,\n",
        "            validation_data=val_char_token_pos_dataset,\n",
        "            validation_steps=int(0.1*len(val_char_token_pos_dataset)))"
      ],
      "metadata": {
        "id": "G_VMTZvHTQko",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72cf9f10-dd65-4e50-e639-6a14e6c9d874"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "562/562 [==============================] - 51s 61ms/step - loss: 1.0898 - accuracy: 0.7328 - val_loss: 0.9771 - val_accuracy: 0.8105\n",
            "Epoch 2/5\n",
            "562/562 [==============================] - 21s 38ms/step - loss: 0.9636 - accuracy: 0.8190 - val_loss: 0.9469 - val_accuracy: 0.8281\n",
            "Epoch 3/5\n",
            "562/562 [==============================] - 23s 40ms/step - loss: 0.9439 - accuracy: 0.8287 - val_loss: 0.9324 - val_accuracy: 0.8348\n",
            "Epoch 4/5\n",
            "562/562 [==============================] - 23s 41ms/step - loss: 0.9363 - accuracy: 0.8395 - val_loss: 0.9263 - val_accuracy: 0.8374\n",
            "Epoch 5/5\n",
            "562/562 [==============================] - 27s 47ms/step - loss: 0.9336 - accuracy: 0.8391 - val_loss: 0.9175 - val_accuracy: 0.8467\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7ab646f6bf10>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.evaluate(val_char_token_pos_dataset)"
      ],
      "metadata": {
        "id": "F3DhR7q6TnAY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0678d773-cb45-41a6-bfc9-4013bbe72c0e"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 24s 26ms/step - loss: 0.9156 - accuracy: 0.8484\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9156453013420105, 0.8483715057373047]"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make prediction with char token pos model\n",
        "model_5_pred_probs=model_5.predict(val_char_token_pos_dataset)\n",
        "print(model_5_pred_probs[:10])\n",
        "model_5_pred=tf.argmax(model_5_pred_probs,axis=1)\n",
        "print(model_5_pred[:10])"
      ],
      "metadata": {
        "id": "PNWpVL6LZubX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1be5cc10-c902-4b51-8182-f76e0090942c"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 26s 23ms/step\n",
            "[[0.61814404 0.08640334 0.01502548 0.2573645  0.02306263]\n",
            " [0.6430619  0.08668339 0.06523234 0.19227107 0.01275133]\n",
            " [0.39280328 0.10609886 0.12051621 0.33037058 0.05021115]\n",
            " [0.02838551 0.05713917 0.82211894 0.0263964  0.06596004]\n",
            " [0.04135244 0.04516194 0.7896156  0.03325615 0.09061384]\n",
            " [0.00982199 0.06718259 0.46512762 0.01664381 0.441224  ]\n",
            " [0.01547739 0.07838588 0.3422028  0.02486704 0.5390669 ]\n",
            " [0.01239983 0.13736357 0.25470844 0.01896067 0.57656753]\n",
            " [0.01995004 0.0564594  0.04327761 0.02915563 0.8511573 ]\n",
            " [0.04031562 0.7229217  0.03651143 0.02921276 0.17103851]]\n",
            "tf.Tensor([0 0 0 2 2 2 4 4 4 1], shape=(10,), dtype=int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5_results=calculate_results(y_pred=model_5_pred,\n",
        "                                  y_true=tf.convert_to_tensor(val_label_encoded))\n",
        "model_5_results"
      ],
      "metadata": {
        "id": "WRqxqdOnafqf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "842ac74d-be51-4af9-d365-0750b24ab7c4"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 84.83715080100622,\n",
              " 'precision': 0.8526174949664923,\n",
              " 'recall': 0.8483715080100622,\n",
              " 'f1': 0.8442588169539788}"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compare model results"
      ],
      "metadata": {
        "id": "64YQR8HdbKU0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine model results into dataframe\n",
        "all_model_results=pd.DataFrame({\"model_0_baseline\":baseline_results,\n",
        "                                \"model_1_custom_token_embedding\":model_1_results,\n",
        "                                \"model_2_pretrained_token_embedding\":model_2_results,\n",
        "                                \"model_3_custom_char_embedding\":model_3_results,\n",
        "                                \"model_4_hybrid_char_token_embedding\":model_4_results,\n",
        "                                \"model_5_char_token_pos_embedding\":model_5_results})\n",
        "all_model_results=all_model_results.transpose()\n",
        "all_model_results"
      ],
      "metadata": {
        "id": "ex4JWYdfb6bR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "7375ab2f-537f-4580-aa12-8b568b7e1681"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      accuracy  precision    recall        f1\n",
              "model_0_baseline                     72.183238   0.718647  0.721832  0.698925\n",
              "model_1_custom_token_embedding       79.978154   0.799452  0.799782  0.796721\n",
              "model_2_pretrained_token_embedding   73.434397   0.730920  0.734344  0.728760\n",
              "model_3_custom_char_embedding        73.500596   0.741555  0.735006  0.733028\n",
              "model_4_hybrid_char_token_embedding  75.705018   0.755276  0.757050  0.752769\n",
              "model_5_char_token_pos_embedding     84.837151   0.852617  0.848372  0.844259"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
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              "      <th>model_0_baseline</th>\n",
              "      <td>72.183238</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "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\": 4.882590653403855,\n        \"min\": 72.1832384482987,\n        \"max\": 84.83715080100622,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          72.1832384482987,\n          79.97815437574474,\n          84.83715080100622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05060861851212824,\n        \"min\": 0.7186466952323352,\n        \"max\": 0.8526174949664923,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7186466952323352,\n          0.7994520094532116,\n          0.8526174949664923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04882590653403857,\n        \"min\": 0.7218323844829869,\n        \"max\": 0.8483715080100622,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7218323844829869,\n          0.7997815437574474,\n          0.8483715080100622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.052780067814658466,\n        \"min\": 0.6989250353450294,\n        \"max\": 0.8442588169539788,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6989250353450294,\n          0.7967205841771905,\n          0.8442588169539788\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #Reduce the accuracy to same as other metrics\n",
        " all_model_results['accuracy']=all_model_results['accuracy']/100\n",
        " all_model_results"
      ],
      "metadata": {
        "id": "NtlbseO2cya8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "65ac00a0-3f5d-4426-d52f-fec126251a95"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     accuracy  precision    recall        f1\n",
              "model_0_baseline                     0.721832   0.718647  0.721832  0.698925\n",
              "model_1_custom_token_embedding       0.799782   0.799452  0.799782  0.796721\n",
              "model_2_pretrained_token_embedding   0.734344   0.730920  0.734344  0.728760\n",
              "model_3_custom_char_embedding        0.735006   0.741555  0.735006  0.733028\n",
              "model_4_hybrid_char_token_embedding  0.757050   0.755276  0.757050  0.752769\n",
              "model_5_char_token_pos_embedding     0.848372   0.852617  0.848372  0.844259"
            ],
            "text/html": [
              "\n",
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              "  <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>model_0_baseline</th>\n",
              "      <td>0.721832</td>\n",
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              "      <td>0.698925</td>\n",
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              "      <th>model_1_custom_token_embedding</th>\n",
              "      <td>0.799782</td>\n",
              "      <td>0.799452</td>\n",
              "      <td>0.799782</td>\n",
              "      <td>0.796721</td>\n",
              "    </tr>\n",
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              "      <th>model_2_pretrained_token_embedding</th>\n",
              "      <td>0.734344</td>\n",
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              "      <th>model_3_custom_char_embedding</th>\n",
              "      <td>0.735006</td>\n",
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              "      <td>0.757050</td>\n",
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              "    </tr>\n",
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              "      <th>model_5_char_token_pos_embedding</th>\n",
              "      <td>0.848372</td>\n",
              "      <td>0.852617</td>\n",
              "      <td>0.848372</td>\n",
              "      <td>0.844259</td>\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "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\": 0.04882590653403857,\n        \"min\": 0.7218323844829869,\n        \"max\": 0.8483715080100622,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7218323844829869,\n          0.7997815437574474,\n          0.8483715080100622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05060861851212824,\n        \"min\": 0.7186466952323352,\n        \"max\": 0.8526174949664923,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7186466952323352,\n          0.7994520094532116,\n          0.8526174949664923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04882590653403857,\n        \"min\": 0.7218323844829869,\n        \"max\": 0.8483715080100622,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7218323844829869,\n          0.7997815437574474,\n          0.8483715080100622\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.052780067814658466,\n        \"min\": 0.6989250353450294,\n        \"max\": 0.8442588169539788,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6989250353450294,\n          0.7967205841771905,\n          0.8442588169539788\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot and compare all model results\n",
        "all_model_results.plot(kind='bar',figsize=(10,7)).legend(bbox_to_anchor=(1.0,1.0));"
      ],
      "metadata": {
        "id": "zNCkEAoXdfEk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "outputId": "0babe5a2-fac4-4e30-e0db-7f3c4a301b45"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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rytMR2YyMDG3dulWRkZH/fQE3N0VGRmrDhg25rnP//fdr69atttOPjxw5omXLlqlly5Y3fZ/09HRdvnzZ7gEAAAAAriAjI8PoCE4vT0dkz549q6ysLAUGBtqNBwYGat++fbmu06lTJ509e1YPPPCArFarMjMz9dJLL93y1OLY2FiNGjUqL9GAfBc6dOkdrf+bd6c7Wj+8Qrk7Wl/i00EAAABn1LRpU9WqVUuFChXS559/rvDwcH344Yd65ZVX9PPPP6tIkSJ69NFHNWHCBAUEBEiSsrOz9cEHH2jGjBk6fvy4AgMD9eKLL+r111+XJA0ZMkTffPONTpw4oaCgIHXu3FkjRoyQh4eHkT/qXZPvsxYnJCRo9OjR+uijj5SYmKivv/5aS5cu1dtvv33TdYYNG6ZLly7ZHsePH8/vmAAAAABgmLlz58rT01Pr1q3TmDFj1KxZM9WrV0+//PKLli9frtOnT+uZZ56xLT9s2DCNGTNGw4cP1549ezRv3jy7A46+vr6Ki4vTnj17NGnSJM2cOVMTJkww4kfLF3k6IhsQECB3d3edPn3abvz06dMKCgrKdZ3hw4erS5cu6tmzpyQpPDxcqampeuGFF/T666/LzS1nl/by8pKXl1deogG4DXurVb+j9avv23uXkgAAAOCvKleurLFjx0qS3nnnHdWrV0+jR4+2PT9nzhyFhITowIEDKlOmjCZNmqQpU6aoW7dukqSKFSvqgQcesC3/xhtv2L4ODQ3V4MGDNX/+fL366qsO+onyV56KrKenpxo0aKD4+HjbjFbZ2dmKj49XdHR0ruukpaXlKKvu7u6SJKvV+g8iAwAAAEDB0qBBA9vXO3bs0KpVq1S0aNEcyx0+fFgXL15Uenq6mjdvftPXW7BggSZPnqzDhw/r6tWryszMlJ+fX75kN0KeZy2OiYlRt27dFBERoYYNG2rixIlKTU21zWLctWtXBQcHKzY2VpLUunVrjR8/XvXq1VOjRo106NAhDR8+XK1bt7YVWgAAAABwZUWKFLF9ffXqVbVu3VrvvfdejuXKlCmjI0eO3PK1NmzYoM6dO2vUqFFq0aKF/P39NX/+fI0bN+6u5zZKnotshw4dlJKSohEjRig5OVl169bV8uXLbedjJyUl2R2BfeONN2SxWPTGG2/o5MmTKlWqlFq3bq1333337v0UAAAAAFBA1K9fX4sWLVJoaKgKFcpZ2SpXrqzChQsrPj7edgnnX61fv17ly5e3TfwkSceOHcvXzI6W5yIrSdHR0Tc9lTghIcH+DQoV0siRIzVy5Mh/8lYAAAAA4FL69u2rmTNnqmPHjnr11VdVokQJHTp0SPPnz9esWbPk7e2tIUOG6NVXX5Wnp6eaNGmilJQU/frrr+rRo4cqV66spKQkzZ8/X/fee6+WLl2qb775xugf667K91mLAQAAAAC3r2zZslq3bp2ysrL06KOPKjw8XC+//LKKFStmO/t1+PDhGjRokEaMGKHq1aurQ4cOOnPmjCSpTZs2GjhwoKKjo1W3bl2tX79ew4cPN/JHuussVhPMuHT58mX5+/vr0qVLBeoCZZhLQbiP7JexmXe0PrMWAwBg/r8JzP73wK26wbVr13T06FFVqFBB3t7eBiXEnbjd/4YckQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAAAMZLVa9cILL6hEiRKyWCzavn270ZGcXiGjAwAAAABAftpbrbpD36/6vr15Wn758uWKi4tTQkKCwsLCdODAAbVu3Vpbt27VqVOn9M0336ht27b5E9akOCILAAAAAAY6fPiwypQpo/vvv19BQUFKTU1VnTp1NHXqVKOjOS2OyAIAAACAQbp37665c+dKkiwWi8qXL6/ffvtNjz/+uMHJnBtFFgAAAAAMMmnSJFWsWFEzZszQli1b5O7ubnQkU6DIAgAAAIBB/P395evrK3d3dwUFBRkdxzS4RhYAAAAAYCoUWQAAAACAqXBqMQAAQB6EDl16R+v/NqbVHa0fPjf8jtbf1W3XHa0PAM6AIgsAAOBIb/rf2foVyt2dHACc1tWrV3Xo0CHb90ePHtX27dtVokQJlSvH7wCJIgsAAOBS9larfkfrV9+39y4lAXAzv/zyix555BHb9zExMZKkbt26KS4uzqBUzoUiCwAAAKBAc/YPYF5++WW9/PLLtu+bNm0qq9VqXCATYLInAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpMGsxAACOdKf3EH3z0t3JAQAFGDP+mtft/rfjiCwAAACAAsHd3V2SlJGRYXAS/FNpaWmSJA8Pj1suxxFZAAAAAAVCoUKF5OPjo5SUFHl4eMjNjeN2ZmG1WpWWlqYzZ86oWLFitg8lboYiCwAAAKBAsFgsKlOmjI4ePapjx44ZHQf/QLFixRQUFPS3y1FkAQDIg9ChS+9o/d+87+z9w+eG39H6u7rturMAAODkPD09VblyZU4vNiEPD4+/PRL7J4osANymOy4wY1rdcQZKDO7U3mrV72j96vv23qUkAJB/3Nzc5O19h58cwqlx0jgAAAAAwFQ4IgsAjnKnt12RpArl7vw1AAAATI4iCwAuhNNKAQBAQcCpxQAAAAAAU6HIAgAAAABMhSILAAAAADAVrpG9TUbfdoNbbgAAAADADRyRBQAAAACYCkdkHeVOb7vBLTcAAAAAQBJHZAEAAAAAJkORBQAAAACYCkUWAAAAAGAqFFkAAAAAgKlQZAEAAAAApkKRBQAAAACYyj8qslOnTlVoaKi8vb3VqFEjbd68+abLNm3aVBaLJcejVatW/zg0AAAAAMB15bnILliwQDExMRo5cqQSExNVp04dtWjRQmfOnMl1+a+//lqnTp2yPXbv3i13d3c9/fTTdxweAAAAAOB68lxkx48fr169eikqKko1atTQ9OnT5ePjozlz5uS6fIkSJRQUFGR7/PDDD/Lx8aHIAgAAAAD+kTwV2YyMDG3dulWRkZH/fQE3N0VGRmrDhg239RqzZ8/Ws88+qyJFitx0mfT0dF2+fNnuAQAAAACAlMcie/bsWWVlZSkwMNBuPDAwUMnJyX+7/ubNm7V792717NnzlsvFxsbK39/f9ggJCclLTAAAAABAAVbIkW82e/ZshYeHq2HDhrdcbtiwYYqJibF9f/nyZcrsHdpbrfodv0b1fXvvQhIAAAAAuDN5KrIBAQFyd3fX6dOn7cZPnz6toKCgW66bmpqq+fPn66233vrb9/Hy8pKXl1deogEAAAAAXESeTi329PRUgwYNFB8fbxvLzs5WfHy8GjdufMt1v/rqK6Wnp+u55577Z0kBAAAAANA/OLU4JiZG3bp1U0REhBo2bKiJEycqNTVVUVFRkqSuXbsqODhYsbGxduvNnj1bbdu2VcmSJe9OcgAAAACAS8pzke3QoYNSUlI0YsQIJScnq27dulq+fLltAqikpCS5udkf6N2/f7/Wrl2rlStX3p3UAAAAAACX9Y8me4qOjlZ0dHSuzyUkJOQYq1q1qqxW6z95KwAAAAAA7OTpGlkAAAAAAIxGkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKZCkQUAAAAAmApFFgAAAABgKhRZAAAAAICpUGQBAAAAAKbyj4rs1KlTFRoaKm9vbzVq1EibN2++5fIXL15U3759VaZMGXl5ealKlSpatmzZPwoMAAAAAHBthfK6woIFCxQTE6Pp06erUaNGmjhxolq0aKH9+/erdOnSOZbPyMjQv/71L5UuXVoLFy5UcHCwjh07pmLFit2N/AAAAAAAF5PnIjt+/Hj16tVLUVFRkqTp06dr6dKlmjNnjoYOHZpj+Tlz5uj8+fNav369PDw8JEmhoaF3lhoAAAAA4LLydGpxRkaGtm7dqsjIyP++gJubIiMjtWHDhlzXWbJkiRo3bqy+ffsqMDBQtWrV0ujRo5WVlXXT90lPT9fly5ftHgAAAAAASHkssmfPnlVWVpYCAwPtxgMDA5WcnJzrOkeOHNHChQuVlZWlZcuWafjw4Ro3bpzeeeedm75PbGys/P39bY+QkJC8xAQAAAAAFGD5Pmtxdna2SpcurRkzZqhBgwbq0KGDXn/9dU2fPv2m6wwbNkyXLl2yPY4fP57fMQEAAAAAJpGna2QDAgLk7u6u06dP242fPn1aQUFBua5TpkwZeXh4yN3d3TZWvXp1JScnKyMjQ56enjnW8fLykpeXV16iAQAAAABcRJ6OyHp6eqpBgwaKj4+3jWVnZys+Pl6NGzfOdZ0mTZro0KFDys7Oto0dOHBAZcqUybXEAgAAAABwK3k+tTgmJkYzZ87U3LlztXfvXvXu3Vupqam2WYy7du2qYcOG2Zbv3bu3zp8/rwEDBujAgQNaunSpRo8erb59+969nwIAAAAA4DLyfPudDh06KCUlRSNGjFBycrLq1q2r5cuX2yaASkpKkpvbf/txSEiIVqxYoYEDB6p27doKDg7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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort models results by f1-score\n",
        "all_model_results.sort_values('f1',ascending=True)['f1'].plot(kind='bar',figsize=(10,7));"
      ],
      "metadata": {
        "id": "MlZKBT8rdqng",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "outputId": "f8e01cd7-60d1-41ad-b9da-8b4e9fe59650"
      },
      "execution_count": 129,
      "outputs": [
        {
          "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": "11SUNV-xewhS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_5.save('skimlit_tribrid_model.keras')"
      ],
      "metadata": {
        "id": "OIiXCViifKpK"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and load in pretrained model from google storage"
      ],
      "metadata": {
        "id": "c8T_7lVEh3C7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/skimlit/skimlit_tribrid_model.zip\n",
        "!mkdir skimlit_gs_model\n",
        "!unzip skimlit_tribrid_model.zip -d skimlit_gs_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "md0w91X_OS_V",
        "outputId": "38f30bf9-686c-4634-ccaa-578408132ab8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-12-31 10:39:02--  https://storage.googleapis.com/ztm_tf_course/skimlit/skimlit_tribrid_model.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.98.207, 74.125.197.207, 74.125.135.207, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.98.207|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 962182847 (918M) [application/zip]\n",
            "Saving to: ‘skimlit_tribrid_model.zip.1’\n",
            "\n",
            "skimlit_tribrid_mod 100%[===================>] 917.61M   139MB/s    in 7.8s    \n",
            "\n",
            "2024-12-31 10:39:10 (118 MB/s) - ‘skimlit_tribrid_model.zip.1’ saved [962182847/962182847]\n",
            "\n",
            "mkdir: cannot create directory ‘skimlit_gs_model’: File exists\n",
            "Archive:  skimlit_tribrid_model.zip\n",
            "replace skimlit_gs_model/skimlit_tribrid_model/keras_metadata.pb? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: skimlit_gs_model/skimlit_tribrid_model/keras_metadata.pb  \n",
            "replace skimlit_gs_model/skimlit_tribrid_model/fingerprint.pb? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            " extracting: skimlit_gs_model/skimlit_tribrid_model/fingerprint.pb  \n",
            "replace skimlit_gs_model/skimlit_tribrid_model/variables/variables.index? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: skimlit_gs_model/skimlit_tribrid_model/variables/variables.index  \n",
            "replace skimlit_gs_model/skimlit_tribrid_model/variables/variables.data-00000-of-00001? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: skimlit_gs_model/skimlit_tribrid_model/variables/variables.data-00000-of-00001  y\n",
            "y\n",
            "y\n",
            "y\n",
            "y\n",
            "y\n",
            "y\n",
            "y\n",
            "y\n",
            "y\n",
            "\n",
            "replace skimlit_gs_model/skimlit_tribrid_model/saved_model.pb? [y]es, [n]o, [A]ll, [N]one, [r]ename:   inflating: skimlit_gs_model/skimlit_tribrid_model/saved_model.pb  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Load in downloaded Google storage\n",
        "loaded_gs_model=tf.keras.models.load_model('skimlit_gs_model/skimlit_tribrid_model')\n",
        "loaded_gs_model"
      ],
      "metadata": {
        "id": "fHBvo5D1PPtk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32054a4f-454e-4848-9cb8-4bb0d3da998d"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.engine.functional.Functional at 0x7ab77dd97370>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate the loaded Google storage model\n",
        "loaded_gs_model.evaluate(val_char_token_pos_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpYesXiMT2ae",
        "outputId": "50d25426-8cab-4351-c551-f00b5daf1c81"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 19s 19ms/step - loss: 0.9388 - accuracy: 0.8303\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9387755990028381, 0.830332338809967]"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Your Challenges\n",
        "\n",
        "1. Test data samples into tf.data Dataset (fast loading) and then evaluate (make predictions) the best performing model on the tests samples.\n",
        "2. Find the wrong predictions from 1 (these are the samples where the model has predictted wrong label with the highest prediction probability).\n",
        "3. Make Example prediction (on RCT abstracts from the wild),\n",
        "you can go to PubMed to find these: https://pubmed.ncbi.nlm.nih.gov/?term=metabolism+rct , find and use our model to predict on 3-4 different abstracts from the wild.\n",
        "  * Some examples: mrdburke github linksss.\n",
        "4. See the exercises and extra curriculum section on GitHub to further test your skills (for section 09) .\n",
        "\n"
      ],
      "metadata": {
        "id": "sdh-VxsnUAG0"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "55ZAmeMQUunx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}