Load and Predict: suicide_detection_gpt2.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JohnCiubuc/Suicide-Detection-GPT2/blob/main/Load%20and%20Predict%3A%20suicide_detection_gpt2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78UA1RvdxDTp"
      },
      "source": [
        "## **Downloads**\n",
        "\n",
        "Downloads a pre-saved model.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dFbVPUXuhPv",
        "outputId": "7dada2e8-bed0-4822-d88b-23c9d6de23c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl\" -O Suicide_Detection.model && rm -rf /tmp/cookies.txt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-26 18:20:22--  https://docs.google.com/uc?export=download&confirm=0V1u&id=1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl\n",
            "Resolving docs.google.com (docs.google.com)... 142.251.2.102, 142.251.2.101, 142.251.2.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.251.2.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-14-1s-docs.googleusercontent.com/docs/securesc/vs7bcgcqg5ciku7u6j5c1vc839thep4e/lg0idd4apsnf28acrvd7qilo369tcsao/1624731600000/07047829658875637856/01701166847667467755Z/1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl?e=download [following]\n",
            "--2021-06-26 18:20:22--  https://doc-14-1s-docs.googleusercontent.com/docs/securesc/vs7bcgcqg5ciku7u6j5c1vc839thep4e/lg0idd4apsnf28acrvd7qilo369tcsao/1624731600000/07047829658875637856/01701166847667467755Z/1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl?e=download\n",
            "Resolving doc-14-1s-docs.googleusercontent.com (doc-14-1s-docs.googleusercontent.com)... 142.251.2.132, 2607:f8b0:4023:c0d::84\n",
            "Connecting to doc-14-1s-docs.googleusercontent.com (doc-14-1s-docs.googleusercontent.com)|142.251.2.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://docs.google.com/nonceSigner?nonce=476sau79c3844&continue=https://doc-14-1s-docs.googleusercontent.com/docs/securesc/vs7bcgcqg5ciku7u6j5c1vc839thep4e/lg0idd4apsnf28acrvd7qilo369tcsao/1624731600000/07047829658875637856/01701166847667467755Z/1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl?e%3Ddownload&hash=kf64381k2ltij6ghkhab37nef8i67bnj [following]\n",
            "--2021-06-26 18:20:22--  https://docs.google.com/nonceSigner?nonce=476sau79c3844&continue=https://doc-14-1s-docs.googleusercontent.com/docs/securesc/vs7bcgcqg5ciku7u6j5c1vc839thep4e/lg0idd4apsnf28acrvd7qilo369tcsao/1624731600000/07047829658875637856/01701166847667467755Z/1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl?e%3Ddownload&hash=kf64381k2ltij6ghkhab37nef8i67bnj\n",
            "Connecting to docs.google.com (docs.google.com)|142.251.2.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://doc-14-1s-docs.googleusercontent.com/docs/securesc/vs7bcgcqg5ciku7u6j5c1vc839thep4e/lg0idd4apsnf28acrvd7qilo369tcsao/1624731600000/07047829658875637856/01701166847667467755Z/1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl?e=download&nonce=476sau79c3844&user=01701166847667467755Z&hash=bki57mb4esqdiugd6gu7oaodd5lcu3ca [following]\n",
            "--2021-06-26 18:20:22--  https://doc-14-1s-docs.googleusercontent.com/docs/securesc/vs7bcgcqg5ciku7u6j5c1vc839thep4e/lg0idd4apsnf28acrvd7qilo369tcsao/1624731600000/07047829658875637856/01701166847667467755Z/1XmCSsOd1kJHHi1x0DrSx9k14mEn7qrbl?e=download&nonce=476sau79c3844&user=01701166847667467755Z&hash=bki57mb4esqdiugd6gu7oaodd5lcu3ca\n",
            "Connecting to doc-14-1s-docs.googleusercontent.com (doc-14-1s-docs.googleusercontent.com)|142.251.2.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/x-zip]\n",
            "Saving to: ‘Suicide_Detection.model’\n",
            "\n",
            "Suicide_Detection.m     [          <=>       ] 486.78M   119MB/s    in 4.9s    \n",
            "\n",
            "2021-06-26 18:20:28 (98.9 MB/s) - ‘Suicide_Detection.model’ saved [510427623]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xFqG3VVtxpi"
      },
      "source": [
        "## **Installs**\n",
        "\n",
        "* **[transformers](https://github.com/huggingface/transformers)** library needs to be installed to use all the awesome code from Hugging Face. To get the latest version I will install it straight from GitHub.\n",
        "\n",
        "* **[ml_things](https://github.com/gmihaila/ml_things)** library used for various machine learning related tasks. I created this library to reduce the amount of code I need to write for each machine learning project.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUz_vmR8tyoc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e72840c4-014f-41be-a4f2-809a09a9cfc9"
      },
      "source": [
        "# Fix pre-req install issue\n",
        "!pip install imgaug==0.2.5\n",
        "# Install transformers library.\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git\n",
        "# Install helper functions.\n",
        "!pip install -q git+https://github.com/gmihaila/ml_things.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting imgaug==0.2.5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d2/60/a06a48d85a7e9062f5870347a3e3e953da30b37928d43b380c949bca458a/imgaug-0.2.5.tar.gz (562kB)\n",
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vF3UbENTtz2_"
      },
      "source": [
        "## **Imports**\n",
        "\n",
        "Import all needed libraries for this notebook.\n",
        "\n",
        "Declare parameters used for this notebook:\n",
        "\n",
        "* `set_seed(123)` - Always good to set a fixed seed for reproducibility.\n",
        "* `epochs` - Number of training epochs (authors recommend between 2 and 4).\n",
        "* `batch_size` - Number of batches - depending on the max sequence length and GPU memory. For 512 sequence length a batch of 10 USUALY works without cuda memory issues. For small sequence length can try batch of 32 or higher.\n",
        "max_length - Pad or truncate text sequences to a specific length. I will set it to 60 to speed up training.\n",
        "* `device` - Look for gpu to use. Will use cpu by default if no gpu found.\n",
        "* `model_name_or_path` - Name of transformers model - will use already pretrained model. Path of transformer model - will load your own model from local disk. In this tutorial I will use `gpt2` model.\n",
        "* `labels_ids` - Dictionary of labels and their id - this will be used to convert string labels to numbers.\n",
        "* `n_labels` - How many labels are we using in this dataset. This is used to decide size of classification head."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjr_J342tOPq"
      },
      "source": [
        "import io\n",
        "import os\n",
        "import torch\n",
        "from tqdm.notebook import tqdm\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from ml_things import plot_dict, plot_confusion_matrix, fix_text\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from transformers import (set_seed,\n",
        "                          TrainingArguments,\n",
        "                          Trainer,\n",
        "                          GPT2Config,\n",
        "                          GPT2Tokenizer,\n",
        "                          AdamW, \n",
        "                          get_linear_schedule_with_warmup,\n",
        "                          GPT2ForSequenceClassification)\n",
        "\n",
        "# Set seed for reproducibility.\n",
        "set_seed(123)\n",
        "\n",
        "# Number of training epochs (authors on fine-tuning Bert recommend between 2 and 4).\n",
        "epochs = 2\n",
        "\n",
        "# Number of batches - depending on the max sequence length and GPU memory.\n",
        "# For 512 sequence length batch of 10 works without cuda memory issues.\n",
        "# For small sequence length can try batch of 32 or higher.\n",
        "batch_size = 32\n",
        "\n",
        "# Pad or truncate text sequences to a specific length\n",
        "# if `None` it will use maximum sequence of word piece tokens allowed by model.\n",
        "max_length = 60\n",
        "\n",
        "# Look for gpu to use. Will use `cpu` by default if no gpu found.\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Name of transformers model - will use already pretrained model.\n",
        "# Path of transformer model - will load your own model from local disk.\n",
        "model_name_or_path = 'gpt2'\n",
        "\n",
        "# Dictionary of labels and their id - this will be used to convert.\n",
        "# String labels to number ids.\n",
        "labels_ids = {'suicide': 0, 'non-suicide': 1}\n",
        "\n",
        "# How many labels are we using in training.\n",
        "# This is used to decide size of classification head.\n",
        "n_labels = len(labels_ids)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdnKsoFbt1Wd"
      },
      "source": [
        "## **Helper Functions**\n",
        "\n",
        "I like to keep all Classes and functions that will be used in this notebook under this section to help maintain a clean look of the notebook:\n",
        "\n",
        "<br>\n",
        "\n",
        "**MovieReviewsDataset(Dataset)**\n",
        "\n",
        "If you worked with PyTorch before, this is pretty standard. We need this class to read in our dataset, parse it and return texts with their associated labels.\n",
        "\n",
        "In this class I only need to read in the content of each file, use fix_text to fix any Unicode problems and keep track of positive and negative sentiments.\n",
        "\n",
        "I will append all texts and labels in lists.\n",
        "\n",
        "There are three main parts of this PyTorch Dataset class:\n",
        "\n",
        "* **init()** where we read in the dataset and transform text and labels into numbers.\n",
        "* **len()** where we need to return the number of examples we read in. This is used when calling len(MovieReviewsDataset()).\n",
        "* **getitem()** always takes as an input an int value that represents which example from our examples to return from our dataset. If a value of 3 is passed, we will return the example form our dataset at position 3.\n",
        "\n",
        "<br>\n",
        "\n",
        "**Gpt2ClassificationCollator**\n",
        "\n",
        "I use this class to create the Data Collator. This will be used in the DataLoader to create the bathes of data that get fed to the model. I use the tokenizer and label encoder on each sequence to convert texts and labels to number.\n",
        "\n",
        "Lucky for us, Hugging Face thought of everything and made the tokenizer do all the heavy lifting (split text into tokens, padding, truncating, encode text into numbers) and is very easy to use!\n",
        "\n",
        "There are two main parts of this Data Collator class:\n",
        "\n",
        "* **init()** where we initialize the tokenizer we plan to use, how to encode our labels and if we need to set the sequence length to a different value.\n",
        "\n",
        "* **__call__()** used as function collator that takes as input a batch of data examples. It needs to return an object with the format that can be fed to our model. Luckily our tokenizer does that for us and returns a dictionary of variables ready to be fed to the model in this way: `model(**inputs)`. Since we are fine-tuning the model I also included the labels.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "**train(dataloader, optimizer_, scheduler_, device_)**\n",
        "\n",
        "I created this function to perform a full pass through the DataLoader object (the DataLoader object is created from our Dataset* type object using the **MovieReviewsDataset class). This is basically one epoch train through the entire dataset.\n",
        "\n",
        "The dataloader is created from PyTorch DataLoader which takes the object created from MovieReviewsDataset class and puts each example in batches. This way we can feed our model batches of data!\n",
        "\n",
        "The optimizer_ and scheduler_ are very common in PyTorch. They are required to update the parameters of our model and update our learning rate during training. There is a lot more than that but I won't go into details. This can actually be a huge rabbit hole since A LOT happens behind these functions that we don't need to worry. Thank you PyTorch!\n",
        "\n",
        "In the process we keep track of the actual labels and the predicted labels along with the loss.\n",
        "\n",
        "<br>\n",
        "\n",
        "**validation(dataloader, device_)**\n",
        "\n",
        "I implemented this function in a very similar way as train but without the parameters update, backward pass and gradient decent part. We don't need to do all of those VERY computationally intensive tasks because we only care about our model's predictions.\n",
        "\n",
        "I use the DataLoader in a similar way as in train to get out batches to feed to our model.\n",
        "\n",
        "In the process I keep track of the actual labels and the predicted labels along with the loss.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDEubgJIt23C"
      },
      "source": [
        "class virtualLoad(Dataset):\n",
        "  def __init__(self):\n",
        "    self.texts=[]\n",
        "    self.labels=[]\n",
        "    self.n_examples = len(self.labels)\n",
        "    \n",
        "    return\n",
        "  def __len__(self):\n",
        "    return self.n_examples\n",
        "  def __getitem__(self, item):\n",
        "    return {'text':self.texts[item],\n",
        "            'label':self.labels[item]}\n",
        "  def getText(self,item):\n",
        "    return self.texts[item]\n",
        "  def loadText(self, text):\n",
        "    self.texts.append(text)\n",
        "    self.labels.append('non-suicide') # can use 'suicide' label too. This is needed for the model to load the item, but it's not used in predicting\n",
        "    self.n_examples = len(self.labels)\n",
        "\n",
        "class MovieReviewsDataset(Dataset):\n",
        "  r\"\"\"PyTorch Dataset class for loading data.\n",
        "\n",
        "  This is where the data parsing happens.\n",
        "\n",
        "  This class is built with reusability in mind: it can be used as is as.\n",
        "\n",
        "  Arguments:\n",
        "\n",
        "    path (:obj:`str`):\n",
        "        Path to the data partition.\n",
        "\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, path, use_tokenizer):\n",
        "\n",
        "    # Check if path exists.\n",
        "    if not os.path.isdir(path):\n",
        "      # Raise error if path is invalid.\n",
        "      raise ValueError('Invalid `path` variable! Needs to be a directory')\n",
        "    self.texts = []\n",
        "    self.labels = []\n",
        "    # Since the labels are defined by folders with data we loop \n",
        "    # through each label.\n",
        "    for label in ['suicide', 'non-suicide']:\n",
        "      sentiment_path = os.path.join(path, label)\n",
        "\n",
        "      # Get all files from path.\n",
        "      files_names = os.listdir(sentiment_path)#[:10] # Sample for debugging.\n",
        "      # Go through each file and read its content.\n",
        "      for file_name in tqdm(files_names, desc=f'{label} files'):\n",
        "        file_path = os.path.join(sentiment_path, file_name)\n",
        "\n",
        "        # Read content.\n",
        "        content = io.open(file_path, mode='r', encoding='utf-8').read()\n",
        "        # Fix any unicode issues.\n",
        "        content = fix_text(content)\n",
        "        # Save content.\n",
        "        self.texts.append(content)\n",
        "        # Save encode labels.\n",
        "        self.labels.append(label)\n",
        "\n",
        "    # Number of exmaples.\n",
        "    self.n_examples = len(self.labels)\n",
        "    \n",
        "\n",
        "    return\n",
        "\n",
        "  def __len__(self):\n",
        "    r\"\"\"When used `len` return the number of examples.\n",
        "\n",
        "    \"\"\"\n",
        "    \n",
        "    return self.n_examples\n",
        "\n",
        "  def __getitem__(self, item):\n",
        "    r\"\"\"Given an index return an example from the position.\n",
        "    \n",
        "    Arguments:\n",
        "\n",
        "      item (:obj:`int`):\n",
        "          Index position to pick an example to return.\n",
        "\n",
        "    Returns:\n",
        "      :obj:`Dict[str, str]`: Dictionary of inputs that contain text and \n",
        "      asociated labels.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    return {'text':self.texts[item],\n",
        "            'label':self.labels[item]}\n",
        "\n",
        "\n",
        "\n",
        "class Gpt2ClassificationCollator(object):\n",
        "    r\"\"\"\n",
        "    Data Collator used for GPT2 in a classificaiton rask. \n",
        "    \n",
        "    It uses a given tokenizer and label encoder to convert any text and labels to numbers that \n",
        "    can go straight into a GPT2 model.\n",
        "\n",
        "    This class is built with reusability in mind: it can be used as is as long\n",
        "    as the `dataloader` outputs a batch in dictionary format that can be passed \n",
        "    straight into the model - `model(**batch)`.\n",
        "\n",
        "    Arguments:\n",
        "\n",
        "      use_tokenizer (:obj:`transformers.tokenization_?`):\n",
        "          Transformer type tokenizer used to process raw text into numbers.\n",
        "\n",
        "      labels_ids (:obj:`dict`):\n",
        "          Dictionary to encode any labels names into numbers. Keys map to \n",
        "          labels names and Values map to number associated to those labels.\n",
        "\n",
        "      max_sequence_len (:obj:`int`, `optional`)\n",
        "          Value to indicate the maximum desired sequence to truncate or pad text\n",
        "          sequences. If no value is passed it will used maximum sequence size\n",
        "          supported by the tokenizer and model.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, use_tokenizer, labels_encoder, max_sequence_len=None):\n",
        "\n",
        "        # Tokenizer to be used inside the class.\n",
        "        self.use_tokenizer = use_tokenizer\n",
        "        # Check max sequence length.\n",
        "        self.max_sequence_len = use_tokenizer.model_max_length if max_sequence_len is None else max_sequence_len\n",
        "        # Label encoder used inside the class.\n",
        "        self.labels_encoder = labels_encoder\n",
        "\n",
        "        return\n",
        "\n",
        "    def __call__(self, sequences):\n",
        "        r\"\"\"\n",
        "        This function allowes the class objesct to be used as a function call.\n",
        "        Sine the PyTorch DataLoader needs a collator function, I can use this \n",
        "        class as a function.\n",
        "\n",
        "        Arguments:\n",
        "\n",
        "          item (:obj:`list`):\n",
        "              List of texts and labels.\n",
        "\n",
        "        Returns:\n",
        "          :obj:`Dict[str, object]`: Dictionary of inputs that feed into the model.\n",
        "          It holddes the statement `model(**Returned Dictionary)`.\n",
        "        \"\"\"\n",
        "\n",
        "        # Get all texts from sequences list.\n",
        "        texts = [sequence['text'] for sequence in sequences]\n",
        "        # Get all labels from sequences list.\n",
        "        labels = [sequence['label'] for sequence in sequences]\n",
        "        # Encode all labels using label encoder.\n",
        "        labels = [self.labels_encoder[label] for label in labels]\n",
        "        # Call tokenizer on all texts to convert into tensors of numbers with \n",
        "        # appropriate padding.\n",
        "        inputs = self.use_tokenizer(text=texts, return_tensors=\"pt\", padding=True, truncation=True,  max_length=self.max_sequence_len)\n",
        "        # Update the inputs with the associated encoded labels as tensor.\n",
        "        inputs.update({'labels':torch.tensor(labels)})\n",
        "\n",
        "        return inputs\n",
        "\n",
        "\n",
        "def predict(dataloader, device_):\n",
        "  r\"\"\"Validation function to evaluate model performance on a \n",
        "  separate set of data.\n",
        "\n",
        "  This function will return the true and predicted labels so we can use later\n",
        "  to evaluate the model's performance.\n",
        "\n",
        "  This function is built with reusability in mind: it can be used as is as long\n",
        "    as the `dataloader` outputs a batch in dictionary format that can be passed \n",
        "    straight into the model - `model(**batch)`.\n",
        "\n",
        "  Arguments:\n",
        "\n",
        "    dataloader (:obj:`torch.utils.data.dataloader.DataLoader`):\n",
        "          Parsed data into batches of tensors.\n",
        "\n",
        "    device_ (:obj:`torch.device`):\n",
        "          Device used to load tensors before feeding to model.\n",
        "\n",
        "  Returns:\n",
        "    \n",
        "    :obj:`List[List[int], List[int], float]`: List of [True Labels, Predicted\n",
        "        Labels, Train Average Loss]\n",
        "  \"\"\"\n",
        "\n",
        "  # Use global variable for model.\n",
        "  global model\n",
        "\n",
        "  # Tracking variables\n",
        "  predictions_labels = []\n",
        "\n",
        "  # Put the model in evaluation mode--the dropout layers behave differently\n",
        "  # during evaluation.\n",
        "  model.eval()\n",
        "\n",
        "  # Evaluate data for one epoch\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "\n",
        "    # move batch to device\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "    # Telling the model not to compute or store gradients, saving memory and\n",
        "    # speeding up validation\n",
        "    with torch.no_grad():        \n",
        "\n",
        "        # Forward pass, calculate logit predictions.\n",
        "        # This will return the logits rather than the loss because we have\n",
        "        # not provided labels.\n",
        "        # token_type_ids is the same as the \"segment ids\", which \n",
        "        # differentiates sentence 1 and 2 in 2-sentence tasks.\n",
        "        # The documentation for this `model` function is here: \n",
        "        # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
        "        outputs = model(**batch)\n",
        "\n",
        "        # The call to `model` always returns a tuple, so we need to pull the \n",
        "        # loss value out of the tuple along with the logits. We will use logits\n",
        "        # later to to calculate training accuracy.\n",
        "        loss, logits = outputs[:2]\n",
        "        # Move logits and labels to CPU\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "        # get predicitons to list\n",
        "        predict_content = logits.argmax(axis=-1).flatten().tolist()\n",
        "        # update list\n",
        "        predictions_labels += predict_content\n",
        "\n",
        "  # Return prediction\n",
        "  return  predictions_labels"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGPaKPcDhjdx"
      },
      "source": [
        "def predictText(text):\n",
        "  # create virtual loader\n",
        "  test_d =  virtualLoad()\n",
        "  # add text to predict\n",
        "  test_d.loadText(text) \n",
        "\n",
        "  # Move pytorch dataset into dataloader.\n",
        "  test_loader = DataLoader(test_d, batch_size=batch_size, shuffle=False, collate_fn=gpt2_classificaiton_collator)\n",
        "\n",
        "  # Run prediction\n",
        "  output = predict(test_loader, device)\n",
        "  iter = 0\n",
        "  for item in output:\n",
        "    print('\"'+test_d.getText(iter)+'\"')\n",
        "    print(list(labels_ids.keys())[list(labels_ids.values()).index(item)])\n",
        "    iter = iter + 1"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ej3snbu3edU"
      },
      "source": [
        "## **Load Model and Tokenizer**\n",
        "\n",
        "Loading the three essential parts of the pretrained GPT2 transformer: configuration, tokenizer and model. \n",
        "\n",
        "For this example I will use `gpt2` from HuggingFace pretrained transformers. You can use any variations of GP2 you want.\n",
        "\n",
        "In creating the `model_config` I will mention the number of labels I need for my classification task. Since I only predict two sentiments: positive and negative I will only need two labels for `num_labels`.\n",
        "\n",
        "Creating the `tokenizer` is pretty standard when using the Transformers library. After creating the tokenizer it is critical for this tutorial to set padding to the left `tokenizer.padding_side = \"left\"` and initialize the padding token to `tokenizer.eos_token` which is the GPT2's original end of sequence token. This is the most essential part of this tutorial since GPT2 uses the last token for prediction so we need to pad to the left.\n",
        "\n",
        "HuggingFace already did most of the work for us and added a classification layer to the GPT2 model. In creating the model I used `GPT2ForSequenceClassification`.\n",
        "Since we have a custom padding token we need to initialize it for the model using `model.config.pad_token_id`.\n",
        "Finally we will need to move the model to the device we defined earlier.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjZr2Lqyh-hL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186,
          "referenced_widgets": [
            "d24c92b0e3b8449581aa2dca8d8edf17",
            "d4af448854ab46fb8bb1c94398db6b98",
            "99a2c3b98c55414f9434e25ba79c5cc6",
            "b4049e7c461b4be89c5c07b163713e1b",
            "17bcd3311ed244749c09bca6f6418a36",
            "8400d6da7f954894a6eb35dc680375a9",
            "b32230a9b5964d25ad05b66eb15c04c3",
            "9441df0518454020bf6a984e85f30bbb",
            "bb96456704e34f49a946d71b7cd7c441",
            "98a55b7782f94fbe91b1db1fa0180d33",
            "c13845e7d7774d5a976f4247031e8164",
            "f1d0900e9da24ec6b91698c1548a69f7",
            "be8686252c3d40fc8220ba08f36598d1",
            "82dd8168a2024e83a1a2152b457576ad",
            "fa0fc237d3124240801d044db695a85d",
            "a1e5ccf05fe347b895f37e652af57502",
            "3bfa486b7cfc447f858de758867e3cce",
            "6cbf9d4f726f4a63be1fc9a6cf37d952",
            "1930ca4f5c7f431b90cb969cdefafd85",
            "3b53dd5cb78b470787ee805d79a8a286",
            "f44fd220878e4b168f0c330377d63f8b",
            "c594ac33540b46a08847a6f9d69dee92",
            "48155210e6a24f92bb3206149a2c66e6",
            "695aff4cfbd14edc95ce06b2e20a4847"
          ]
        },
        "outputId": "b62a3060-3567-4473-d7ff-fffbddb0b25c"
      },
      "source": [
        "\n",
        "# Get model's tokenizer.\n",
        "print('Loading tokenizer...')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path=model_name_or_path)\n",
        "# default to left padding\n",
        "tokenizer.padding_side = \"left\"\n",
        "# Define PAD Token = EOS Token = 50256\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Create data collator to encode text and labels into numbers.\n",
        "gpt2_classificaiton_collator = Gpt2ClassificationCollator(use_tokenizer=tokenizer, \n",
        "                                                          labels_encoder=labels_ids, \n",
        "                                                          max_sequence_len=max_length)\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading tokenizer...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d24c92b0e3b8449581aa2dca8d8edf17",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1042301.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb96456704e34f49a946d71b7cd7c441",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3bfa486b7cfc447f858de758867e3cce",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1355256.0, style=ProgressStyle(descript…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwMhIYGOJDm0"
      },
      "source": [
        "## Load saved Suicide_Detection Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wE1KGJAeiynb",
        "outputId": "5f629c94-094a-4680-f1bb-9986bea872b7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Load and Initialize Model\n",
        "model = torch.load('Suicide_Detection.model')\n",
        "model.eval()\n",
        "\n",
        "# Look for gpu to use. Will use `cpu` by default if no gpu found.\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Move model to device\n",
        "model.to(device)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2ForSequenceClassification(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (score): Linear(in_features=768, out_features=2, bias=False)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gE6gxIk0gJ_t"
      },
      "source": [
        "## Run predictions on Suicide_Detection model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huYc8-Z5T3-K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
          "referenced_widgets": [
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        "outputId": "5a3ce068-eb47-4798-fb0a-7aade64f96df"
      },
      "source": [
        "# Run predictor\n",
        "predictText(\"I am looking forward to grabbing some ice cream with the bros\") \n",
        "predictText(\"I am trying my best honestly, but everything sucks. I'm depressed and stressed as shit.\") \n",
        "predictText(\"Does anyone know where the closest bahamas google server's at?\") \n",
        "predictText(\"My spouse left me. My cat cheated on me with a golfball. Life sucks and I need someone to talk to\") "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f4e8833f6f34b1d83d2f719a2c657da",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\"I am looking forward to grabbing some ice cream with the bros\"\n",
            "non-suicide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\"I am trying my best honestly, but everything sucks. I'm depressed and stressed as shit.\"\n",
            "suicide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7ebc85a03d44fa3b5a71febc1294d9c",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\"Does anyone know where the closest bahamas google server's at?\"\n",
            "non-suicide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7b5b2ebf6344428abb583ba44de37c5",
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\"My spouse left me. My cat cheated on me with a golfball. Life sucks and I need someone to talk to\"\n",
            "suicide\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}