09_SkimLit_nlp_milestone_project_2_video.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "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/fyemane/TensorFlow-ZTM-/blob/main/09_SkimLit_nlp_milestone_project_2_video.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 of this notebook is to build an 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 paper above, we see that the model architecture that they use to achieve their best results is available here: https://arxiv.org/abs/1612.05251\n",
        "\n",
        "**Resource:** If you want to find the ground truth for this notebook (with lots of diagrams and text annotations), see the GitHub: https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/09_SkimLit_nlp_milestone_project_2.ipynb\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "cKRYlG0MnL1l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setting up our notebook for Milestone Project 2 (getting the data)"
      ],
      "metadata": {
        "id": "YMe8avpinSB_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Confirm access to a GPU"
      ],
      "metadata": {
        "id": "wsZhFpbBoYhn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi -L"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7DpP5bZoaG3",
        "outputId": "e0b7a8ea-b130-4f62-ef94-f6b30b65f01b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-17bb4290-0006-ed9e-aff1-d475897ce75c)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Get data\n",
        "\n",
        "Since we'll be replicating the paper above (PubMed 200k RCT), let's download the dataset they used.\n",
        "\n",
        "We can do so from the authors GitHub: https://github.com/Franck-Dernoncourt/pubmed-rct"
      ],
      "metadata": {
        "id": "G8xSOIpFobTj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/Franck-Dernoncourt/pubmed-rct\n",
        "!ls pubmed-rct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIzseN1Ro7Bm",
        "outputId": "4ff6d85c-9ad7-425a-ffb1-d03f5042efdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'pubmed-rct'...\n",
            "remote: Enumerating objects: 33, done.\u001b[K\n",
            "remote: Counting objects: 100% (8/8), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 33 (delta 5), reused 5 (delta 5), pack-reused 25\u001b[K\n",
            "Unpacking objects: 100% (33/33), 177.08 MiB | 6.35 MiB/s, done.\n",
            "PubMed_200k_RCT\n",
            "PubMed_200k_RCT_numbers_replaced_with_at_sign\n",
            "PubMed_20k_RCT\n",
            "PubMed_20k_RCT_numbers_replaced_with_at_sign\n",
            "README.md\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/\n",
        "!ls pubmed-rct/PubMed_20k_RCT/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mfFMKAnto-01",
        "outputId": "c9c7a003-3fd8-46c1-d768-112fdae1cd5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dev.txt  test.txt  train.txt\n",
            "dev.txt  test.txt  train.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # Start our experiments using the 20k dataset with numbers replaced by \"@\" sign\n",
        "data_dir = \"/content/pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/\""
      ],
      "metadata": {
        "id": "Bjlb7jYKp7oK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check all of the filenames in the target directory\n",
        "import os\n",
        "filenames = [data_dir + filename for filename in os.listdir(data_dir)]\n",
        "filenames"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppPyvtfsxq2_",
        "outputId": "b570f2ab-1a55-4a6b-8f52-a603824ef498"
      },
      "execution_count": null,
      "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/train.txt',\n",
              " '/content/pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign/test.txt']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualising examples from the dataset (becoming one with the data)\n",
        "\n",
        "Now we've got some text data, it's time to become one with it.\n",
        "\n",
        "One of the best ways to become one with the data is to...\n",
        "\n",
        "> Visualize, visualize, visualize\n",
        "\n",
        "So with that in mind, let's write a function to read in all of the lines of a target text file.\n",
        "\n",
        "https://realpython.com/read-write-files-python/"
      ],
      "metadata": {
        "id": "d9nJl52Dxxun"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create function to read lines of a document\n",
        "def get_lines(filename):\n",
        "  \"\"\"\n",
        "  Reads filename (a text filename) and returns the lines of text as a list.\n",
        "\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 filename\n",
        "  \"\"\"\n",
        "  # https://www.w3schools.com/python/ref_file_readlines.asp\n",
        "  with open(filename, \"r\") as f:\n",
        "    return f.readlines()"
      ],
      "metadata": {
        "id": "kh915xgYzwsM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's read in the training lines\n",
        "train_lines = get_lines(data_dir + \"train.txt\") # read the lines within the training file\n",
        "train_lines[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Byfu7Boa0uZn",
        "outputId": "b38bf323-d57d-4a1f-bc32-77ffb07b24e4"
      },
      "execution_count": null,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_lines)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNRJfMov050-",
        "outputId": "cedaf602-6a4b-4333-c6e7-cccec1c0cf74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "210040"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's think about how we want our data to look...\n",
        "\n",
        "How I think our data would be best represented...\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",
        "```\n",
        "\n",
        "Let's write a function which turns each of our datasets into the above format so we can continue to prepare our data for modelling."
      ],
      "metadata": {
        "id": "PKBJM5K_1d34"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Writing a preprocessing function to structure our data for modelling"
      ],
      "metadata": {
        "id": "wFI84caWPjo6"
      }
    },
    {
      "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 its contents and sorts through each line,\n",
        "  extracting the target label, the text of the sentence, how many sentenecs\n",
        "  are in the current abstract and what sentence number the target line is.\n",
        "  \"\"\"\n",
        "  input_lines = get_lines(filename) # get all lines from filename\n",
        "  abstract_lines = \"\" # create an empty abstract\n",
        "  abstract_samples = [] # create an empty list of abstracts\n",
        "\n",
        "  # Loop through each line in the target file\n",
        "  for line in input_lines:\n",
        "    # https://www.w3schools.com/python/ref_string_startswith.asp\n",
        "    if line.startswith(\"###\"): # check to see if it is an ID Line\n",
        "      abstract_id = line\n",
        "      abstract_lines = \"\" # reset the abstract string if the line is an id line\n",
        "    # https://www.w3schools.com/python/ref_string_isspace.asp\n",
        "    elif line.isspace(): # check to see if line is a new line (\\n is a space)\n",
        "      abstract_line_split = abstract_lines.splitlines() # split abstract into separate lines\n",
        "\n",
        "      # Iterate through each line in a single abstract and count them at the same time\n",
        "      for abstract_line_number, 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_number # what number line does the line appear in the abstract\n",
        "        line_data[\"total_lines\"] = len(abstract_line_split) # 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",
        "    else: # if the above conditions aren't fulfilled, the line contains a labelled sentence\n",
        "      abstract_lines += line\n",
        "\n",
        "  return abstract_samples"
      ],
      "metadata": {
        "id": "37vcK_Mj3HpO"
      },
      "execution_count": null,
      "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 validation\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": "Ys7F2aRZ47VA",
        "outputId": "1ef0c417-97a3-4284-dbc4-7c5bc9052c7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "180040 30212 30135\n",
            "CPU times: user 326 ms, sys: 79 ms, total: 405 ms\n",
            "Wall time: 405 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the first abstract of our training data\n",
        "train_samples[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vN83IINf8Eet",
        "outputId": "a1a914df-2a13-4230-8b72-a534bce45cac"
      },
      "execution_count": null,
      "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': 12},\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': 12},\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': 12},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'pain was assessed using the visual analog pain scale ( @-@ mm ) .',\n",
              "  'line_number': 3,\n",
              "  'total_lines': 12},\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': 12},\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': 12},\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': 12},\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': 12},\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': 12},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'these differences remained significant at @ weeks .',\n",
              "  'line_number': 9,\n",
              "  'total_lines': 12},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'the outcome measures in rheumatology clinical trials-osteoarthritis research society international responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ) .',\n",
              "  'line_number': 10,\n",
              "  'total_lines': 12},\n",
              " {'target': 'CONCLUSIONS',\n",
              "  'text': 'low-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee oa ( clinicaltrials.gov identifier nct@ ) .',\n",
              "  'line_number': 11,\n",
              "  'total_lines': 12},\n",
              " {'target': 'BACKGROUND',\n",
              "  'text': 'emotional eating is associated with overeating and the development of obesity .',\n",
              "  'line_number': 0,\n",
              "  'total_lines': 11},\n",
              " {'target': 'BACKGROUND',\n",
              "  'text': 'yet , empirical evidence for individual ( trait ) differences in emotional eating and cognitive mechanisms that contribute to eating during sad mood remain equivocal .',\n",
              "  'line_number': 1,\n",
              "  'total_lines': 11},\n",
              " {'target': 'OBJECTIVE',\n",
              "  'text': 'the aim of this study was to test if attention bias for food moderates the effect of self-reported emotional eating during sad mood ( vs neutral mood ) on actual food intake .',\n",
              "  'line_number': 2,\n",
              "  'total_lines': 11},\n",
              " {'target': 'OBJECTIVE',\n",
              "  'text': 'it was expected that emotional eating is predictive of elevated attention for food and higher food intake after an experimentally induced sad mood and that attentional maintenance on food predicts food intake during a sad versus a neutral mood .',\n",
              "  'line_number': 3,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'participants ( n = @ ) were randomly assigned to one of the two experimental mood induction conditions ( sad/neutral ) .',\n",
              "  'line_number': 4,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'attentional biases for high caloric foods were measured by eye tracking during a visual probe task with pictorial food and neutral stimuli .',\n",
              "  'line_number': 5,\n",
              "  'total_lines': 11},\n",
              " {'target': 'METHODS',\n",
              "  'text': 'self-reported emotional eating was assessed with the dutch eating behavior questionnaire ( debq ) and ad libitum food intake was tested by a disguised food offer .',\n",
              "  'line_number': 6,\n",
              "  'total_lines': 11},\n",
              " {'target': 'RESULTS',\n",
              "  'text': 'hierarchical multivariate regression modeling showed that self-reported emotional eating did not account for changes in attention allocation for food or food intake in either condition .',\n",
              "  'line_number': 7,\n",
              "  'total_lines': 11}]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performing visual data analysis on our preprocessed text\n",
        "\n",
        "Now that our data is the format of a list of dictionaries, how about we turn it into a DataFrame to further visualize it?"
      ],
      "metadata": {
        "id": "v2tMfzxs8Qps"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "train_df = pd.DataFrame(train_samples)\n",
        "val_df = pd.DataFrame(val_samples)\n",
        "test_df = pd.DataFrame(test_samples)\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "-YF19WDNOepc",
        "outputId": "1216c392-898b-4e7d-d4da-8fe35cb097af"
      },
      "execution_count": null,
      "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",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # Distribution of labels in training data\n",
        "train_df.target.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNJqYZNjOp_t",
        "outputId": "1f8f8f23-0b26-46f4-c677-8c7f333493f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "METHODS        59353\n",
              "RESULTS        57953\n",
              "CONCLUSIONS    27168\n",
              "BACKGROUND     21727\n",
              "OBJECTIVE      13839\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check the length of different lines\n",
        "train_df.total_lines.plot.hist();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "lCN5EYvoPBzO",
        "outputId": "a1b8af8f-a6ef-4b4a-fa1c-ae514b4a0952"
      },
      "execution_count": null,
      "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 lists of sentences"
      ],
      "metadata": {
        "id": "ptRiNTx4PNfJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert abstract text lines into lists\n",
        "train_sentences = train_df[\"text\"].tolist()\n",
        "val_sentences = val_df[\"text\"].tolist()\n",
        "test_sentences = test_df[\"text\"].tolist()\n",
        "len(train_sentences), len(val_sentences), len(test_sentences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8nfQX8wPrw2",
        "outputId": "1b984c6c-b9e8-46fa-b1f3-26f221bc0ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180040, 30212, 30135)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the 10 lines of training sentences\n",
        "train_sentences[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5_r5alkP4bN",
        "outputId": "0911a73a-9d96-460a-f4d5-04df06280e17"
      },
      "execution_count": null,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Turning our target labels into numbers (ML models require numbers)"
      ],
      "metadata": {
        "id": "gxLEfXytP7pn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# One hot encode labels\n",
        "# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "one_hot_encoder = OneHotEncoder(sparse=False) # we want non-sparse matrix\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",
        "# check what one hot encoded labels look like\n",
        "train_labels_one_hot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AKJeD29VTNB",
        "outputId": "64bf96cf-8deb-4218-b2bd-9236db5657c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 1., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., 0., 1.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Label encoded labels\n",
        "\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html"
      ],
      "metadata": {
        "id": "YyG2iL9OWFME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract labels (\"target\" column) and encode them into integers\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "train_labels_encoded = label_encoder.fit_transform(train_df[\"target\"].to_numpy())\n",
        "val_labels_encoded = label_encoder.transform(val_df[\"target\"].to_numpy())\n",
        "test_labels_encoded = label_encoder.transform(test_df[\"target\"].to_numpy())\n",
        "\n",
        "# check what training labels look like\n",
        "train_labels_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsuOgU5eXSju",
        "outputId": "741c9776-7df6-4081-e115-d5256d99e242"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 2, ..., 4, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VHasDexXvz-",
        "outputId": "63b0d629-6e11-402d-fc17-ea79c358554b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5,\n",
              " array(['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'],\n",
              "       dtype=object))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "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 ones work best.\n",
        "\n",
        "And as always, we're going to start with a baseline (TF-IDF Multinomial Naive Bayes classifier)"
      ],
      "metadata": {
        "id": "xjXHWc-viJ05"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 0: Creating, fitting and evaluating a baseline model for SkimLit"
      ],
      "metadata": {
        "id": "ZiJD53jmhZRz"
      }
    },
    {
      "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",
        "    (\"clf\", MultinomialNB())\n",
        "])\n",
        "\n",
        "# Fit the pipeline to the training data\n",
        "model_0.fit(X=train_sentences, y=train_labels_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "9Cg2RFaIiIt0",
        "outputId": "6687756d-784f-48d3-a67d-8d40aab3f778"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tf-idf', TfidfVectorizer()), ('clf', MultinomialNB())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tf-idf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tf-idf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate baseline model on validation dataset\n",
        "model_0.score(X=val_sentences, y=val_labels_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUjDkY1ii9wL",
        "outputId": "007849e7-3c13-4774-ae56-fb27fdbe555a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7218323844829869"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions using our baseline model\n",
        "baseline_preds = model_0.predict(val_sentences)\n",
        "baseline_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBn8z5hfjE8i",
        "outputId": "4f16e5a1-4e3e-40d3-a16b-996b8e75ff9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 1, 3, ..., 4, 4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_labels_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bft9eZvojQjK",
        "outputId": "999b310b-2a86-48bd-8b04-8268561f59c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 3, ..., 4, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download helper function script\n",
        "\n",
        "In a previous module, we wrote a function to compare predictions across different metrics (accuracy, precision, recall, and f1) and rather than rewriting it here, let's download it from our helper function script."
      ],
      "metadata": {
        "id": "qOJ8hUGNjTQT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBxuNfAMjfaD",
        "outputId": "0feddef4-9736-45a8-c9de-8979447c6bc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-07-08 17:17:12--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10246 (10K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "helper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-07-08 17:17:13 (114 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import calculate_results"
      ],
      "metadata": {
        "id": "1I3pqkMPjuJK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate baseline results\n",
        "baseline_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                     y_pred=baseline_preds)\n",
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNnm4Tvyjwjy",
        "outputId": "88a4a490-5766-4baf-e0d5-9da4f29cc840"
      },
      "execution_count": null,
      "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": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing our data for deep sequence models\n",
        "\n",
        "Before we start buiding deep models, we've got to create vectorization and embedding layers."
      ],
      "metadata": {
        "id": "3jIojJN5kE5n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "PsvtJSalm2O0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# How long is each sentence on average?\n",
        "sent_lens = [len(sentence.split()) for sentence in train_sentences]\n",
        "avg_sent_len = np.mean(sent_lens)\n",
        "avg_sent_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tENFmzf9nCIh",
        "outputId": "695eb310-a824-4adc-d70a-2078610de2bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.338269273494777"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What's the distribution look like?\n",
        "import matplotlib.pyplot as plt\n",
        "plt.hist(sent_lens, bins=50);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "oKwwGPlxr4j_",
        "outputId": "b0298258-232f-4ee7-d19c-02ec1415270f"
      },
      "execution_count": null,
      "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 of a sentence length covers 95% of examples?\n",
        "# https://numpy.org/doc/stable/reference/generated/numpy.percentile.html\n",
        "output_seq_len = int(np.percentile(sent_lens, 95))\n",
        "output_seq_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6z647Tzt5c_",
        "outputId": "58974823-33e6-49a2-c47b-e263da648d2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Maximum sequence length in the training set\n",
        "max(sent_lens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zmba7cKRunyu",
        "outputId": "e27e755a-90b1-4b2c-aac1-9dd02f336480"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "296"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a text vectoriser to map our tokens (text) to numbers\n",
        "\n",
        "We want to make a layer which maps our texts from words to numbers.\n",
        "\n",
        "https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization"
      ],
      "metadata": {
        "id": "7F6gTa2bu07D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# How many words are in our vocabulary? (taken from table 1 in: https://arxiv.org/pdf/1612.05251.pdf)\n",
        "max_tokens = 68000"
      ],
      "metadata": {
        "id": "86PIHSbYu6d7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create text vectorizer\n",
        "from tensorflow.keras.layers.experimental.preprocessing 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 sequences"
      ],
      "metadata": {
        "id": "lWrL5D6yvX8-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adapt text vectorizer to training sentences\n",
        "text_vectorizer.adapt(train_sentences)"
      ],
      "metadata": {
        "id": "Ma_xb3WMvv-3"
      },
      "execution_count": null,
      "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: {text_vectorizer([target_sentence])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFj2xxerwCLG",
        "outputId": "7577c7e0-4ee3-4905-b94a-781ca5c25fe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text:\n",
            "our results did not show that pemf treatment was mechanically more effective for isometric peak torque generation compared to the sham group .\n",
            "\n",
            "Length of text: 23\n",
            "\n",
            "Vectorized text: [[  246   117   112    31   812    28 15619    19    10  6634    69   137\n",
            "     11  3379   690  4257  3752    34     6     2   786    13     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": "-d3oUzONwfDA",
        "outputId": "d5d70718-dbec-48ee-e98b-9baad904ce12"
      },
      "execution_count": null,
      "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 the config of our text vectorizer\n",
        "text_vectorizer.get_config()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1fc8mi4xVl9",
        "outputId": "31fa1901-00fe-4d0f-a083-24001ef6eeed"
      },
      "execution_count": null,
      "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": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a custom token embedding layer with TensorFlow"
      ],
      "metadata": {
        "id": "5y1Hc3h5xzqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create token embedding layer\n",
        "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 numbers of parameters to train,\n",
        "                               mask_zero=True, # use masking to handle variable sequence lengths (save space)\n",
        "                               name=\"token_embedding\")"
      ],
      "metadata": {
        "id": "TldLH61Qz0zd"
      },
      "execution_count": null,
      "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 (before embedding): {vectorized_sentence}\\n\")\n",
        "embedded_sentence = token_embed(vectorized_sentence)\n",
        "print(f\"Sentence after embedding:\\n {embedded_sentence}\\n\")\n",
        "print(f\"Embedded sentence shape: {embedded_sentence.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HRiGYPy6zf2",
        "outputId": "19e9b735-a447-4cef-de3a-199efec675ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence before vectorization:\n",
            " our results did not show that pemf treatment was mechanically more effective for isometric peak torque generation compared to the sham group .\n",
            "\n",
            "Sentence after vectorization (before embedding): [[  246   117   112    31   812    28 15619    19    10  6634    69   137\n",
            "     11  3379   690  4257  3752    34     6     2   786    13     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",
            " [[[ 0.03836082 -0.02389183  0.02041031 ... -0.02247231  0.02325699\n",
            "   -0.02154949]\n",
            "  [ 0.01733481  0.01011053  0.02455891 ... -0.01591859 -0.03107038\n",
            "   -0.00277019]\n",
            "  [ 0.03336468  0.02133042  0.02022925 ...  0.00429337  0.04379598\n",
            "   -0.04638555]\n",
            "  ...\n",
            "  [-0.01056803 -0.00775651 -0.01343326 ...  0.03313867  0.0069316\n",
            "   -0.01606699]\n",
            "  [-0.01056803 -0.00775651 -0.01343326 ...  0.03313867  0.0069316\n",
            "   -0.01606699]\n",
            "  [-0.01056803 -0.00775651 -0.01343326 ...  0.03313867  0.0069316\n",
            "   -0.01606699]]]\n",
            "\n",
            "Embedded sentence shape: (1, 55, 128)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating fast loading dataset with the TensorFlow tf.data API\n",
        "\n",
        "We're going to setup our data to run as fast as possible with the TensorFlow tf.data API, many of the steps here are discussed at length in these two resources:\n",
        "* https://www.tensorflow.org/guide/data_performance\n",
        "* https://www.tensorflow.org/guide/data"
      ],
      "metadata": {
        "id": "c3fDRfZ-7Q-c"
      }
    },
    {
      "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",
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGPKnfLyIKb7",
        "outputId": "18f3a374-0049-4785-b37a-2b1df302f665"
      },
      "execution_count": null,
      "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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels_one_hot.shape, train_labels_one_hot[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTbISJi2IxPa",
        "outputId": "9cb0eaf1-53ea-4191-8731-e6298cf7114c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((180040, 5), array([0., 0., 0., 1., 0.]))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Take 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_datset = test_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Je_S-Py0IqzP",
        "outputId": "a1003871-a5de-4e13-ba0f-8ca824c835d5"
      },
      "execution_count": null,
      "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": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 1: Building, fitting and evaluating a Conv1D with token embeddings"
      ],
      "metadata": {
        "id": "RRo8BxPgLXoF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hu8HAX8FPdhc",
        "outputId": "e06dce28-26ce-429e-daf2-9f728e60b105"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['BACKGROUND', 'CONCLUSIONS', 'METHODS', 'OBJECTIVE', 'RESULTS'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "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/map text inputs to integers\n",
        "token_embeddings = token_embed(text_vectors) # create embedding\n",
        "x = layers.Conv1D(64, kernel_size=5, padding=\"same\", activation=\"relu\")(token_embeddings)\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",
        "# Compile\n",
        "model_1.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "6JWTF7gSNSAq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkkkxtQhNlyV",
        "outputId": "97addf73-b046-4074-b1b4-b11f3f3d9b51"
      },
      "execution_count": null,
      "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 (TextVec  (None, 55)               0         \n",
            " torization)                                                     \n",
            "                                                                 \n",
            " token_embedding (Embedding)  (None, 55, 128)          8299648   \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 55, 64)            41024     \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 64)               0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,340,997\n",
            "Trainable params: 8,340,997\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGMReQaqTQS5",
        "outputId": "42b9a805-0fbd-4338-e933-e689d04710d9"
      },
      "execution_count": null,
      "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": 46
        }
      ]
    },
    {
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPZdaN53PyNP",
        "outputId": "29d0083b-7ca3-4245-c33b-4618c303659a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "562/562 [==============================] - 44s 62ms/step - loss: 0.9126 - accuracy: 0.6381 - val_loss: 0.6877 - val_accuracy: 0.7374\n",
            "Epoch 2/5\n",
            "562/562 [==============================] - 9s 15ms/step - loss: 0.6610 - accuracy: 0.7549 - val_loss: 0.6361 - val_accuracy: 0.7693\n",
            "Epoch 3/5\n",
            "562/562 [==============================] - 5s 9ms/step - loss: 0.6201 - accuracy: 0.7744 - val_loss: 0.6003 - val_accuracy: 0.7832\n",
            "Epoch 4/5\n",
            "562/562 [==============================] - 4s 8ms/step - loss: 0.5899 - accuracy: 0.7884 - val_loss: 0.5785 - val_accuracy: 0.7892\n",
            "Epoch 5/5\n",
            "562/562 [==============================] - 5s 10ms/step - loss: 0.5903 - accuracy: 0.7916 - val_loss: 0.5603 - val_accuracy: 0.7965\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate on whole validation dataset\n",
        "model_1.evaluate(valid_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEoFg_8aURWb",
        "outputId": "08279121-a851-4136-819c-7e3b315ef0b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 4s 4ms/step - loss: 0.5605 - accuracy: 0.8018\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5604760050773621, 0.8017675280570984]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "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": "-yB64qWvVZbZ",
        "outputId": "ca5dfbfa-64a6-4587-c36d-c43a7c568358"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 2s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[5.8039778e-01, 1.6335209e-01, 3.3200983e-02, 1.7391002e-01,\n",
              "         4.9139153e-02],\n",
              "        [4.8109576e-01, 3.1462115e-01, 2.3878081e-02, 1.4641419e-01,\n",
              "         3.3990838e-02],\n",
              "        [1.9080548e-01, 4.6305833e-03, 1.5512316e-03, 8.0296880e-01,\n",
              "         4.3805372e-05],\n",
              "        ...,\n",
              "        [3.0497358e-05, 9.0488407e-04, 4.3967781e-03, 2.9682216e-05,\n",
              "         9.9463814e-01],\n",
              "        [1.1017788e-01, 2.5664508e-01, 1.4347814e-01, 8.6483136e-02,\n",
              "         4.0321580e-01],\n",
              "        [1.6124679e-01, 7.5438774e-01, 4.2127904e-02, 1.2204163e-02,\n",
              "         3.0033449e-02]], dtype=float32),\n",
              " (30212, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "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": "WuX9oDvzVmqH",
        "outputId": "5c432518-46a3-4a04-bd04-2b9cc98a4f2d"
      },
      "execution_count": null,
      "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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate model_1 results\n",
        "model_1_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                    y_pred=model_1_preds)\n",
        "model_1_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-NExue8Wb1-",
        "outputId": "1a854a96-87f5-436e-8b80-1899cd136c14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 80.17675095988349,\n",
              " 'precision': 0.8026582351450349,\n",
              " 'recall': 0.8017675095988349,\n",
              " 'f1': 0.79890614827618}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GorhpVgkWnDS",
        "outputId": "62b67183-cd6a-44d6-9be4-8b59faba6c77"
      },
      "execution_count": null,
      "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": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2: Preparing a pretrained embedding layer from TensorFlow Hub for Model 2\n",
        "\n",
        "Now let' use pretrained word embeddings from TensorFlow Hub, more specifically the universal sentence encoder (USE): https://tfhub.dev/google/universal-sentence-encoder/4\n",
        "\n",
        "Check https://huggingface.co/models for pretrained NLP models\n",
        "\n",
        "The paper originally used Glove embeddings, however, we're going to stick with the later created USE pretrained embeddings."
      ],
      "metadata": {
        "id": "m79Vzb6ZWpI8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download pretrained TensorFlow Hub USE\n",
        "import tensorflow_hub as hub\n",
        "tf_hub_embedding_layer = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder/4\",\n",
        "                                        trainable=False,\n",
        "                                        name=\"universal_sentence_encoder\")"
      ],
      "metadata": {
        "id": "qia3o6JXYtJj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out the pretrained embedding on a random sentence\n",
        "random_train_sentence = random.choice(train_sentences)\n",
        "print(f\"Random sentence:\\n {random_train_sentence}\")\n",
        "use_embedded_sentence = tf_hub_embedding_layer([random_train_sentence]) # no need to vectorize\n",
        "print(f\"Sentence after embedding:\\n {use_embedded_sentence[0][:30]}\\n\")\n",
        "print(f\"Length of sentence embedding: {len(use_embedded_sentence[0])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sx-gjtyFesX4",
        "outputId": "a289aae3-b821-46e5-d17e-b38758d4c5f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random sentence:\n",
            " the aim of this study was to investigate whether intracranial area ( ica ) reliably predicts actual icv in a healthy pediatric cohort and in children with convulsive status epilepticus ( cse ) .\n",
            "Sentence after embedding:\n",
            " [-0.04977063  0.07225271 -0.06082502 -0.03774996 -0.0754696  -0.05510222\n",
            " -0.05499706 -0.03838943  0.0514782  -0.02478872  0.07125878  0.02477203\n",
            "  0.0567278   0.02868585 -0.00478395  0.04896519 -0.09169798 -0.00182896\n",
            " -0.01284292 -0.08772787 -0.00219573  0.03173465  0.00133895 -0.00985405\n",
            "  0.04547279  0.02590039  0.00482306 -0.01139645  0.05216054  0.05653668]\n",
            "\n",
            "Length of sentence embedding: 512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 2: Building, fitting and evaluating a Conv1D model with token embeddings"
      ],
      "metadata": {
        "id": "L9kiW7MqfSEO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define feature extraction model using TF Hub layer\n",
        "inputs = layers.Input(shape=[], dtype=tf.string)\n",
        "pretrained_embedding = tf_hub_embedding_layer(inputs) # tokenize text and create embedding of each sequence (512 long vector)\n",
        "x = layers.Dense(128, activation=\"relu\")(pretrained_embedding)\n",
        "# Note: you could add more layers here if you wanted to\n",
        "outputs = layers.Dense(num_classes, activation=\"softmax\")(x) # create the output layer\n",
        "model_2 = tf.keras.Model(inputs=inputs,\n",
        "                         outputs=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\"])"
      ],
      "metadata": {
        "id": "PPs1y7GTgjiM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amB1OyfuiFoC",
        "outputId": "e334ce6a-be67-424d-e116-68bbcbc9ea38"
      },
      "execution_count": null,
      "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",
            " universal_sentence_encoder   (None, 512)              256797824 \n",
            " (KerasLayer)                                                    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 5)                 645       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 256,864,133\n",
            "Trainable params: 66,309\n",
            "Non-trainable params: 256,797,824\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit model 2 to the data\n",
        "history_model_2 = model_2.fit(train_dataset,\n",
        "                              epochs=3,\n",
        "                              steps_per_epoch=int(0.1 * len(train_dataset)),\n",
        "                              validation_data=valid_dataset,\n",
        "                              validation_steps=int(0.1 * len(valid_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwaafhUziHEe",
        "outputId": "773cb6b4-39ca-432a-b139-d8e5e26ebbdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 14s 19ms/step - loss: 0.9151 - accuracy: 0.6500 - val_loss: 0.7972 - val_accuracy: 0.6888\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 7s 13ms/step - loss: 0.7692 - accuracy: 0.7016 - val_loss: 0.7563 - val_accuracy: 0.7035\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 8s 14ms/step - loss: 0.7528 - accuracy: 0.7123 - val_loss: 0.7411 - val_accuracy: 0.7138\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate on the whole validation dataset\n",
        "model_2.evaluate(valid_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNALi4vmijiU",
        "outputId": "7936c55e-44bc-4c6d-d1e5-8e6c4ea57959"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 14s 15ms/step - loss: 0.7425 - accuracy: 0.7140\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7425259351730347, 0.7140209078788757]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REKPVkm3i7IL",
        "outputId": "357483b2-b4f5-4ebe-f55a-8cd08e90d448"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 12s 12ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.43852   , 0.34104204, 0.00302077, 0.20897903, 0.00843822],\n",
              "       [0.38456422, 0.4567108 , 0.00381037, 0.15139247, 0.00352211],\n",
              "       [0.22812776, 0.15005475, 0.01854679, 0.55932647, 0.0439443 ],\n",
              "       ...,\n",
              "       [0.00151968, 0.00651914, 0.04940793, 0.00097182, 0.9415814 ],\n",
              "       [0.00445871, 0.05288133, 0.18312006, 0.00148336, 0.7580565 ],\n",
              "       [0.17963439, 0.2492078 , 0.51225114, 0.00513642, 0.05377023]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the predictions probabilities found with feature extraction model to labels\n",
        "model_2_preds = tf.argmax(model_2_pred_probs, axis=1)\n",
        "model_2_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSmJnXrtjEWV",
        "outputId": "dfdd48d9-039c-4d6a-8f9e-c22f74b88506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 1, 3, ..., 4, 4, 2])>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate results from TF Hub pretrained embddings on val set\n",
        "model_2_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                    y_pred=model_2_preds)\n",
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YN-Ci6LfjQV-",
        "outputId": "ffa8df60-638c-4e8c-a527-75c1c8fb4e33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 71.4020918840196,\n",
              " 'precision': 0.7143302573902427,\n",
              " 'recall': 0.7140209188401959,\n",
              " 'f1': 0.711085946701376}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
      ],
      "metadata": {
        "id": "yk8o3VRujbnK",
        "outputId": "1f1bbcba-2aa7-46c0-c747-fce4213f0dd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 3: Creating a character-level tokeniser with TensorFlow's TextVectorization layer\n",
        "\n",
        "The paper which we're replicating states they used a combination of token and character-level embeddings.\n",
        "\n",
        "Previously we've used token-level embeddings but we'll need to do similar steps for characters if we want to use char-level embeddings."
      ],
      "metadata": {
        "id": "uB9jKAvEjc0n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a character-level tokenizer"
      ],
      "metadata": {
        "id": "ZBL5EjHu8BcZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lW4ihvEX_545",
        "outputId": "4b72d273-5d57-4877-939e-c76334c776dc"
      },
      "execution_count": null,
      "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": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make function to split sentences into characters\n",
        "def split_chars(text):\n",
        "  return \" \".join(list(text))\n",
        "\n",
        "# Test splitting non-character-level sequence into characters\n",
        "split_chars(random_train_sentence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "ew-9JtGn_8Tn",
        "outputId": "e66c9573-efa1-4cc4-cbdf-16e1257d0c08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'t h e   a i m   o f   t h i s   s t u d y   w a s   t o   i n v e s t i g a t e   w h e t h e r   i n t r a c r a n i a l   a r e a   (   i c a   )   r e l i a b l y   p r e d i c t s   a c t u a l   i c v   i n   a   h e a l t h y   p e d i a t r i c   c o h o r t   a n d   i n   c h i l d r e n   w i t h   c o n v u l s i v e   s t a t u s   e p i l e p t i c u s   (   c s e   )   .'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\" \".join(list(train_sentences[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "nxLgSm-mAFyC",
        "outputId": "5b7ca1c5-932f-49f7-f00e-3ae5612cef07"
      },
      "execution_count": null,
      "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   )   .'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split sequence-level data splits into character-level data splits\n",
        "train_chars = [split_chars(sentence) for sentence in train_sentences]\n",
        "val_chars = [split_chars(sentence) for sentence in val_sentences]\n",
        "test_chars = [split_chars(sentence) for sentence in test_sentences]\n",
        "train_chars[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHWizCkxAGsj",
        "outputId": "6c775b1f-3dd7-4246-d1e3-5a24e594d084"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['t o   i n v e s t i g a t e   t h e   e f f i c a c y   o f   @   w e e k s   o f   d a i l y   l o w - d o s e   o r a l   p r e d n i s o l o n e   i n   i m p r o v i n g   p a i n   ,   m o b i l i t y   ,   a n d   s y s t e m i c   l o w - g r a d e   i n f l a m m a t i o n   i n   t h e   s h o r t   t e r m   a n d   w h e t h e r   t h e   e f f e c t   w o u l d   b e   s u s t a i n e d   a t   @   w e e k s   i n   o l d e r   a d u l t s   w i t h   m o d e r a t e   t o   s e v e r e   k n e e   o s t e o a r t h r i t i s   (   o a   )   .',\n",
              " 'a   t o t a l   o f   @   p a t i e n t s   w i t h   p r i m a r y   k n e e   o a   w e r e   r a n d o m i z e d   @ : @   ;   @   r e c e i v e d   @   m g / d a y   o f   p r e d n i s o l o n e   a n d   @   r e c e i v e d   p l a c e b o   f o r   @   w e e k s   .',\n",
              " 'o u t c o m e   m e a s u r e s   i n c l u d e d   p a i n   r e d u c t i o n   a n d   i m p r o v e m e n t   i n   f u n c t i o n   s c o r e s   a n d   s y s t e m i c   i n f l a m m a t i o n   m a r k e r s   .',\n",
              " 'p a i n   w a s   a s s e s s e d   u s i n g   t h e   v i s u a l   a n a l o g   p a i n   s c a l e   (   @ - @   m m   )   .',\n",
              " 's e c o n d a r y   o u t c o m e   m e a s u r e s   i n c l u d e d   t h e   w e s t e r n   o n t a r i o   a n d   m c m a s t e r   u n i v e r s i t i e s   o s t e o a r t h r i t i s   i n d e x   s c o r e s   ,   p a t i e n t   g l o b a l   a s s e s s m e n t   (   p g a   )   o f   t h e   s e v e r i t y   o f   k n e e   o a   ,   a n d   @ - m i n   w a l k   d i s t a n c e   (   @ m w d   )   .']"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What's the average character length?\n",
        "char_lens = [len(sentence) for sentence in train_sentences]\n",
        "mean_char_len = np.mean(char_lens)\n",
        "mean_char_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQNPJp6lAxsg",
        "outputId": "f508cdb6-b75c-4e1b-bfda-944d6fb6e801"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149.3662574983337"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the distribution of our sequencs at a character-level\n",
        "plt.hist(char_lens, bins=50);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "YMofuAwrBhKJ",
        "outputId": "b0016c37-8aae-4195-cb7c-7230cfdf081c"
      },
      "execution_count": null,
      "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 what character length covers 95% of sequences\n",
        "output_seq_char_len = int(np.percentile(char_lens, 95))\n",
        "output_seq_char_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xT7jSgRjCJlG",
        "outputId": "6b5ba7b2-2001-434d-8905-8bb65321e404"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "290"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all keyboard characters\n",
        "import string\n",
        "alphabet = string.ascii_lowercase + string.digits + string.punctuation\n",
        "alphabet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "9LwikBLKCdMP",
        "outputId": "4a0bd579-e5b2-4422-92f8-5143ded62c0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'abcdefghijklmnopqrstuvwxyz0123456789!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create char-level token vectorizer instance\n",
        "NUM_CHAR_TOKENS = len(alphabet) + 2 # add 2 for space and OOV token (OOV = out of vocabulary)\n",
        "char_vectorizer = TextVectorization(max_tokens=NUM_CHAR_TOKENS,\n",
        "                                    output_sequence_length=output_seq_char_len,\n",
        "                                    # standardize=None, # set standardization to \"None\" if you want to leave punctuation in\n",
        "                                    name=\"char_vectorizer\")"
      ],
      "metadata": {
        "id": "STb-qvKgDZqY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adapt character vectorizer to training character\n",
        "char_vectorizer.adapt(train_chars)"
      ],
      "metadata": {
        "id": "aJeqDMIkEdNg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check character vocab stats\n",
        "char_vocab = char_vectorizer.get_vocabulary()\n",
        "print(f\"Number of different characters in character vocab: {len(char_vocab)}\")\n",
        "print(f\"5 most common characters: {char_vocab[:5]}.\")\n",
        "print(f\"5 least common characters: {char_vocab[-5:]}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpOH-isNEiaa",
        "outputId": "1cbff498-d4f4-4d8c-d432-9f6f55c5f6f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of different characters in character vocab: 28\n",
            "5 most common characters: ['', '[UNK]', 'e', 't', 'i'].\n",
            "5 least common characters: ['k', 'x', 'z', 'q', 'j'].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out charater vectorizer\n",
        "random_train_chars = random.choice(train_chars)\n",
        "print(f\"Charified text:\\n {random_train_chars}\")\n",
        "print(f\"\\nLength of random_train_chars: {len(random_train_chars.split())}\")\n",
        "vectorized_chars = char_vectorizer([random_train_chars])\n",
        "print(f\"\\nVectorized chars:\\n {vectorized_chars}\")\n",
        "print(f\"\\nLength of vectorized chars: {len(vectorized_chars[0])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKZSqpGoE5Kh",
        "outputId": "0831c45b-9a59-40c5-c691-d5f0fedfb048"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Charified text:\n",
            " f i n d i n g s   w i l l   i n f o r m   s a m p l e   s i z e   c a l c u l a t i o n s   f o r   a   d e f i n i t i v e   t r i a l   .\n",
            "\n",
            "Length of random_train_chars: 60\n",
            "\n",
            "Vectorized chars:\n",
            " [[17  4  6 10  4  6 18  9 20  4 12 12  4  6 17  7  8 15  9  5 15 14 12  2\n",
            "   9  4 25  2 11  5 12 11 16 12  5  3  4  7  6  9 17  7  8  5 10  2 17  4\n",
            "   6  4  3  4 21  2  3  8  4  5 12  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0  0]]\n",
            "\n",
            "Length of vectorized chars: 290\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a character-level embedding layer with tf.keras.layers.Embedding"
      ],
      "metadata": {
        "id": "f-weg82gF5VP"
      }
    },
    {
      "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: https://arxiv.org/abs/1612.05251\n",
        "                              mask_zero=True,\n",
        "                              name=\"char_embed\")"
      ],
      "metadata": {
        "id": "ttd4t9OOP-fP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out 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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJERHk0OSDOl",
        "outputId": "30910b4f-934c-47f0-dd45-d6aee35a582a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Charified text:\n",
            " f i n d i n g s   w i l l   i n f o r m   s a m p l e   s i z e   c a l c u l a t i o n s   f o r   a   d e f i n i t i v e   t r i a l   .\n",
            "\n",
            "Embedded chars (after vectorization and embedding):\n",
            " [[[-0.03896061 -0.01522144 -0.04023875 ...  0.04981387  0.03993871\n",
            "    0.0269846 ]\n",
            "  [ 0.01808107  0.01617037 -0.04691405 ...  0.00285504  0.00391089\n",
            "   -0.01423983]\n",
            "  [-0.04017736  0.02166886 -0.00324623 ...  0.04860066 -0.02180341\n",
            "    0.00857287]\n",
            "  ...\n",
            "  [-0.03375632  0.00756352 -0.0403185  ... -0.04130257  0.02782586\n",
            "    0.0307536 ]\n",
            "  [-0.03375632  0.00756352 -0.0403185  ... -0.04130257  0.02782586\n",
            "    0.0307536 ]\n",
            "  [-0.03375632  0.00756352 -0.0403185  ... -0.04130257  0.02782586\n",
            "    0.0307536 ]]]\n",
            "\n",
            "Character embedding shape: (1, 290, 25)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(random_train_chars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwgylQTlSZAu",
        "outputId": "381d188d-f3f1-47d8-dc8d-cc85969b9807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "139"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 3: Building, fitting and evaluating a Conv1D model on character embeddings"
      ],
      "metadata": {
        "id": "4Bx0q_5FSo0c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make Conv1D on chars only\n",
        "inputs = layers.Input(shape=(1,), dtype=\"string\") # (1,) for taking one sequence at a time\n",
        "char_vectors = char_vectorizer(inputs)\n",
        "char_embeddings = char_embed(char_vectors)\n",
        "x = layers.Conv1D(64, kernel_size=5, padding=\"same\", activation=\"relu\")(char_embeddings)\n",
        "x = layers.GlobalMaxPool1D()(x)\n",
        "outputs = layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "model_3 = tf.keras.Model(inputs=inputs,\n",
        "                         outputs=outputs,\n",
        "                         name=\"model_3_conv1d_char_embeddings\")\n",
        "\n",
        "# Compile\n",
        "model_3.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "Ehj9zZzuTW07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GEHDm1gWKit",
        "outputId": "5662861e-9b14-4e35-e183-1296ea004536"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3_conv1d_char_embeddings\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 1)]               0         \n",
            "                                                                 \n",
            " char_vectorizer (TextVector  (None, 290)              0         \n",
            " ization)                                                        \n",
            "                                                                 \n",
            " char_embed (Embedding)      (None, 290, 25)           700       \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 290, 64)           8064      \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 64)               0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9,089\n",
            "Trainable params: 9,089\n",
            "Non-trainable params: 0\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",
        "val_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",
        "train_char_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iN_6ZipJWLke",
        "outputId": "b0ef657f-df88-4ddf-f64f-0a3e4a636155"
      },
      "execution_count": null,
      "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": 80
        }
      ]
    },
    {
      "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=3,\n",
        "                              validation_data=val_char_dataset,\n",
        "                              validation_steps=int(0.1 * len(val_char_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Je6MGMyPXBp7",
        "outputId": "f4e38740-5e8d-4ec9-92e7-d50dffc3f095"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 7s 11ms/step - loss: 1.2512 - accuracy: 0.4973 - val_loss: 1.0467 - val_accuracy: 0.5814\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 5s 9ms/step - loss: 1.0058 - accuracy: 0.5939 - val_loss: 0.9480 - val_accuracy: 0.6300\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 3s 6ms/step - loss: 0.9321 - accuracy: 0.6311 - val_loss: 0.8749 - val_accuracy: 0.6619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions with character model only\n",
        "model_3_pred_probs = model_3.predict(val_char_dataset)\n",
        "model_3_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_wa5tucXZ_t",
        "outputId": "d1e5dc35-06e1-407a-86e6-158a082a269c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 2s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.21458593, 0.38833073, 0.07678685, 0.24252284, 0.07777369],\n",
              "       [0.12929417, 0.6259944 , 0.00684369, 0.20073815, 0.03712964],\n",
              "       [0.11206801, 0.40758026, 0.08808852, 0.2917088 , 0.10055447],\n",
              "       ...,\n",
              "       [0.01974881, 0.0405837 , 0.26754838, 0.03460064, 0.63751847],\n",
              "       [0.0241317 , 0.13079241, 0.27525774, 0.03649729, 0.53332084],\n",
              "       [0.40451065, 0.46000123, 0.05283505, 0.06859942, 0.01405369]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert prediction probabilities to class labels\n",
        "model_3_preds = tf.argmax(model_3_pred_probs, axis=1)\n",
        "model_3_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vV_zG-SHXwXb",
        "outputId": "428c887e-4398-4171-bc02-ed7edac2bfb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([1, 1, 1, ..., 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate results for Conv1D model chars only\n",
        "model_3_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                    y_pred=model_3_preds)\n",
        "model_3_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQbrUFptX4S5",
        "outputId": "dd91e6e0-c254-4f65-e392-1c7e4c9e9edb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 64.87819409506157,\n",
              " 'precision': 0.6431529168482056,\n",
              " 'recall': 0.6487819409506157,\n",
              " 'f1': 0.6381875460811469}"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2MMhILNYCkj",
        "outputId": "522612bc-a4ea-48dd-85ff-24bb85129723"
      },
      "execution_count": null,
      "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": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 4: Discussing how we're going to build Model 4 (character + token embeddings)\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`) - https://www.tensorflow.org/api_docs/python/tf/keras/layers/Concatenate\n",
        "4. Build a series of output layers on top of 3 similar to Figure 1 and section 4.2 of https://arxiv.org/pdf/1612.05251.pdf\n",
        "  * Using dropout layer https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout\n",
        "5. Construct a model which takes token and character-level sequences as input and produces sequence label probabilities as output."
      ],
      "metadata": {
        "id": "kvzt6gfXYEsU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 4: Building a multi-input model (hybrid token + character embeddings)"
      ],
      "metadata": {
        "id": "qAAcFWkMZG1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Setup token inputs/models\n",
        "token_inputs = layers.Input(shape=[], dtype=tf.string, name=\"token_input\") # [] because tf hub UE expects [] as input\n",
        "token_embeddings = tf_hub_embedding_layer(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. 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(24))(char_embeddings) # bi-LSTM shown in Figure 1 of https://arxiv.org/pdf/1612.05251.pdf\n",
        "char_model = tf.keras.Model(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 - adding in Dropout, discussed in section 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)\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": "1A1rIdYeG-xt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a summary of our model\n",
        "model_4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwtt46O_MsYY",
        "outputId": "fbb120fa-61e9-4f21-8246-0a9edf048651"
      },
      "execution_count": null,
      "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 (TextVectoriza  (None, 290)         0           ['char_input[0][0]']             \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " universal_sentence_encoder (Ke  (None, 512)         256797824   ['token_input[0][0]']            \n",
            " rasLayer)                                                                                        \n",
            "                                                                                                  \n",
            " char_embed (Embedding)         (None, 290, 25)      700         ['char_vectorizer[1][0]']        \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 128)          65664       ['universal_sentence_encoder[1][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            " bidirectional (Bidirectional)  (None, 48)           9600        ['char_embed[1][0]']             \n",
            "                                                                                                  \n",
            " token_char_hybrid (Concatenate  (None, 176)         0           ['dense_4[0][0]',                \n",
            " )                                                                'bidirectional[0][0]']          \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 176)          0           ['token_char_hybrid[0][0]']      \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 128)          22656       ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 128)          0           ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            " dense_6 (Dense)                (None, 5)            645         ['dropout_1[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 256,897,089\n",
            "Trainable params: 99,265\n",
            "Non-trainable params: 256,797,824\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 4: Plotting and visually exploring different data inputs"
      ],
      "metadata": {
        "id": "jTjmVNTwNIm_"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "R4XphINOOH44",
        "outputId": "e9b6ac3b-dd63-467f-a545-e495bebc4464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile token char model\n",
        "model_4.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(), # sectino 4.2 of the paper says they use SGD, you might want to try this\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "UHbRy-cSOhvU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Crafting multi-input fast loading tf.data datasets for Model 4"
      ],
      "metadata": {
        "id": "ioKebtA4Tpo5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine chars and tokens 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 and labels\n",
        "\n",
        "# Prefetch and batch train data\n",
        "train_char_token_dataset = train_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "opn8PNdEWW56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Repeat the above steps for our validation data\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 and labels\n",
        "\n",
        "# Prefetch and batch val data\n",
        "val_char_token_dataset = val_char_token_dataset.batch(32).prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "sTozSzpSYHVB"
      },
      "execution_count": null,
      "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": "rpIESqYlYW_h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e546918d-7e68-4595-f70b-6caf37493014"
      },
      "execution_count": null,
      "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": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 4: Building, fitting and evaluating a hybrid embedding model on token and character-level sequences"
      ],
      "metadata": {
        "id": "wzSdPbZOYomU"
      }
    },
    {
      "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=3,\n",
        "                              validation_data=val_char_token_dataset,\n",
        "                              validation_steps=int(0.1 * len(val_char_token_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1A48WhbZQ1S",
        "outputId": "09d9e74b-87f1-4e63-b55d-49fe18d26ebd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 55s 64ms/step - loss: 0.9869 - accuracy: 0.6077 - val_loss: 0.7829 - val_accuracy: 0.7015\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 27s 48ms/step - loss: 0.7987 - accuracy: 0.6944 - val_loss: 0.7166 - val_accuracy: 0.7274\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 28s 50ms/step - loss: 0.7732 - accuracy: 0.7029 - val_loss: 0.6897 - val_accuracy: 0.7410\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate on the whole validation dataset\n",
        "model_4.evaluate(val_char_token_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEVr8-YgstO_",
        "outputId": "6b390a01-c124-4b3f-8a20-2155a4d54279"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 32s 34ms/step - loss: 0.6942 - accuracy: 0.7367\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6941831707954407, 0.7366609573364258]"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVPj-l_ttY5c",
        "outputId": "8009602d-f8e4-48d4-fcf8-fa441f274f91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 25s 23ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.59235907e-01, 2.72948056e-01, 7.07014138e-03, 2.50942856e-01,\n",
              "        9.80301760e-03],\n",
              "       [3.18054378e-01, 5.25595427e-01, 3.65194585e-03, 1.50556609e-01,\n",
              "        2.14162306e-03],\n",
              "       [3.05125296e-01, 1.37044117e-01, 5.58107197e-02, 4.61066604e-01,\n",
              "        4.09532376e-02],\n",
              "       ...,\n",
              "       [6.19153085e-04, 1.01273935e-02, 6.74110726e-02, 2.99710693e-04,\n",
              "        9.21542704e-01],\n",
              "       [5.73273655e-03, 6.29908219e-02, 1.60582557e-01, 2.68759974e-03,\n",
              "        7.68006325e-01],\n",
              "       [3.11287820e-01, 4.10126626e-01, 2.14819908e-01, 1.75121240e-02,\n",
              "        4.62535731e-02]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Format pred probs into pred labels\n",
        "model_4_preds = tf.argmax(model_4_pred_probs, axis=1)\n",
        "model_4_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQ0E1f6ItjwY",
        "outputId": "70b28f12-fa5a-49cf-b28a-d46af751e180"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 1, 3, ..., 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get results of token-char-hybrid model\n",
        "model_4_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                    y_pred=model_4_preds)\n",
        "model_4_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xT0k9kiVt3aV",
        "outputId": "63215145-ae2e-4d86-b47c-d0403d0795f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 73.66609294320138,\n",
              " 'precision': 0.7370983017201089,\n",
              " 'recall': 0.7366609294320138,\n",
              " 'f1': 0.7338978672305317}"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Model 5: Adding positional embeddings via feature engineering (overview)"
      ],
      "metadata": {
        "id": "_7eZzbdFuHJd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "y1My5y1R5ojX",
        "outputId": "b39706c9-7746-487b-b781-288eff7309c4"
      },
      "execution_count": null,
      "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           12  \n",
              "1           12  \n",
              "2           12  \n",
              "3           12  \n",
              "4           12  "
            ],
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              "      <th>target</th>\n",
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              "      <td>12</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Note:** 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": "G0I6pKjthGhs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoding the line number feature to used with Model 5"
      ],
      "metadata": {
        "id": "Ufi1RJoP5pWT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Create positional embeddings"
      ],
      "metadata": {
        "id": "ttHPdMyihFi8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# How many different line numbers are there?\n",
        "train_df[\"line_number\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5IJWhdhheIC",
        "outputId": "c82f2dba-9737-4024-d37d-83fb1c24fef1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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: line_number, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the distribution of \"line_number\" column\n",
        "train_df.line_number.plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "ZsdsfxPfhmOj",
        "outputId": "8c28b231-cc08-45ec-b035-e651eb23c690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# User TensorFlow to create 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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzc0fnUhiCMp",
        "outputId": "503d304e-fda2-4975-dcae-57c2eecd2f3a"
      },
      "execution_count": null,
      "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": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoding the total lines feature to be used with Model 5\n",
        "\n",
        "Now we've encoded our line numbers feature, let's do the same for our total lines feature..."
      ],
      "metadata": {
        "id": "-lAHuAYNijk3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# How many different numbers of lines are there?\n",
        "train_df[\"total_lines\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iOP05iFjfdU",
        "outputId": "7c07c51f-2ae7-450a-d948-6f615218ae56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12    24468\n",
              "11    23639\n",
              "13    22113\n",
              "10    19400\n",
              "14    18438\n",
              "15    14610\n",
              "9     12285\n",
              "16    10768\n",
              "8      7464\n",
              "17     7429\n",
              "18     5202\n",
              "7      3353\n",
              "19     3344\n",
              "20     2480\n",
              "21     1281\n",
              "6      1146\n",
              "22      770\n",
              "23      759\n",
              "24      264\n",
              "5       215\n",
              "25      200\n",
              "26      182\n",
              "27       81\n",
              "29       58\n",
              "4        32\n",
              "31       31\n",
              "28       28\n",
              "Name: total_lines, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the distributions of total lines\n",
        "train_df.total_lines.plot.hist(bins=25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "nAfpcIyljpTp",
        "outputId": "84689b7a-7f11-47e0-9720-17dc60c481c8"
      },
      "execution_count": null,
      "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_lines\" value of 20\n",
        "np.percentile(train_df.total_lines, 97)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYTMEMT1j-eV",
        "outputId": "8c9c6acb-8b1f-44b8-e742-82fd9b9e9f79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.0"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use TensorFlow to create one-hot-encoded tensors of our \"total_lines\" feature\n",
        "train_total_lines_one_hot = tf.one_hot(train_df[\"total_lines\"].to_numpy(), depth=20)\n",
        "val_total_lines_one_hot = tf.one_hot(val_df[\"total_lines\"].to_numpy(), depth=20)\n",
        "test_total_lines_one_hot = tf.one_hot(test_df[\"total_lines\"].to_numpy(), depth=20)\n",
        "train_total_lines_one_hot.shape, train_total_lines_one_hot[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXM-v3rAkiWg",
        "outputId": "fe1159fa-686b-4d64-a37f-91edc508fdd6"
      },
      "execution_count": null,
      "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., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "         0., 0., 0., 0.]], dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 5: Building the foundations of a tribrid embedding model\n",
        "\n",
        "1. Create 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 1tf.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 to tribrid embedding and output label probabilities\n",
        "8. Combine the inputs of 1, 2, 3, 4 and the outputs of 7 into a tf.keras.Model\n"
      ],
      "metadata": {
        "id": "CHCp7-M4k4Un"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_line_numbers_one_hot[0].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yoRB6CyP2TEt",
        "outputId": "6d5b8545-5c19-4ed4-9d10-ab21d3c6a027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tf.float32"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Token inputs\n",
        "token_inputs = layers.Input(shape=[], dtype=\"string\", name=\"token_inputs\")\n",
        "token_embeddings = tf_hub_embedding_layer(token_inputs)\n",
        "token_outputs = layers.Dense(128, activation=\"relu\")(token_embeddings)\n",
        "token_model = tf.keras.Model(inputs=token_inputs,\n",
        "                             outputs=token_outputs)\n",
        "\n",
        "# 2. Char inputs\n",
        "char_inputs = layers.Input(shape=(1,), dtype=\"string\", name=\"char_inputs\")\n",
        "char_vectors = char_vectorizer(char_inputs)\n",
        "char_embeddings = char_embed(char_vectors)\n",
        "char_bi_lstm = layers.Bidirectional(layers.LSTM(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_input\")\n",
        "# dense layer with 32 units & relu activation\n",
        "x = layers.Dense(32, activation=\"relu\")(line_number_inputs)\n",
        "# combine inputs & dense layer into modle\n",
        "line_number_model = tf.keras.Model(inputs=line_number_inputs,\n",
        "                                   outputs=x)\n",
        "\n",
        "# 4. Total lines model\n",
        "total_lines_inputs = layers.Input(shape=(20,), dtype=tf.float32, name=\"total_lines_input\")\n",
        "total_lines_outputs = layers.Dense(32, activation=\"relu\")(total_lines_inputs)\n",
        "total_lines_model = tf.keras.Model(inputs=total_lines_inputs,\n",
        "                                   outputs=total_lines_outputs)\n",
        "\n",
        "# 5. Combine total and char embeddings into a hybrid embedding\n",
        "combined_embeddings = layers.Concatenate(name=\"char_token_hybrid_embedding\")([token_model.output,\n",
        "                                                                              char_model.output])\n",
        "z = layers.Dense(256, activation=\"relu\")(combined_embeddings)\n",
        "z = layers.Dropout(0.5)(z)\n",
        "\n",
        "# 6. Combine positional embeddings with combined token and char embeddings\n",
        "tribrid_embeddings = layers.Concatenate(name=\"char_token_positional_embedding\")([line_number_model.output,\n",
        "                                                                                 total_lines_model.output,\n",
        "                                                                                 z])\n",
        "\n",
        "# 7. Create output\n",
        "final_dropout = layers.Dropout(0.5)(tribrid_embeddings)\n",
        "output_layer = layers.Dense(5, activation=\"softmax\", name=\"output_layer\")(final_dropout)\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,\n",
        "                         name=\"model_5_tribrid_embedding_model\")"
      ],
      "metadata": {
        "id": "dX6XoQYrwFZj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a summary of our tribrid embedding model\n",
        "model_5.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_k6ir1ig45Rq",
        "outputId": "fc2e1d4e-a57d-45d0-bb9d-07f77af569af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5_tribrid_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 (TextVectoriza  (None, 290)         0           ['char_inputs[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " universal_sentence_encoder (Ke  (None, 512)         256797824   ['token_inputs[0][0]']           \n",
            " rasLayer)                                                                                        \n",
            "                                                                                                  \n",
            " char_embed (Embedding)         (None, 290, 25)      700         ['char_vectorizer[7][0]']        \n",
            "                                                                                                  \n",
            " dense_25 (Dense)               (None, 128)          65664       ['universal_sentence_encoder[7][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            " bidirectional_6 (Bidirectional  (None, 48)          9600        ['char_embed[7][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " char_token_hybrid_embedding (C  (None, 176)         0           ['dense_25[0][0]',               \n",
            " oncatenate)                                                      'bidirectional_6[0][0]']        \n",
            "                                                                                                  \n",
            " line_number_input (InputLayer)  [(None, 15)]        0           []                               \n",
            "                                                                                                  \n",
            " total_lines_input (InputLayer)  [(None, 20)]        0           []                               \n",
            "                                                                                                  \n",
            " dense_28 (Dense)               (None, 256)          45312       ['char_token_hybrid_embedding[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_26 (Dense)               (None, 32)           512         ['line_number_input[0][0]']      \n",
            "                                                                                                  \n",
            " dense_27 (Dense)               (None, 32)           672         ['total_lines_input[0][0]']      \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)            (None, 256)          0           ['dense_28[0][0]']               \n",
            "                                                                                                  \n",
            " char_token_positional_embeddin  (None, 320)         0           ['dense_26[0][0]',               \n",
            " g (Concatenate)                                                  'dense_27[0][0]',               \n",
            "                                                                  'dropout_5[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_6 (Dropout)            (None, 320)          0           ['char_token_positional_embedding\n",
            "                                                                 [0][0]']                         \n",
            "                                                                                                  \n",
            " output_layer (Dense)           (None, 5)            1605        ['dropout_6[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 256,921,889\n",
            "Trainable params: 124,065\n",
            "Non-trainable params: 256,797,824\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visually inspecting the architecture of our tribrid embedding model"
      ],
      "metadata": {
        "id": "BWXz6Vpv54nZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot model_5 to explore it visually...\n",
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model_5, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "lhLxWMco6PSB",
        "outputId": "723709b6-f780-4e6e-98c7-2f75006ce1d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is label smoothing?\n",
        "\n",
        "For example, if our model gets too confident on a single class (e.g. its prediction probability is really high), it may get stuck on that class and not consider other classes...\n",
        "\n",
        "Really confident; `[0.0, 0.0, 1.0, 0.0, 0.0]`\n",
        "\n",
        "What label smoothing does is it assigns some of the value from the highest pred prob to other classes, in turn, hopefully improving generalization: `[0.01, 0.01, 0.96, 0.01, 0.01]`\n",
        "\n",
        "> **Resource:** For more on label smoothing, see this blog post from PyImageSearch: https://pyimagesearch.com/2019/12/30/label-smoothing-with-keras-tensorflow-and-deep-learning/"
      ],
      "metadata": {
        "id": "AiT8TLbQ8-OP"
      }
    },
    {
      "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": "F2wCxxC26X9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating multi-level data input pipelines for Model 5 with the tf.data API"
      ],
      "metadata": {
        "id": "5hF-mM5O6aEr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create training and validation datasets (with all four kinds of input data)\n",
        "train_char_token_pos_data = 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_data, 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 as above but for the validation dataset\n",
        "val_char_token_pos_data = tf.data.Dataset.from_tensor_slices((val_line_numbers_one_hot,\n",
        "                                                              val_total_lines_one_hot,\n",
        "                                                              val_sentences,\n",
        "                                                              val_chars))\n",
        "val_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_data, val_char_token_pos_labels))\n",
        "val_char_token_pos_dataset = val_char_token_pos_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "# Check input shapes\n",
        "train_char_token_pos_dataset, val_char_token_pos_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoolPU-IBdiu",
        "outputId": "c30689ee-e336-43cf-bb4f-6c4ae725122b"
      },
      "execution_count": null,
      "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": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " ### Bringing SkimLit to life!!! (fitting and evaluating Model 5)"
      ],
      "metadata": {
        "id": "TI4CT2uJCbr5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit our tribrid embedding model\n",
        "history_model_5 = model_5.fit(train_char_token_pos_dataset,\n",
        "                              steps_per_epoch=int(0.1 * len(train_char_token_pos_dataset)),\n",
        "                              epochs=3,\n",
        "                              validation_data=val_char_token_pos_dataset,\n",
        "                              validation_steps=int(0.1 * len(val_char_token_pos_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "co4KGRPAHBBV",
        "outputId": "46d14274-b5ae-41b4-af72-27e26e6ddc5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "562/562 [==============================] - 56s 78ms/step - loss: 1.1522 - accuracy: 0.6871 - val_loss: 1.0037 - val_accuracy: 0.7952\n",
            "Epoch 2/3\n",
            "562/562 [==============================] - 47s 84ms/step - loss: 1.0170 - accuracy: 0.7873 - val_loss: 0.9635 - val_accuracy: 0.8168\n",
            "Epoch 3/3\n",
            "562/562 [==============================] - 28s 50ms/step - loss: 0.9967 - accuracy: 0.7995 - val_loss: 0.9520 - val_accuracy: 0.8251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions with the char token pos model\n",
        "model_5_pred_probs = model_5.predict(val_char_token_pos_dataset, verbose=1)\n",
        "model_5_pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iok6G5wiHYGM",
        "outputId": "e5042a9f-2691-4473-b73e-76adc8418b5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 28s 26ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.4912743 , 0.1135749 , 0.01364091, 0.35950255, 0.02200741],\n",
              "       [0.52507746, 0.10700551, 0.04034343, 0.31324342, 0.01433012],\n",
              "       [0.35307753, 0.09763614, 0.14522268, 0.33182707, 0.07223662],\n",
              "       ...,\n",
              "       [0.03561665, 0.08872724, 0.04767592, 0.03036189, 0.7976183 ],\n",
              "       [0.03620387, 0.20111594, 0.12032439, 0.03020381, 0.61215204],\n",
              "       [0.18716973, 0.54138863, 0.1398137 , 0.0409811 , 0.09064676]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert pred probs to pred labels\n",
        "model_5_preds = tf.argmax(model_5_pred_probs, axis=1)\n",
        "model_5_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Emb-yuvPHneg",
        "outputId": "b8959223-1f53-4248-d44a-049953ad3281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(30212,), dtype=int64, numpy=array([0, 0, 0, ..., 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate results of char token pos model\n",
        "model_5_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                    y_pred=model_5_preds)\n",
        "model_5_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0V7HtA4Ht9R",
        "outputId": "79e7928f-ebd5-4085-f740-adcbcec1e9cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 82.4208923606514,\n",
              " 'precision': 0.8236817437788542,\n",
              " 'recall': 0.824208923606514,\n",
              " 'f1': 0.8230459503001218}"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_4_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ex4WDmxLIRTm",
        "outputId": "47d38137-486d-4603-8f7e-e763e57b7a02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 73.66609294320138,\n",
              " 'precision': 0.7370983017201089,\n",
              " 'recall': 0.7366609294320138,\n",
              " 'f1': 0.7338978672305317}"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparing the performance of all of our modelling experiments"
      ],
      "metadata": {
        "id": "sH_2na--IVG_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine model results into a 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_pos_char_token_embedding\": model_5_results})\n",
        "all_model_results = all_model_results.transpose()\n",
        "all_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "dAKQnPwVTg7V",
        "outputId": "dc57ec29-67ce-4f61-8d43-c36f937b5051"
      },
      "execution_count": null,
      "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       80.176751   0.802658  0.801768  0.798906\n",
              "model_2_pretrained_token_embedding   71.402092   0.714330  0.714021  0.711086\n",
              "model_3_custom_char_embedding        64.878194   0.643153  0.648782  0.638188\n",
              "model_4_hybrid_char_token_embedding  73.666093   0.737098  0.736661  0.733898\n",
              "model_5_pos_char_token_embedding     82.420892   0.823682  0.824209  0.823046"
            ],
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              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
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              "      <td>72.183238</td>\n",
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              "      <td>0.801768</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Reduce the accuracy to same scale as other metricsj\n",
        "all_model_results[\"accuracy\"] = all_model_results[\"accuracy\"]/100"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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      "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))"
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        "colab": {
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort model results by f1-score\n",
        "all_model_results.sort_values(\"f1\", ascending=True)[\"f1\"].plot(kind=\"bar\", figsize=(10, 7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "2oih5fUXUly2",
        "outputId": "077d0b63-43a6-455e-e0a7-68aa736186d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 151
        },
        {
          "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": [
        "## Saving, loading & testing our best performing model"
      ],
      "metadata": {
        "id": "bwlQNSAjWnI_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the best performing model to SavedModel format (default)\n",
        "model_5.save(\"skimlit_tribrid_model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3A_v_TWZ7KH",
        "outputId": "88056bca-c084-4ebd-ad91-b5f460415796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_19_layer_call_fn, lstm_cell_19_layer_call_and_return_conditional_losses, lstm_cell_20_layer_call_fn, lstm_cell_20_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in best performing model\n",
        "loaded_model = tf.keras.models.load_model(\"skimlit_tribrid_model\")"
      ],
      "metadata": {
        "id": "lPdqpiDDaAii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions with the loaded model on the validation set\n",
        "loaded_pred_probs = loaded_model.predict(val_char_token_pos_dataset)\n",
        "loaded_preds = tf.argmax(loaded_pred_probs, axis=1)\n",
        "loaded_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IpZAg49qaXXi",
        "outputId": "ecb441db-8b5c-4ae0-c4fb-e2d89fe58d84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 29s 26ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int64, numpy=array([0, 0, 0, 2, 2, 4, 4, 4, 4, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the results of our loaded model\n",
        "loaded_model_results = calculate_results(y_true=val_labels_encoded,\n",
        "                                         y_pred=loaded_preds)\n",
        "loaded_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHpuSkyqarLO",
        "outputId": "cfb5a3d7-6b5c-40d0-be0f-66fe80640447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 82.4208923606514,\n",
              " 'precision': 0.8236817437788542,\n",
              " 'recall': 0.824208923606514,\n",
              " 'f1': 0.8230459503001218}"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_5_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a27YREyta4Gr",
        "outputId": "8cc0e17d-d351-4e0b-965c-fcb5741fc40d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 82.4208923606514,\n",
              " 'precision': 0.8236817437788542,\n",
              " 'recall': 0.824208923606514,\n",
              " 'f1': 0.8230459503001218}"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert model_5_results == loaded_model_results"
      ],
      "metadata": {
        "id": "3Ep6AL1Ka6Hj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the loaded model summary\n",
        "loaded_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sP2ZRg5LbZcc",
        "outputId": "49c6afd5-5bfc-4a19-e821-7bda2bb75c10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5_tribrid_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 (TextVectoriza  (None, 290)         0           ['char_inputs[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " universal_sentence_encoder (Ke  (None, 512)         256797824   ['token_inputs[0][0]']           \n",
            " rasLayer)                                                                                        \n",
            "                                                                                                  \n",
            " char_embed (Embedding)         (None, 290, 25)      700         ['char_vectorizer[0][0]']        \n",
            "                                                                                                  \n",
            " dense_25 (Dense)               (None, 128)          65664       ['universal_sentence_encoder[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            " bidirectional_6 (Bidirectional  (None, 48)          9600        ['char_embed[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " char_token_hybrid_embedding (C  (None, 176)         0           ['dense_25[0][0]',               \n",
            " oncatenate)                                                      'bidirectional_6[0][0]']        \n",
            "                                                                                                  \n",
            " line_number_input (InputLayer)  [(None, 15)]        0           []                               \n",
            "                                                                                                  \n",
            " total_lines_input (InputLayer)  [(None, 20)]        0           []                               \n",
            "                                                                                                  \n",
            " dense_28 (Dense)               (None, 256)          45312       ['char_token_hybrid_embedding[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_26 (Dense)               (None, 32)           512         ['line_number_input[0][0]']      \n",
            "                                                                                                  \n",
            " dense_27 (Dense)               (None, 32)           672         ['total_lines_input[0][0]']      \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)            (None, 256)          0           ['dense_28[0][0]']               \n",
            "                                                                                                  \n",
            " char_token_positional_embeddin  (None, 320)         0           ['dense_26[0][0]',               \n",
            " g (Concatenate)                                                  'dense_27[0][0]',               \n",
            "                                                                  'dropout_5[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_6 (Dropout)            (None, 320)          0           ['char_token_positional_embedding\n",
            "                                                                 [0][0]']                         \n",
            "                                                                                                  \n",
            " output_layer (Dense)           (None, 5)            1605        ['dropout_6[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 256,921,889\n",
            "Trainable params: 124,065\n",
            "Non-trainable params: 256,797,824\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Congratulations and your challenge before heading to the next module"
      ],
      "metadata": {
        "id": "BitBtiIDbf1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization"
      ],
      "metadata": {
        "id": "TTLXPxHvd68w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!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": "IbV0LmlUejxp",
        "outputId": "6a79d3be-2da9-42a6-fffb-e45a69aa30b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-07-08 21:49:10--  https://storage.googleapis.com/ztm_tf_course/skimlit/skimlit_tribrid_model.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.194.128, 142.250.4.128, 74.125.200.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.194.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 962182847 (918M) [application/zip]\n",
            "Saving to: ‘skimlit_tribrid_model.zip’\n",
            "\n",
            "skimlit_tribrid_mod 100%[===================>] 917.61M  23.1MB/s    in 42s     \n",
            "\n",
            "2023-07-08 21:49:52 (22.0 MB/s) - ‘skimlit_tribrid_model.zip’ saved [962182847/962182847]\n",
            "\n",
            "Archive:  skimlit_tribrid_model.zip\n",
            "   creating: skimlit_gs_model/skimlit_tribrid_model/\n",
            "  inflating: skimlit_gs_model/skimlit_tribrid_model/keras_metadata.pb  \n",
            "   creating: skimlit_gs_model/skimlit_tribrid_model/assets/\n",
            " extracting: skimlit_gs_model/skimlit_tribrid_model/fingerprint.pb  \n",
            "   creating: skimlit_gs_model/skimlit_tribrid_model/variables/\n",
            "  inflating: skimlit_gs_model/skimlit_tribrid_model/variables/variables.index  \n",
            "  inflating: skimlit_gs_model/skimlit_tribrid_model/variables/variables.data-00000-of-00001  \n",
            "  inflating: skimlit_gs_model/skimlit_tribrid_model/saved_model.pb  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in downloaded Google Storage model\n",
        "loaded_gs_model = tf.keras.models.load_model(\"skimlit_gs_model/skimlit_tribrid_model\")"
      ],
      "metadata": {
        "id": "gfHrS5A2euyb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "ChGVI96zfB2E",
        "outputId": "02a9c515-e80f-4134-e830-38d4e6393120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "945/945 [==============================] - 24s 23ms/step - loss: 0.9387 - accuracy: 0.8307\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9387035369873047, 0.8307294845581055]"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Your challenges\n",
        "\n",
        "1. Turn the test data samples into a tf.data Dataset (fast loading) and then evaluate (make predictions) the best performing model on the test samples.\n",
        "2. Find the most wrong predictions from 1 (these are the samples where the model has predicted the wrong label with the highest prediction probability).\n",
        "3. Make example predictions (on RCT abstracts from the wild), you can go to PubMed to find these: https://pubmed.ncbi.nlm.nih.gov/, find and use our model to predict on 3-4 different abstracts from the wild.\n",
        "  * Some examples: https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/extras/skimlit_example_abstracts.json\n",
        "4. See the exercises and extra curriculum section on GitHub to further test your skills (for section 09)."
      ],
      "metadata": {
        "id": "viE7zrlXfN2_"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L-su52chgAJP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}