ClassificationProj.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/brianferrell787/Classifying-community-engaged-research-with-transformer-based-models/blob/main/ClassificationProj.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# **Upload the two training data files, and hold out dataset**\n"
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    },
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "X3xHKGjxo8my",
        "outputId": "5fa51566-21f8-4639-8602-1e594bda4a10"
      },
      "source": [
        "#Upload zip file turned in\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-89ddd408-23e9-409b-b7ff-d990be0b15b3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-89ddd408-23e9-409b-b7ff-d990be0b15b3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving DataforProject.zip to DataforProject.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnpxdng1pGJu"
      },
      "source": [
        "!unzip \"/content/DataforProject.zip\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YZo1R_aMRml"
      },
      "source": [
        "# **Install packages and other various tools**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfUsVaYzo8P-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16fc6a4e-2987-4c75-f89e-b92eab58b206"
      },
      "source": [
        "#GPU information \n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Jan  5 14:18:52 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.27.04    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P0    24W / 300W |      0MiB / 16130MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lh_g5EiLAVzh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b9721e91-5f61-4639-c165-7cf98d86bb79"
      },
      "source": [
        "pip install simpletransformers"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting simpletransformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/8f/4607b72b25ded04835b2282b401c904e24da873e934a0e976b2debe27a41/simpletransformers-0.51.10-py3-none-any.whl (201kB)\n",
            "\u001b[K     |████████████████████████████████| 204kB 12.6MB/s \n",
            "\u001b[?25hCollecting wandb\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/5e/9df94df3bfee51b92b54a5e6fa277d6e1fcdf1f27b1872214b98f55ec0f7/wandb-0.10.12-py2.py3-none-any.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 21.8MB/s \n",
            "\u001b[?25hCollecting tensorboardx\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/af/0c/4f41bcd45db376e6fe5c619c01100e9b7531c55791b7244815bac6eac32c/tensorboardX-2.1-py2.py3-none-any.whl (308kB)\n",
            "\u001b[K     |████████████████████████████████| 317kB 60.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (1.4.1)\n",
            "Collecting streamlit\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/dd/8d/4c7676d01e90852254e2275fb4639b747274430f2fa066aa94848d3a6ee4/streamlit-0.73.1-py2.py3-none-any.whl (7.4MB)\n",
            "\u001b[K     |████████████████████████████████| 7.4MB 61.7MB/s \n",
            "\u001b[?25hCollecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/2d/6d4ca4bef9a67070fa1cac508606328329152b1df10bdf31fb6e4e727894/sentencepiece-0.1.94-cp36-cp36m-manylinux2014_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 67.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (1.19.4)\n",
            "Collecting tqdm>=4.47.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ac/a2/1fc6ceff232a4755fa85e2bd3aaca1145c3103b298000129381309995072/tqdm-4.55.1-py2.py3-none-any.whl (68kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.9MB/s \n",
            "\u001b[?25hCollecting seqeval\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz (43kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 7.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (1.1.5)\n",
            "Collecting transformers>=4.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/0c/7d5950fcd80b029be0a8891727ba21e0cd27692c407c51261c3c921f6da3/transformers-4.1.1-py3-none-any.whl (1.5MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5MB 50.7MB/s \n",
            "\u001b[?25hCollecting tokenizers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 51.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (2.23.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (0.22.2.post1)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (2019.12.20)\n",
            "Collecting configparser>=3.8.1\n",
            "  Downloading https://files.pythonhosted.org/packages/08/b2/ef713e0e67f6e7ec7d59aea3ee78d05b39c15930057e724cc6d362a8c3bb/configparser-5.0.1-py3-none-any.whl\n",
            "Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.6/dist-packages (from wandb->simpletransformers) (3.12.4)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/c8/564be4d12629b912ea431f1a50eb8b3b9d00f1a0b1ceff17f266be190007/subprocess32-3.5.4.tar.gz (97kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/24/d1/a7f8fe3df258549b303415157328bfcc63e9b11d06a7ad7a3327f3d32606/GitPython-3.1.11-py3-none-any.whl (159kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/3c/03/58572025c77b9e6027155b272a1b96298e711cd4f95c24967f7137ab0c4b/base58-2.0.1-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
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            "Collecting ipykernel>=5.1.2; python_version >= \"3.4\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/56/c5/97a972202414c79c28814b1450a31a89777638fb3524e8b5b686a5436abe/ipykernel-5.4.2-py3-none-any.whl (119kB)\n",
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            "Collecting smmap<4,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/b0/9a/4d409a6234eb940e6a78dfdfc66156e7522262f5f2fecca07dc55915952d/smmap-3.0.4-py2.py3-none-any.whl\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.1)\n",
            "Building wheels for collected packages: seqeval, subprocess32, blinker, sacremoses\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-cp36-none-any.whl size=16171 sha256=bddcabc2ef6d9119f71b88f5034d909da32a550b4b625c572cbb81b6c5f6f8fd\n",
            "  Stored in directory: /root/.cache/pip/wheels/52/df/1b/45d75646c37428f7e626214704a0e35bd3cfc32eda37e59e5f\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp36-none-any.whl size=6490 sha256=dbfbebb339ed1798d0349fd79c178ab47324fe25168ac8690614237a7313100d\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
            "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for blinker: filename=blinker-1.4-cp36-none-any.whl size=13450 sha256=be0647454b12aaff528d3ada1470576f778e4c84c52f7d3a2cd83a6bc4eff043\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/a0/00/8690a57883956a301d91cf4ec999cc0b258b01e3f548f86e89\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=3323d71b92fb9409b1853ead0521c5b87a7acba637245b289070a72b6a585b65\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built seqeval subprocess32 blinker sacremoses\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.4.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: configparser, docker-pycreds, subprocess32, sentry-sdk, watchdog, shortuuid, smmap, gitdb, GitPython, wandb, tensorboardx, blinker, ipykernel, pydeck, base58, validators, streamlit, sentencepiece, tqdm, seqeval, sacremoses, tokenizers, transformers, simpletransformers\n",
            "  Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "  Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "Successfully installed GitPython-3.1.11 base58-2.0.1 blinker-1.4 configparser-5.0.1 docker-pycreds-0.4.0 gitdb-4.0.5 ipykernel-5.4.2 pydeck-0.5.0 sacremoses-0.0.43 sentencepiece-0.1.94 sentry-sdk-0.19.5 seqeval-1.2.2 shortuuid-1.0.1 simpletransformers-0.51.10 smmap-3.0.4 streamlit-0.73.1 subprocess32-3.5.4 tensorboardx-2.1 tokenizers-0.9.4 tqdm-4.55.1 transformers-4.1.1 validators-0.18.2 wandb-0.10.12 watchdog-1.0.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "ipykernel"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEOLVc7oMYFG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0822d81-18c9-435e-98fb-365a7f79979a"
      },
      "source": [
        "# WandB – Install the W&B library -- You don't need this unless you want to see the wandb graph but you have to create a account \n",
        "!pip install wandb --upgrade"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: wandb in /usr/local/lib/python3.6/dist-packages (0.10.12)\n",
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            "Requirement already satisfied, skipping upgrade: six>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from wandb) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: Click>=7.0 in /usr/local/lib/python3.6/dist-packages (from wandb) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: psutil>=5.0.0 in /usr/local/lib/python3.6/dist-packages (from wandb) (5.4.8)\n",
            "Requirement already satisfied, skipping upgrade: gitdb<5,>=4.0.1 in /usr/local/lib/python3.6/dist-packages (from GitPython>=1.0.0->wandb) (4.0.5)\n",
            "Requirement already satisfied, skipping upgrade: urllib3>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from sentry-sdk>=0.4.0->wandb) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: certifi in /usr/local/lib/python3.6/dist-packages (from sentry-sdk>=0.4.0->wandb) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.12.0->wandb) (51.0.0)\n",
            "Requirement already satisfied, skipping upgrade: smmap<4,>=3.0.1 in /usr/local/lib/python3.6/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (3.0.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmzMyPLMak-A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d2c5310-1d36-4b78-d394-7aedf213af89"
      },
      "source": [
        "import matplotlib as plt\n",
        "\n",
        "# NLTK\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.stem import WordNetLemmatizer \n",
        "nltk.download('wordnet')\n",
        "# Other\n",
        "import re\n",
        "import string\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCl7QHp6I2TQ"
      },
      "source": [
        "### **Upload JSON file and Excel file for training data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrrNG-X4GAGv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "6461f245-7682-4323-8117-a07d3ab2a368"
      },
      "source": [
        "train1_df = pd.read_excel('training_df.xlsx')\n",
        "train1_df=train1_df.fillna(\"\")\n",
        "train1_df = shuffle(train1_df)\n",
        "train1_df"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AimsGoal</th>\n",
              "      <th>Hypothesis</th>\n",
              "      <th>Study Design</th>\n",
              "      <th>Background</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2106</th>\n",
              "      <td>building capacity for children ' s involvement...</td>\n",
              "      <td>this study will use an interpretivist , qualit...</td>\n",
              "      <td>study design : this is a qualitative interpret...</td>\n",
              "      <td>despite legislative initiatives supporting chi...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1709</th>\n",
              "      <td>whole - school learning in an urban middle sch...</td>\n",
              "      <td>this survey will examine the process and resul...</td>\n",
              "      <td>in keeping with the nature of the data , the c...</td>\n",
              "      <td>the initiative was instigated by a proposal ma...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>703</th>\n",
              "      <td>implementation and evaluation of a 24 - hour a...</td>\n",
              "      <td>secondly : it will be important to establish a...</td>\n",
              "      <td>design : prospective , single - arm , non - ra...</td>\n",
              "      <td>hypertension ( htn ) is the leading mortality ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>inclusion project : surveys to students in vir...</td>\n",
              "      <td>h1 : the inclusion project will raise awarenes...</td>\n",
              "      <td>in conjunction with project staff , the evalua...</td>\n",
              "      <td>inclusion project is a multi - phase project f...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1703</th>\n",
              "      <td>inter - school literacy in an urban middle sch...</td>\n",
              "      <td>this article will examine the process and effe...</td>\n",
              "      <td>in keeping with the nature of the data , the c...</td>\n",
              "      <td>the study was instigated by a request made by ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1548</th>\n",
              "      <td>to end consumer health outreach and project ac...</td>\n",
              "      <td>participants through online application resear...</td>\n",
              "      <td>consistent with a community - based participat...</td>\n",
              "      <td>at 12 % proficiency , u . s . health literacy ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1105</th>\n",
              "      <td>relationship between exposure to anesthetic dr...</td>\n",
              "      <td>multiple studies have been conducted examining...</td>\n",
              "      <td>we plan on examining the school enrollment rec...</td>\n",
              "      <td>fatalities resulting from drug overdose have d...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1364</th>\n",
              "      <td>the impact of human - animal interaction on ad...</td>\n",
              "      <td>hypothesis : aims 2 . we hypothesize that chil...</td>\n",
              "      <td>breif overview of method for each aim ( detail...</td>\n",
              "      <td>alzheimers and related diseases data shows an ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1912</th>\n",
              "      <td>inclusion projects : outreach to schools in vi...</td>\n",
              "      <td>h1 : the action project will raise awareness i...</td>\n",
              "      <td>in conjunction with project staff , the evalua...</td>\n",
              "      <td>integration project is a three - year project ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2142</th>\n",
              "      <td>community partners working with richmond healt...</td>\n",
              "      <td>this page will contain lists of biomedical pro...</td>\n",
              "      <td>qualitative interviews will be used in this ph...</td>\n",
              "      <td>characterized by an aging and increasingly com...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2627 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               AimsGoal  ... Class\n",
              "2106  building capacity for children ' s involvement...  ...     5\n",
              "1709  whole - school learning in an urban middle sch...  ...     5\n",
              "703   implementation and evaluation of a 24 - hour a...  ...     3\n",
              "2613  inclusion project : surveys to students in vir...  ...     5\n",
              "1703  inter - school literacy in an urban middle sch...  ...     5\n",
              "...                                                 ...  ...   ...\n",
              "1548  to end consumer health outreach and project ac...  ...     5\n",
              "1105  relationship between exposure to anesthetic dr...  ...     4\n",
              "1364  the impact of human - animal interaction on ad...  ...     4\n",
              "1912  inclusion projects : outreach to schools in vi...  ...     5\n",
              "2142  community partners working with richmond healt...  ...     5\n",
              "\n",
              "[2627 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYE2Y-4oJgGi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "7e00974f-afd5-479a-b12e-2914d943afef"
      },
      "source": [
        "train2_df = pd.read_json('/content/training_df7k.json')\n",
        "train2_df = shuffle(train2_df)\n",
        "train2_df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6124</th>\n",
              "      <td>to end consumer literacy outreach and project ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4029</th>\n",
              "      <td>community engagement education program - 2 , a...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3290</th>\n",
              "      <td>examples of effective research about mental il...</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>incentives for primary care use : a randomized...</td>\n",
              "      <td>0</td>\n",
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              "      <th>5651</th>\n",
              "      <td>agri - cultureo : growing food and community w...</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>2001</th>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4266</th>\n",
              "      <td>assessment of health needs : a community based...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>516</th>\n",
              "      <td>enhancing at - risk latina participant ' s pra...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3916</th>\n",
              "      <td>level of awareness for students and adolescent...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4960</th>\n",
              "      <td>patients perceptions about compliance package ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6785 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   Text  Class\n",
              "6124  to end consumer literacy outreach and project ...      5\n",
              "4029  community engagement education program - 2 , a...      2\n",
              "3290  examples of effective research about mental il...      2\n",
              "107   incentives for primary care use : a randomized...      0\n",
              "5651  agri - cultureo : growing food and community w...      4\n",
              "...                                                 ...    ...\n",
              "2001                                                         0\n",
              "4266  assessment of health needs : a community based...      2\n",
              "516   enhancing at - risk latina participant ' s pra...      0\n",
              "3916  level of awareness for students and adolescent...      2\n",
              "4960  patients perceptions about compliance package ...      3\n",
              "\n",
              "[6785 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQ22EQXAKETb"
      },
      "source": [
        "## **Combine columns for the second training set to match first and combine both dataframes for more data**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Df0SnfippTor"
      },
      "source": [
        "I have two files for training data from augmenting it so that is why I am combining them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErcwJ-nc6wDw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "48b1e6c7-bc97-4ce9-8042-adba8f26335f"
      },
      "source": [
        "#This adds the columns\n",
        "train1_df['Text'] = train1_df['AimsGoal'] + train1_df['Hypothesis'] + train1_df['Background'] + train1_df['Study Design'] \n",
        "\n",
        "\n",
        "#This creates a new dataframes of the labels and train_df2 which will later be combined with the other training set just to have more data.. it is not necessary \n",
        "classes = pd.DataFrame(train1_df['Class'])\n",
        "trainingset_2 = pd.DataFrame(train1_df['Text'])\n",
        "trainingset_2['Class'] = classes\n",
        "\n",
        "trainingset_2"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Class</th>\n",
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              "  </thead>\n",
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              "      <th>2106</th>\n",
              "      <td>building capacity for children ' s involvement...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1709</th>\n",
              "      <td>whole - school learning in an urban middle sch...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>703</th>\n",
              "      <td>implementation and evaluation of a 24 - hour a...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>inclusion project : surveys to students in vir...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1703</th>\n",
              "      <td>inter - school literacy in an urban middle sch...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1548</th>\n",
              "      <td>to end consumer health outreach and project ac...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1105</th>\n",
              "      <td>relationship between exposure to anesthetic dr...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1364</th>\n",
              "      <td>the impact of human - animal interaction on ad...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1912</th>\n",
              "      <td>inclusion projects : outreach to schools in vi...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2142</th>\n",
              "      <td>community partners working with richmond healt...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2627 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   Text  Class\n",
              "2106  building capacity for children ' s involvement...      5\n",
              "1709  whole - school learning in an urban middle sch...      5\n",
              "703   implementation and evaluation of a 24 - hour a...      3\n",
              "2613  inclusion project : surveys to students in vir...      5\n",
              "1703  inter - school literacy in an urban middle sch...      5\n",
              "...                                                 ...    ...\n",
              "1548  to end consumer health outreach and project ac...      5\n",
              "1105  relationship between exposure to anesthetic dr...      4\n",
              "1364  the impact of human - animal interaction on ad...      4\n",
              "1912  inclusion projects : outreach to schools in vi...      5\n",
              "2142  community partners working with richmond healt...      5\n",
              "\n",
              "[2627 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8n7GrlJLNxb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "4ec80158-bb88-4577-91ee-65a7ff3beabc"
      },
      "source": [
        "train_df = pd.concat([trainingset_2, train2_df], axis=0, sort=False)\n",
        "train_df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>2613</th>\n",
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              "      <td>5</td>\n",
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              "      <th>1703</th>\n",
              "      <td>inter - school literacy in an urban middle sch...</td>\n",
              "      <td>5</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>2001</th>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4266</th>\n",
              "      <td>assessment of health needs : a community based...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>516</th>\n",
              "      <td>enhancing at - risk latina participant ' s pra...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3916</th>\n",
              "      <td>level of awareness for students and adolescent...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4960</th>\n",
              "      <td>patients perceptions about compliance package ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9412 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   Text  Class\n",
              "2106  building capacity for children ' s involvement...      5\n",
              "1709  whole - school learning in an urban middle sch...      5\n",
              "703   implementation and evaluation of a 24 - hour a...      3\n",
              "2613  inclusion project : surveys to students in vir...      5\n",
              "1703  inter - school literacy in an urban middle sch...      5\n",
              "...                                                 ...    ...\n",
              "2001                                                         0\n",
              "4266  assessment of health needs : a community based...      2\n",
              "516   enhancing at - risk latina participant ' s pra...      0\n",
              "3916  level of awareness for students and adolescent...      2\n",
              "4960  patients perceptions about compliance package ...      3\n",
              "\n",
              "[9412 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvGpd6RwGEoS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c368345-c2cc-4ce3-ff22-c0b9a8395143"
      },
      "source": [
        "unique = train_df.groupby('Class').nunique()\n",
        "\n",
        "print (unique)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       Text\n",
            "Class      \n",
            "0      1931\n",
            "1      1427\n",
            "2      1413\n",
            "3      1564\n",
            "4      1431\n",
            "5      1404\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8dB1WzJnNjMi"
      },
      "source": [
        "## **Find and replace Nan/NULL values**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPzJ3eSrWQiy"
      },
      "source": [
        "train_df=train_df.fillna(\"\")\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9deO2iIVwXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76821bd7-b575-4b90-d8c2-d5d34306a5fc"
      },
      "source": [
        "train_df.isnull().any()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text     False\n",
              "Class    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drJljjK51EqF"
      },
      "source": [
        "## **Change the training values to a less rigid classification spectrum(n = 3 intead of 5)**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1jBCnQ71oD5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "68b04408-6770-490c-9401-04de7a9a868b"
      },
      "source": [
        "train_df['Class'] = train_df['Class'].replace([2],1)\n",
        "train_df['Class'] = train_df['Class'].replace([3],2)\n",
        "train_df['Class'] = train_df['Class'].replace([5],4)\n",
        "train_df['Class'] = train_df['Class'].replace([4],3)\n",
        "train_df['Class'] = train_df['Class'].replace([3],2)\n",
        "train_df"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2106</th>\n",
              "      <td>building capacity for children ' s involvement...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1709</th>\n",
              "      <td>whole - school learning in an urban middle sch...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>703</th>\n",
              "      <td>implementation and evaluation of a 24 - hour a...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>inclusion project : surveys to students in vir...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1703</th>\n",
              "      <td>inter - school literacy in an urban middle sch...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4266</th>\n",
              "      <td>assessment of health needs : a community based...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>516</th>\n",
              "      <td>enhancing at - risk latina participant ' s pra...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3916</th>\n",
              "      <td>level of awareness for students and adolescent...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4960</th>\n",
              "      <td>patients perceptions about compliance package ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9412 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   Text  Class\n",
              "2106  building capacity for children ' s involvement...      2\n",
              "1709  whole - school learning in an urban middle sch...      2\n",
              "703   implementation and evaluation of a 24 - hour a...      2\n",
              "2613  inclusion project : surveys to students in vir...      2\n",
              "1703  inter - school literacy in an urban middle sch...      2\n",
              "...                                                 ...    ...\n",
              "2001                                                         0\n",
              "4266  assessment of health needs : a community based...      1\n",
              "516   enhancing at - risk latina participant ' s pra...      0\n",
              "3916  level of awareness for students and adolescent...      1\n",
              "4960  patients perceptions about compliance package ...      2\n",
              "\n",
              "[9412 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1KEdKasN-nc"
      },
      "source": [
        "## **Preproccessing steps(cleaning, lemmatizing, stopword removal, padding, tokenization, etc**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZlXzF1bqpXl"
      },
      "source": [
        "I also have to combine the columns for the hold out data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqwoUa6k6aF4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e909a72-c248-461e-c858-6a5c215dbfe1"
      },
      "source": [
        "import nltk\n",
        "import re  \n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JptJY2fL2sAQ"
      },
      "source": [
        "def clean_text(text):\n",
        "    \n",
        "    ## Remove puncuation\n",
        "    text = text.translate(string.punctuation)\n",
        "    \n",
        "    ## Convert words to lower case and split them\n",
        "    text = text.lower().split()\n",
        "    \n",
        "    ## Remove stop words\n",
        "    nltk.download('stopwords')\n",
        "    stops = set(stopwords.words(\"english\"))\n",
        "    text = [w for w in text if not w in stops and len(w) >= 3]\n",
        "    \n",
        "    text = \" \".join(text)\n",
        "    ## Clean the text\n",
        "    text = re.sub(r\"[^A-Za-z0-9^,!.\\/'+-=]\", \" \", text)\n",
        "    text = re.sub(r\"what's\", \"what is \", text)\n",
        "    text = re.sub(r\"\\'s\", \" \", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
        "    text = re.sub(r\"n't\", \" not \", text)\n",
        "    text = re.sub(r\"i'm\", \"i am \", text)\n",
        "    text = re.sub(r\"\\'re\", \" are \", text)\n",
        "    text = re.sub(r\"\\'d\", \" would \", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
        "    text = re.sub(r\",\", \" \", text)\n",
        "    text = re.sub(r\"\\.\", \" \", text)\n",
        "    text = re.sub(r\"!\", \"  \", text)\n",
        "    text = re.sub(r\"=\", \"  \", text)\n",
        "    text = re.sub(r\"\\/\", \" \", text)\n",
        "    text = re.sub(r\"\\^\", \" ^ \", text)\n",
        "    text = re.sub(r\"\\+\", \" + \", text)\n",
        "    text = re.sub(r\"\\-\", \" - \", text)\n",
        "    text = re.sub(r\"\\=\", \" = \", text)\n",
        "    text = re.sub(r\"'\", \" \", text)\n",
        "    text = re.sub(r\"(\\d+)(k)\", r\"\\g<1>000\", text)\n",
        "    text = re.sub(r\":\", \"  \", text)\n",
        "    text = re.sub(r\";\", \"  \", text)\n",
        "    text = re.sub(r\"http\", \"  \", text)\n",
        "    text = re.sub(r\"www\", \"  \", text)\n",
        "    text = re.sub(r\"-\", \"  \", text)\n",
        "    text = re.sub(r\" e g \", \" eg \", text)\n",
        "    text = re.sub(r\" b g \", \" bg \", text)\n",
        "    text = re.sub(r\"\\0s\", \"0\", text)\n",
        "    text = re.sub(r\" 9 11 \", \"911\", text)\n",
        "    text = re.sub(r\"e - mail\", \"email\", text)\n",
        "    text = re.sub(r\"j k\", \"jk\", text)\n",
        "    text = re.sub(r\"\\s{2,}\", \" \", text)\n",
        "    text = re.sub(r\"\\d+\", \" \", text)\n",
        "\n",
        "    ## Lemmatize\n",
        "    text = text.split()\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemma_words = [lemmatizer.lemmatize(word) for word in text]\n",
        "    text = \" \".join(lemma_words)\n",
        "\n",
        "    return text\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUEFtSln2sKK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "583d231e-7836-473c-b0a7-a53c81e0d9a3"
      },
      "source": [
        "# apply the above function to df['text']\n",
        "traintext = train_df['Text'].map(lambda x: clean_text(x))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
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            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91EI5udD2sHW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "932e3971-619d-4aa8-86fb-ecdab3b83e0e"
      },
      "source": [
        "traintext"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2106    building capacity child involvement child welf...\n",
              "1709    whole school learning urban middle school stud...\n",
              "703     implementation evaluation hour ambulatory bloo...\n",
              "2613    inclusion project survey student virginia help...\n",
              "1703    inter school literacy urban middle school stud...\n",
              "                              ...                        \n",
              "2001                                                     \n",
              "4266    assessment health need community based approac...\n",
              "516     enhancing risk latina participant practice gen...\n",
              "3916    level awareness student adolescent autism atte...\n",
              "4960    patient perception compliance package usage co...\n",
              "Name: Text, Length: 9412, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7VN5ALHWEwm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa21cdb4-4f33-4934-e719-14ca609748df"
      },
      "source": [
        "test_df = pd.read_excel('Hold Out data.xlsx' )\n",
        "test_df =test_df.fillna(\"\")\n",
        "test_df['New'] = test_df['AimsGoal'] + test_df['Hypothesis'] + test_df['Background'] + test_df['Study Design']\n",
        "test_df['New'] =test_df['New'].astype(str)\n",
        "valtext = test_df['New'].map(lambda x: clean_text(x))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-XXMzEG71LX"
      },
      "source": [
        "## **Turning test set into 3 class spectrum**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dY4YmjQ2rHD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "fc8f308f-d8ee-4601-fc2f-13b971cbe43b"
      },
      "source": [
        "test_df['Class'] = test_df['Class'].replace([2],1)\n",
        "test_df['Class'] = test_df['Class'].replace([3],2)\n",
        "test_df['Class'] = test_df['Class'].replace([5],4)\n",
        "test_df['Class'] = test_df['Class'].replace([4],3)\n",
        "test_df['Class'] = test_df['Class'].replace([3],2)\n",
        "test_df"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>AimsGoal</th>\n",
              "      <th>Hypothesis</th>\n",
              "      <th>Background</th>\n",
              "      <th>Study Design</th>\n",
              "      <th>Class</th>\n",
              "      <th>New</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>HM20013139</td>\n",
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              "      <td>The primary aim of this pilot feasibility stud...</td>\n",
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              "      <td>Participants will report increased performance...</td>\n",
              "      <td>The vast majority of United States homeowners ...</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
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              "      <td>HM20014813</td>\n",
              "      <td>Cultural Diversity Within Schools Survey , The...</td>\n",
              "      <td>The research questions guiding this study are:...</td>\n",
              "      <td>The purpose of this survey is to better unders...</td>\n",
              "      <td>Study design: The proposed study will use a su...</td>\n",
              "      <td>2</td>\n",
              "      <td>Cultural Diversity Within Schools Survey , The...</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Research questionsHow do parents of children w...</td>\n",
              "      <td>Approximately 10% of children under age 5 have...</td>\n",
              "      <td>The overarching, tiered approach to identifica...</td>\n",
              "      <td>0</td>\n",
              "      <td>Stakeholder Perspectives on Social Needs Scree...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HM20011134</td>\n",
              "      <td>Development of Psychosocial Need Assessment fo...</td>\n",
              "      <td>The proposed study will develop a reliable, va...</td>\n",
              "      <td>Cystic fibrosis (CF) places children and adole...</td>\n",
              "      <td>The study will be longitudinal in nature, cons...</td>\n",
              "      <td>0</td>\n",
              "      <td>Development of Psychosocial Need Assessment fo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>HM20002308</td>\n",
              "      <td>Virginia's National Core Indicator Project Sec...</td>\n",
              "      <td>.General research questions will include:ò Wha...</td>\n",
              "      <td>National Core Indicators (NCI) is a collaborat...</td>\n",
              "      <td>This study will be a secondary data analysis o...</td>\n",
              "      <td>1</td>\n",
              "      <td>Virginia's National Core Indicator Project Sec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>HM15211</td>\n",
              "      <td>Identifying Medication-Related Issues with Tra...</td>\n",
              "      <td>Dominion Place, located in Richmond, Virginia,...</td>\n",
              "      <td></td>\n",
              "      <td>Background information on transitions of care ...</td>\n",
              "      <td>2</td>\n",
              "      <td>Identifying Medication-Related Issues with Tra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>HM20003653</td>\n",
              "      <td>Reporting Intimate Violence to Law Enforcement...</td>\n",
              "      <td>Little is known about when and how African Ame...</td>\n",
              "      <td>The proposed study focuses on developing multi...</td>\n",
              "      <td>To address the dearth of knowledge of African ...</td>\n",
              "      <td>1</td>\n",
              "      <td>Reporting Intimate Violence to Law Enforcement...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>HM20014642</td>\n",
              "      <td>Barriers to care in the diagnosis and treatmen...</td>\n",
              "      <td>We hypothesize that women who have barriers to...</td>\n",
              "      <td>Despite improved screening programs and vaccin...</td>\n",
              "      <td>This is a descriptive, observational study inv...</td>\n",
              "      <td>1</td>\n",
              "      <td>Barriers to care in the diagnosis and treatmen...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>HM20011964</td>\n",
              "      <td>Accountable Health Communities , The Accountab...</td>\n",
              "      <td>The Centers for Medicare and Medicaid Services...</td>\n",
              "      <td>Before describing this studys background and s...</td>\n",
              "      <td>Before describing this studys background and s...</td>\n",
              "      <td>2</td>\n",
              "      <td>Accountable Health Communities , The Accountab...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>309 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             ID  ...                                                New\n",
              "0    HM20013139  ...  Identifying and Prioritizing Patient Goals Bef...\n",
              "1    HM20003563  ...  The Impact of Home Modifications on the Safety...\n",
              "2    HM20014813  ...  Cultural Diversity Within Schools Survey , The...\n",
              "3    HM20010722  ...  Stakeholder Perspectives on Social Needs Scree...\n",
              "4    HM20011134  ...  Development of Psychosocial Need Assessment fo...\n",
              "..          ...  ...                                                ...\n",
              "304  HM20002308  ...  Virginia's National Core Indicator Project Sec...\n",
              "305     HM15211  ...  Identifying Medication-Related Issues with Tra...\n",
              "306  HM20003653  ...  Reporting Intimate Violence to Law Enforcement...\n",
              "307  HM20014642  ...  Barriers to care in the diagnosis and treatmen...\n",
              "308  HM20011964  ...  Accountable Health Communities , The Accountab...\n",
              "\n",
              "[309 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xNOCYRo7_-T"
      },
      "source": [
        "## **Last few steps to format the data into what SimpleTransformer's needs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fyrmm-DCMcu5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "969a16fb-f1fb-49f1-8088-6c926cc20779"
      },
      "source": [
        "training = pd.DataFrame(traintext)\n",
        "training['Labels'] = train_df['Class']\n",
        "training"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "      <th>516</th>\n",
              "      <td>enhancing risk latina participant practice gen...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3916</th>\n",
              "      <td>level awareness student adolescent autism atte...</td>\n",
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              "      <th>4960</th>\n",
              "      <td>patient perception compliance package usage co...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>9412 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   Text  Labels\n",
              "2106  building capacity child involvement child welf...       2\n",
              "1709  whole school learning urban middle school stud...       2\n",
              "703   implementation evaluation hour ambulatory bloo...       2\n",
              "2613  inclusion project survey student virginia help...       2\n",
              "1703  inter school literacy urban middle school stud...       2\n",
              "...                                                 ...     ...\n",
              "2001                                                          0\n",
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              "\n",
              "[9412 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbkJ0IBnr8oF"
      },
      "source": [
        "**The dataframe below which was previously uploaded is the testing data and the hold out data combined; therefore, I only use the first 280 values as the other 30 are used as the hold out dataset.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RB6VsF-MLz7m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "0491504d-725b-4954-8896-8cb459f5836f"
      },
      "source": [
        "testing = pd.DataFrame(valtext)\n",
        "testing['Labels'] = test_df['Class']\n",
        "test = testing[:279]\n",
        "test"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>comprehensive injury surveillance latin americ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>phase drug interaction self administration stu...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>exploration afrocentric mentoring paradigm sch...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>resident staff perception safety engagement co...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>invisible victim longer lgbtq + dating sexual ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>279 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   New  Labels\n",
              "0    identifying prioritizing patient goal total kn...       0\n",
              "1    impact home modification safety community dwel...       2\n",
              "2    cultural diversity within school survey resear...       2\n",
              "3    stakeholder perspective social need screening ...       0\n",
              "4    development psychosocial need assessment cysti...       0\n",
              "..                                                 ...     ...\n",
              "274  comprehensive injury surveillance latin americ...       1\n",
              "275  phase drug interaction self administration stu...       0\n",
              "276  exploration afrocentric mentoring paradigm sch...       2\n",
              "277  resident staff perception safety engagement co...       2\n",
              "278  invisible victim longer lgbtq + dating sexual ...       2\n",
              "\n",
              "[279 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_GOCZLONeh5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbcd4a48-4d54-40be-df8f-8de05cfb2c34"
      },
      "source": [
        "from sklearn.utils import class_weight\n",
        "class_weights = class_weight.compute_class_weight('balanced', np.unique(train_df['Class']),train_df['Class'])\n",
        "class_weights = dict(enumerate(class_weights))\n",
        "class_weights"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 1.5304065040650408, 1: 1.0844567346468488, 2: 0.7020213321399269}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Vrsgmt50ka_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a31000f-76f6-4c23-f005-0156f5eed216"
      },
      "source": [
        "#This code will log you in to the wandb website but you will need an account for it. It is not neccesary for training though, just visualizes training\n",
        "!wandb login"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WakKEti4EiI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "64a6f0df094a4c3cb3c602a3ce1063af",
            "302a1ec8c873401b9a26f2d55fb161f7",
            "70d55c4fa7d84fedbd7a1d1bd3c35ca8",
            "05056d22d4ff41dbbed3ffffb9132e67",
            "fd07138582d546b7b00f585ee7cd962a",
            "7975442e5a404a20aa3c5633fb852e1d",
            "c11144c9746c4ecdbd7348800bb823a1",
            "c979e036253c4cd9bc600edf0ce9689d",
            "ac4952451a244c79a11498c0e367a832",
            "310e2530134240abaf8bf0284ff97980",
            "fefafa8749d54c919bb4f097c75f84a4",
            "0c95cc61bcda40eeb342210803be4ee8",
            "9799d36fab2d4b0d80720f4d40f84547",
            "143577287802420389bbdad5ad940c37",
            "1b2070499fa148db997744ae8a3bf212",
            "da326615d732494f89bbcf8c4e815326",
            "b91bf8343d5c4fb1b71859736481bf5a",
            "d659cc085bf1404f9f5104224fad0cf8",
            "4eff8d56f2e7426781d58f12c5fa05d3",
            "c26c7175568c4bf59cdcbbedb7f807de",
            "8e0c19770cb04fb89f4cb2f1b9b9be54",
            "f750f8d6ddc44d0aa836fa969e4ca950",
            "3bb8712a28c94f998227dfd4247fa011",
            "2535b6611b844ba692fcc902bcadbd9a",
            "0be4a72e39434dc5b0fb871c5ef20757",
            "f9d500ec65c04f4fad7c4d1361b060a6",
            "d3cb1dc39165411685a7c5d50633e40c",
            "2076369f3ae84a0db7e5beace623d1cb",
            "1acfcca576c94532a3ed6c61f4f36315",
            "c3df5e4413b24fd1ab08005b92956003",
            "55dd9c692b174dd7810ec33acd7e2b57",
            "b7121a7864144424994acf320ccfcbaf",
            "dfd416fa0ab44637863ec005b762735f"
          ]
        },
        "outputId": "7d18311a-dfac-4abc-a6a7-0514ed235f19"
      },
      "source": [
        "from simpletransformers.classification import ClassificationModel\n",
        "\n",
        "\n",
        "#This will probably train for ~1 hour\n",
        "# Training arguments\n",
        "model_args = {\n",
        "    'evaluate_during_training': True,\n",
        "    'logging_steps': 10,\n",
        "    'num_train_epochs': 3,\n",
        "    'evaluate_during_training_steps': 500,\n",
        "    'save_eval_checkpoints': False,\n",
        "    #\"save_steps\": 109,\n",
        "    'manual_seed':4,\n",
        "    'train_batch_size': 32,\n",
        "    'eval_batch_size': 8,\n",
        "    'overwrite_output_dir': True,\n",
        "    'learning_rate': 3e-5,\n",
        "    'sliding_window':True,\n",
        "    'max_seq_length':128,\n",
        "    'use_cuda':True,\n",
        "    'silent':True, #Silent means no verbose\n",
        "    'no_cache':True,\n",
        "\n",
        "\n",
        "\n",
        "  #wandb_project is only needed if you do the wandb login\n",
        "   # 'wandb_project': \"ProjectDemonstration\",\n",
        "}\n",
        "\n",
        "# Create a ClassificationModel\n",
        "model = ClassificationModel(\n",
        "    \"bert\", \n",
        "    \"bert-base-uncased\",\n",
        "    num_labels=3, #this would be 6 if we were using the 6 class spectrum \n",
        "    weight=[1.5304065040650408,1.0844567346468488, 0.7020213321399269],\n",
        "    args=model_args\n",
        ")\n",
        "\n",
        "# Train model\n",
        "model.train_model(training,eval_df=test)\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "64a6f0df094a4c3cb3c602a3ce1063af",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/433 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c95cc61bcda40eeb342210803be4ee8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3bb8712a28c94f998227dfd4247fa011",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:391: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n",
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:970: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:216: UserWarning: Please also save or load the state of the optimizer when saving or loading the scheduler.\n",
            "  warnings.warn(SAVE_STATE_WARNING, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14442,\n",
              " {'eval_loss': [0.3986532556406443,\n",
              "   0.2567031302491603,\n",
              "   0.12695340983332679,\n",
              "   0.14006401783276987,\n",
              "   0.14031018596283937,\n",
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              "   0.13345776799203954,\n",
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              "   0.10876490533992311,\n",
              "   0.11661123323472232,\n",
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              "   0.13495525657246019,\n",
              "   0.08840198760338935,\n",
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              "   0.13657752011247523,\n",
              "   0.11536818147810776,\n",
              "   0.10950265635892961,\n",
              "   0.10953917424972566,\n",
              "   0.10982202144185407],\n",
              "  'global_step': [500,\n",
              "   1000,\n",
              "   1500,\n",
              "   2000,\n",
              "   2500,\n",
              "   3000,\n",
              "   3500,\n",
              "   4000,\n",
              "   4500,\n",
              "   4814,\n",
              "   5000,\n",
              "   5500,\n",
              "   6000,\n",
              "   6500,\n",
              "   7000,\n",
              "   7500,\n",
              "   8000,\n",
              "   8500,\n",
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              "   9500,\n",
              "   9628,\n",
              "   10000,\n",
              "   10500,\n",
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              "   11500,\n",
              "   12000,\n",
              "   12500,\n",
              "   13000,\n",
              "   13500,\n",
              "   14000,\n",
              "   14442],\n",
              "  'mcc': [0.8214727815172844,\n",
              "   0.9706892907976225,\n",
              "   0.988177154161103,\n",
              "   1.0,\n",
              "   0.9882157606090206,\n",
              "   1.0,\n",
              "   0.99410640245175,\n",
              "   1.0,\n",
              "   0.9941029925491545,\n",
              "   1.0,\n",
              "   1.0,\n",
              "   1.0,\n",
              "   0.9941238395318983,\n",
              "   1.0,\n",
              "   0.988233681405466,\n",
              "   0.9941029925491545,\n",
              "   1.0,\n",
              "   1.0,\n",
              "   1.0,\n",
              "   1.0,\n",
              "   0.9941029925491545,\n",
              "   0.9941029925491545,\n",
              "   0.9941029925491545,\n",
              "   0.9941029925491545,\n",
              "   0.9941029925491545,\n",
              "   0.9941029925491545,\n",
              "   0.9941029925491545,\n",
              "   1.0,\n",
              "   1.0,\n",
              "   1.0,\n",
              "   1.0],\n",
              "  'train_loss': [0.2733161449432373,\n",
              "   0.034861382097005844,\n",
              "   0.0015200746711343527,\n",
              "   0.07627134025096893,\n",
              "   0.15913593769073486,\n",
              "   0.0024048667401075363,\n",
              "   0.00020150071941316128,\n",
              "   0.010328945703804493,\n",
              "   0.00020088072051294148,\n",
              "   7.86527743912302e-05,\n",
              "   0.0001074077226803638,\n",
              "   6.394214142346755e-05,\n",
              "   5.2079314627917483e-05,\n",
              "   4.056420948472805e-05,\n",
              "   0.075152687728405,\n",
              "   8.2796708738897e-05,\n",
              "   2.1056559489807114e-05,\n",
              "   4.5474269427359104e-05,\n",
              "   2.9608956538140774e-05,\n",
              "   4.390427056932822e-05,\n",
              "   2.989895256177988e-05,\n",
              "   3.394947270862758e-05,\n",
              "   4.665646338253282e-05,\n",
              "   1.4721608749823645e-05,\n",
              "   1.3029509318585042e-05,\n",
              "   2.8555245080497116e-05,\n",
              "   1.1888084372913e-05,\n",
              "   1.1433312465669587e-05,\n",
              "   9.634441084926948e-06,\n",
              "   1.0738222954387311e-05,\n",
              "   1.1341954632371198e-05]})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sbyo7dLgt_pn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a900b3ef-790d-45f6-d461-e9e73e6c34e9"
      },
      "source": [
        "result, model_outputs, wrong_predictions = model.eval_model(test)\n",
        "result"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:970: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.10982202144185407, 'mcc': 1.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHa7LidTsf8j"
      },
      "source": [
        "The output below is the predictions on the original/testing data, and the last 30 values are the hold out data which is where I get my hold out dataset acc. from \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IY-3h3wOtISQ"
      },
      "source": [
        "I make predictions based on the bestmodel output and the regular model output to compare. Which is provided by SimpleTransformers under the \"outputs\" folder found if you click the files buttom on the left side pane"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bU9WBoEyvklf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b0104bc-d2af-4d1b-9b87-308313c8c1f4"
      },
      "source": [
        "prediction_v1, raw_outputs = model.predict(valtext)\n",
        "prediction_v1 "
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 1, 2, 0, 0, 1, 2, 2, 0, 2, 1, 0,\n",
              "       1, 2, 0, 1, 2, 0, 2, 0, 0, 1, 0, 2, 0, 1, 2, 2, 2, 2, 1, 2, 1, 2,\n",
              "       1, 2, 0, 0, 2, 0, 0, 2, 2, 0, 2, 2, 1, 0, 1, 0, 0, 2, 2, 0, 2, 0,\n",
              "       1, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2,\n",
              "       0, 0, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0, 1, 2, 2, 1, 1, 0, 2, 0, 2, 2,\n",
              "       0, 0, 1, 0, 2, 0, 2, 0, 2, 0, 2, 2, 2, 2, 1, 2, 0, 0, 2, 2, 2, 0,\n",
              "       0, 1, 0, 0, 2, 0, 1, 1, 2, 1, 2, 0, 2, 2, 0, 2, 1, 2, 1, 1, 1, 2,\n",
              "       1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 0, 2, 1, 2, 0, 2, 1, 2, 2, 1, 2, 0,\n",
              "       2, 1, 0, 2, 1, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 1, 2, 2, 2, 1, 2, 0,\n",
              "       2, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 0, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2,\n",
              "       0, 2, 2, 1, 0, 2, 2, 0, 2, 1, 1, 2, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 0, 2, 0, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0, 0, 2, 1, 2,\n",
              "       2, 1, 0, 0, 2, 0, 0, 2, 0, 0, 1, 0, 2, 2, 2, 2, 2, 2, 1, 0, 0, 1,\n",
              "       2, 0, 0, 0, 2, 1, 2, 2, 0, 2, 0, 2, 2, 2, 0, 1, 2, 2, 2, 2, 0, 2,\n",
              "       2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lqZDkwPA-U3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4ce19d7-ca95-4add-cd53-236d0a9a68f6"
      },
      "source": [
        "bestcheckpoint_model = ClassificationModel(\"bert\", \"outputs/best_model/\",args=model_args,num_labels=3)\n",
        "predictions, raw_outputs = bestcheckpoint_model.predict(valtext)\n",
        "print(predictions)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 1, 2, 0, 0, 1, 2, 2, 0, 2, 1, 0, 1, 2, 0, 1, 2, 0, 2, 0, 0, 1, 0, 2, 0, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 0, 0, 2, 0, 0, 2, 2, 0, 2, 2, 1, 0, 1, 0, 0, 2, 2, 0, 2, 0, 1, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0, 1, 2, 2, 1, 1, 0, 2, 0, 2, 2, 0, 0, 1, 0, 2, 0, 2, 0, 2, 0, 2, 2, 2, 2, 1, 2, 0, 0, 2, 2, 2, 0, 0, 1, 0, 0, 2, 0, 1, 1, 2, 1, 2, 0, 2, 2, 0, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 0, 2, 1, 2, 0, 2, 1, 2, 2, 1, 2, 0, 2, 1, 0, 2, 1, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 1, 2, 2, 2, 1, 2, 0, 2, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 0, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 1, 0, 2, 2, 0, 2, 1, 1, 2, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 0, 2, 1, 2, 2, 1, 0, 0, 2, 0, 0, 2, 0, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 2, 0, 0, 0, 2, 1, 2, 2, 0, 2, 0, 2, 2, 2, 0, 1, 2, 2, 2, 2, 0, 2, 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaZpSKX6vYgj"
      },
      "source": [
        "I usually copy and paste the predictions in a google sheet I created that has all of the original data and compare my models from that but for this I can show the F1 score from this code file below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2mxkj-dwPxT"
      },
      "source": [
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "#f1 score of the last 30 values as they are the hold out data\n",
        "#I print out for each class and the average\n",
        "scores=f1_score(testing[279:]['Labels'], prediction_v1[279:], average=None)\n",
        "scoremacro=f1_score(testing[279:]['Labels'], prediction_v1[279:], average='macro')\n",
        "\n",
        "\n",
        "\n",
        "print(\"F1 scores per class: \") \n",
        "print(\"\")\n",
        "print(\"Zero's class: \", scores[0])\n",
        "print(\"One's class: \", scores[1])\n",
        "print(\"Two's class: \", scores[2])\n",
        "print(\"\")\n",
        "print(\"Macro F1 score: \")\n",
        "print(\"\")\n",
        "print(\"F1 macro :\",scoremacro)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Rtb9aApriZz"
      },
      "source": [
        "# **The cross-validation models were performed using this**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4p5gSiKDlcV"
      },
      "source": [
        "from sklearn.model_selection  import StratifiedKFold, cross_val_score\n",
        "from sklearn.model_selection import KFold \n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd\n",
        "from simpletransformers.classification import ClassificationModel\n",
        "import logging\n",
        "\n",
        "\n",
        "# Training arguments\n",
        "model_args = {\n",
        "    'evaluate_during_training': False,\n",
        "    'logging_steps': 10,\n",
        "    'num_train_epochs': 3,\n",
        "    'evaluate_during_training_steps': 500,\n",
        "    'save_eval_checkpoints': False,\n",
        "    #\"save_steps\": 109,\n",
        "    'manual_seed':4,\n",
        "    'train_batch_size': 32,\n",
        "    'eval_batch_size': 8,\n",
        "    'overwrite_output_dir': True,\n",
        "    'learning_rate': 3e-5,\n",
        "    'sliding_window':True,\n",
        "    'max_seq_length':128,\n",
        "    'use_cuda':True,\n",
        "    'silent':True,\n",
        "    'no_cache':True,\n",
        "\n",
        "\n",
        "\n",
        "  \n",
        "    #'wandb_project': \"xlmroberta\",\n",
        "}\n",
        "\n",
        "# Create a ClassificationModel\n",
        "model = ClassificationModel(\n",
        "    \"bert\", \n",
        "    \"bert-base-uncased\",\n",
        "    num_labels=3,\n",
        "    weight=[1.5304065040650408,1.0844567346468488, 0.7020213321399269],\n",
        "    args=model_args)\n",
        "\n",
        "\n",
        "\n",
        "#prepare cross validation\n",
        "n=8\n",
        "kf = KFold(n_splits=n, random_state=42, shuffle=True)\n",
        "\n",
        "results = []\n",
        "# Log scores\n",
        "#for cv_score in results:\n",
        "  # wandb.log({'score': results})\n",
        "  # wandb.watch(results, log='all')\n",
        "\n",
        "for train_index, val_index in kf.split(training):\n",
        "  # splitting Dataframe (dataset not included)\n",
        "    train = training.iloc[train_index]\n",
        "    val = training.iloc[val_index]\n",
        "  # train the model\n",
        "    model.train_model(train)\n",
        "  # validate the model \n",
        "    result, model_outputs, wrong_predictions = model.eval_model(val, acc=accuracy_score)\n",
        "    print(result['acc'])\n",
        "  # append model score\n",
        "    results.append(result['acc'])\n",
        "\n",
        "\n",
        "print(\"results\",results)\n",
        "print(f\"Mean-Precision: {sum(results) / len(results)}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2zX4zNhLNa_"
      },
      "source": [
        "model.train_model(training)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pw83n94MLNhF"
      },
      "source": [
        "predictions2, raw_outputs2 = model.predict(valtext)\n",
        "print(predictions2)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}