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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIW5WZrd7837",
        "outputId": "0f124560-3d66-4898-b3a3-7ffee37f6d81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports and parameter defining"
      ],
      "metadata": {
        "id": "er-J1ZnnjnCP"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjr_J342tOPq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "011d868e-f3dd-4fac-ee3f-4b1a7b2015c2"
      },
      "source": [
        "import io\n",
        "import os\n",
        "import torch\n",
        "from tqdm.notebook import tqdm\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "!pip install ml_things\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",
        "\n",
        "set_seed(123)\n",
        "epochs = 8\n",
        "batch_size = 32\n",
        "learning_rate = 1e-4\n",
        "max_length = 160\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",
        "# get model name\n",
        "model_name_or_path = 'gpt2'\n",
        "\n",
        "\n",
        "# String labels to number ids.\n",
        "labels_ids = {'appeal_to_fear_prejudice': 0, 'causal_oversimplification': 1, 'doubt':2, 'exaggeration,minimisation':3, 'flag_waving':4, 'loaded_language':5, 'name_calling,labeling':6, 'repetition':7}\n",
        "\n",
        "n_labels = len(labels_ids) # get number of labels for training, used to decide size of classification head."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ml_things\n",
            "  Downloading ml_things-0.0.1.tar.gz (8.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.1/8.1 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from ml_things) (1.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from ml_things) (1.25.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from ml_things) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from ml_things) (4.66.4)\n",
            "Collecting ftfy>=5.8 (from ml_things)\n",
            "  Downloading ftfy-6.2.0-py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from ml_things) (3.7.1)\n",
            "Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /usr/local/lib/python3.10/dist-packages (from ftfy>=5.8->ml_things) (0.2.13)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.4.0->ml_things) (2.8.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->ml_things) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->ml_things) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->ml_things) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->ml_things) (2024.2.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->ml_things) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->ml_things) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->ml_things) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.4.0->ml_things) (1.16.0)\n",
            "Building wheels for collected packages: ml_things\n",
            "  Building wheel for ml_things (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ml_things: filename=ml_things-0.0.1-py3-none-any.whl size=24393 sha256=8d48299ab95a597f62aa603ad4a923d7f6ee5c7a0b40ecc95ccd38747b047ad8\n",
            "  Stored in directory: /root/.cache/pip/wheels/2a/b9/36/3725744c1d8ebec00cd847c9461b4352ce08de9d3f20b6bdcf\n",
            "Successfully built ml_things\n",
            "Installing collected packages: ftfy, ml_things\n",
            "Successfully installed ftfy-6.2.0 ml_things-0.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Creating Dataset and Dataloader**"
      ],
      "metadata": {
        "id": "RTkdHfC7QO30"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDEubgJIt23C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abc60249-13ea-4e95-f4fb-670a512b4526"
      },
      "source": [
        "class PropagandaDetectorDataset(Dataset):\n",
        "  \"\"\"\n",
        "    PyTorch Dataset class for loading data.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, path, use_tokenizer):\n",
        "    \"\"\"\n",
        "      arguments:\n",
        "        path: path to partitioned, preprocessed data\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",
        "\n",
        "    self.texts = []\n",
        "    self.labels = []\n",
        "\n",
        "    # for each label...\n",
        "    for label in ['appeal_to_fear_prejudice', 'causal_oversimplification', 'doubt', 'exaggeration,minimisation', 'flag_waving', 'loaded_language', 'name_calling,labeling', 'repetition']:\n",
        "\n",
        "      # Get path of that class\n",
        "      propaganda_path = os.path.join(path, label)\n",
        "\n",
        "      # Get all files from path.\n",
        "      files_names = os.listdir(propaganda_path)\n",
        "\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(propaganda_path, file_name)\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",
        "      # get numbe rof labels.\n",
        "      self.n_examples = len(self.labels)\n",
        "\n",
        "    return\n",
        "\n",
        "  def __len__(self):\n",
        "    \"\"\"\n",
        "    returns the number of examples.\n",
        "    \"\"\"\n",
        "    return self.n_examples\n",
        "\n",
        "  def __getitem__(self, item):\n",
        "    \"\"\"\n",
        "    Given an index, return an example from the position.\n",
        "    \"\"\"\n",
        "\n",
        "    return {'text':self.texts[item],\n",
        "            'label':self.labels[item]}\n",
        "\n",
        "\n",
        "\n",
        "class Gpt2ClassificationCollator(object):\n",
        "    \"\"\"\n",
        "    Data Collator.\n",
        "\n",
        "    Uses a given tokenizer and label encoder to convert any text and labels to representation that can go straight into a GPT2 model.\n",
        "\n",
        "    Args:\n",
        "\n",
        "      use_tokenizer: tokenizer used to process raw text into numbers.\n",
        "\n",
        "      labels_ids: Dictionary to encode labels to numbers.\n",
        "\n",
        "      max_sequence_len: Value to indicate the maximum desired sequence to truncate or pad text sequences to.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, use_tokenizer, labels_encoder, max_sequence_len=None):\n",
        "\n",
        "        # Tokenizer to use\n",
        "        self.use_tokenizer = use_tokenizer\n",
        "\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",
        "\n",
        "        # Label encoder to use\n",
        "        self.labels_encoder = labels_encoder\n",
        "\n",
        "        return\n",
        "\n",
        "    def __call__(self, sequences):\n",
        "\n",
        "        #get all texts from sequences list.\n",
        "        texts = [sequence['text'] for sequence in sequences]\n",
        "\n",
        "        #get all labels from sequences list.\n",
        "        labels = [sequence['label'] for sequence in sequences]\n",
        "\n",
        "        #encode all labels\n",
        "        labels = [self.labels_encoder[label] for label in labels]\n",
        "\n",
        "        #call tokenizer on all texts to convert into tensors\n",
        "        inputs = self.use_tokenizer(text=texts, return_tensors=\"pt\", padding=True, truncation=True,  max_length=self.max_sequence_len)\n",
        "\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 train(dataloader, optimizer_, scheduler_, device_):\n",
        "  \"\"\"\n",
        "  Train pytorch model on a single pass through the data loader.\n",
        "\n",
        "  uses global variable 'model' which is the transformer model loaded on device_.\n",
        "\n",
        "\n",
        "  Arguments:\n",
        "\n",
        "      dataloader: Parsed data into batches of tensors.\n",
        "\n",
        "      optimizer_: Optimizer used for training.\n",
        "\n",
        "      scheduler_: PyTorch scheduler.\n",
        "\n",
        "      device_: Device used to load tensors.\n",
        "\n",
        "  Returns:\n",
        "\n",
        "      :obj:`[True Labels, Predicted Labels, Train Average Loss].\n",
        "  \"\"\"\n",
        "\n",
        "  # Use global variable for model.\n",
        "  global model\n",
        "\n",
        "  #tracking variables.\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "\n",
        "  #total loss for this epoch.\n",
        "  total_loss = 0\n",
        "\n",
        "  #put the model in training mode.\n",
        "  model.train()\n",
        "\n",
        "  # For each batch of training data...\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "\n",
        "    # add original labels - use later for evaluation.\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "\n",
        "    # move batch to device\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "\n",
        "    # clear any previously calculated gradients before performing a backward pass.\n",
        "    model.zero_grad()\n",
        "\n",
        "    # Perform a forward pass (evaluate the model on this training batch).\n",
        "    outputs = model(**batch)\n",
        "\n",
        "    # Pull loss and logits for later use\n",
        "    loss, logits = outputs[:2]\n",
        "\n",
        "    #accumulate loss.\n",
        "    total_loss += loss.item()\n",
        "\n",
        "    # Perform backward pass to calculate the gradients.\n",
        "    loss.backward()\n",
        "\n",
        "    # Clip the norm of the gradients to 1.0, prevents gradients exploding.\n",
        "    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "    #optimise gradients\n",
        "    optimizer_.step()\n",
        "\n",
        "    # update learning rate.\n",
        "    scheduler_.step()\n",
        "\n",
        "    # move logits and labels to CPU\n",
        "    logits = logits.detach().cpu().numpy()\n",
        "\n",
        "    # convert logits to list of predicted labels ints.\n",
        "    predictions_labels += logits.argmax(axis=-1).flatten().tolist()\n",
        "\n",
        "  # calculate the average loss over the training data.\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "\n",
        "  # return all true labels and prediction for future eval.\n",
        "  return true_labels, predictions_labels, avg_epoch_loss\n",
        "\n",
        "\n",
        "\n",
        "def validation(dataloader, device_):\n",
        "  \"\"\"\n",
        "  Validation function to evaluate model performance on validation set.\n",
        "\n",
        "  Args:\n",
        "\n",
        "    dataloader: Parsed data into batches of tensors.\n",
        "\n",
        "    device_: Device used to load tensors.\n",
        "\n",
        "  Returns:\n",
        "\n",
        "    :obj: [True Labels, Predicted Labels, Average Loss]\n",
        "  \"\"\"\n",
        "\n",
        "\n",
        "  global model\n",
        "\n",
        "  # tracking variables\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "\n",
        "  #total loss for this epoch.\n",
        "  total_loss = 0\n",
        "\n",
        "  # Put model in evaluation mode\n",
        "  model.eval()\n",
        "\n",
        "  # Evaluate data for one epoch\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "\n",
        "    # add original labels\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "\n",
        "    # move batch to device\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "\n",
        "    # do not to compute or store gradients, don't need them for validation\n",
        "    with torch.no_grad():\n",
        "\n",
        "        # forward pass, calculate logit predictions.\n",
        "        outputs = model(**batch)\n",
        "\n",
        "        # get logits & loss\n",
        "        loss, logits = outputs[:2]\n",
        "\n",
        "        # move logits and labels to CPU\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "\n",
        "        # accumulate validation loss\n",
        "        total_loss += loss.item()\n",
        "\n",
        "        # get predicitons to list\n",
        "        predict_content = logits.argmax(axis=-1).flatten().tolist()\n",
        "\n",
        "        # update list\n",
        "        predictions_labels += predict_content\n",
        "\n",
        "  # calculate the average loss over the training data.\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "\n",
        "  # return true labels and predicitons\n",
        "  return true_labels, predictions_labels, avg_epoch_loss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ej3snbu3edU"
      },
      "source": [
        "## **Load Model and Tokenizer**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctTxnW903_VF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472,
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            "d20059431c144e89bded712e5251012a"
          ]
        },
        "outputId": "18a0b938-52ae-486f-ada4-6ba8ff056a61"
      },
      "source": [
        "# get model configuration.\n",
        "print('Loading configuraiton...')\n",
        "model_config = GPT2Config.from_pretrained(pretrained_model_name_or_path=model_name_or_path, num_labels=n_labels)\n",
        "\n",
        "# get model 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 padding Token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "\n",
        "# get model.\n",
        "print('Loading model...')\n",
        "model = GPT2ForSequenceClassification.from_pretrained(pretrained_model_name_or_path=model_name_or_path, config=model_config)\n",
        "\n",
        "# resize model embedding to match new tokenizer\n",
        "model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "# fix model padding token id\n",
        "model.config.pad_token_id = model.config.eos_token_id\n",
        "\n",
        "# move model to gpu.\n",
        "model.to(device)\n",
        "print(f'Model loaded to {device}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading configuraiton...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b87bcce189f41ba9762fade47714972"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading tokenizer...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71c6e04beb2f4d8da19b411d2aafacea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5b43d841f574fe18eb5c12f745efe5b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d5706daaa7a4d0e84cac4fa6d6b1ff4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce668fa7c15342ac9d99fc40b276d386"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f72e0b7ceb7946518117c4ca414533d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded to cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Datasets, Dataloaders and Collator"
      ],
      "metadata": {
        "id": "zdquiGV1QlNJ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 705,
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        },
        "outputId": "ed78fbbc-a384-4e49-ab05-54ca208988df"
      },
      "source": [
        "# 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",
        "\n",
        "print('Dealing with Train...')\n",
        "# create pytorch dataset.\n",
        "train_dataset = PropagandaDetectorDataset(path='/content/drive/MyDrive/ANLE_Coursework/Data/propaganda_train',\n",
        "                               use_tokenizer=tokenizer)\n",
        "print(f'Created `train_dataset` with {len(train_dataset)} examples')\n",
        "\n",
        "# move pytorch dataset into dataloader.\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=gpt2_classificaiton_collator)\n",
        "print(f'Created train_dataloader with {len(train_dataloader)} batches')\n",
        "\n",
        "print()\n",
        "\n",
        "print('Dealing with Validation...')\n",
        "# create pytorch dataset.\n",
        "valid_dataset =  PropagandaDetectorDataset(path='/content/drive/MyDrive/ANLE_Coursework/Data/propaganda_val',\n",
        "                               use_tokenizer=tokenizer)\n",
        "print(f'Created valid_dataset with {len(valid_dataset)} examples!')\n",
        "\n",
        "# move pytorch dataset into dataloader.\n",
        "valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, collate_fn=gpt2_classificaiton_collator)\n",
        "print(f'Created eval_dataloader with {len(valid_dataloader)} batches!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dealing with Train...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
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        {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created `train_dataset` with 1223 examples\n",
            "Created train_dataloader with 39 batches\n",
            "\n",
            "Dealing with Validation...\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "Created valid_dataset with 279 examples!\n",
            "Created eval_dataloader with 9 batches!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WiyPMlR8lfga"
      },
      "source": [
        "## **Training**"
      ]
    },
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        },
        "outputId": "2519e3fd-2d1a-4b94-b04e-6c7516435f9e"
      },
      "source": [
        "\n",
        "# define optimiser\n",
        "optimizer = AdamW(model.parameters(),\n",
        "                  lr = learning_rate, # default is 5e-5,\n",
        "                  eps = 1e-8 # default is 1e-8.\n",
        "                  )\n",
        "\n",
        "# calculate total number of steps\n",
        "total_steps = len(train_dataloader) * epochs\n",
        "\n",
        "# create lr scheduler\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer,\n",
        "                                            num_warmup_steps = 0, # Default value in run_glue.py\n",
        "                                            num_training_steps = total_steps)\n",
        "\n",
        "# atore the average loss after each epoch so they can be plotted.\n",
        "all_loss = {'train_loss':[], 'val_loss':[]}\n",
        "all_acc = {'train_acc':[], 'val_acc':[]}\n",
        "\n",
        "#loop through each epoch.\n",
        "print('Epoch')\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print()\n",
        "  print('Training on batches...')\n",
        "  # Perform one full pass over the training set.\n",
        "  train_labels, train_predict, train_loss = train(train_dataloader, optimizer, scheduler, device)\n",
        "  train_acc = accuracy_score(train_labels, train_predict)\n",
        "\n",
        "  # Get prediction form model on validation data.\n",
        "  print('Validation on batches...')\n",
        "  valid_labels, valid_predict, val_loss = validation(valid_dataloader, device)\n",
        "  val_acc = accuracy_score(valid_labels, valid_predict)\n",
        "\n",
        "  # Print loss and accuracy values to see how training evolves.\n",
        "  print(\"  train_loss: %.5f - val_loss: %.5f - train_acc: %.5f - valid_acc: %.5f\"%(train_loss, val_loss, train_acc, val_acc))\n",
        "  print()\n",
        "\n",
        "  # Store the loss value for plotting the learning curve.\n",
        "  all_loss['train_loss'].append(train_loss)\n",
        "  all_loss['val_loss'].append(val_loss)\n",
        "  all_acc['train_acc'].append(train_acc)\n",
        "  all_acc['val_acc'].append(val_acc)\n",
        "\n",
        "# Plot loss curves.\n",
        "plot_dict(all_loss, use_xlabel='Epochs', use_ylabel='Value', use_linestyles=['-', '--'])\n",
        "\n",
        "# Plot accuracy curves.\n",
        "plot_dict(all_acc, use_xlabel='Epochs', use_ylabel='Value', use_linestyles=['-', '--'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34641a1215404a3eb6398483b6a8d9c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4940854f1d44491d96b57a357528259c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 2.23401 - val_loss: 1.91415 - train_acc: 0.20442 - valid_acc: 0.22581\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4ddf7f0b3d0d488ba292ffca2aed92c7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "77063decff91418ba48498e430cc63f6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 1.74263 - val_loss: 1.72566 - train_acc: 0.30581 - valid_acc: 0.39068\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e4faa94d37d54c9e9d72bee3d39aae14"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 1.50821 - val_loss: 1.58796 - train_acc: 0.43254 - valid_acc: 0.40502\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5266832655848a7b86da8e05e71e868"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a091d827e3a840aab0503c9c6fc4f4f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 1.26499 - val_loss: 1.62115 - train_acc: 0.52903 - valid_acc: 0.44444\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ac3abcf7c144921b97ee3fed2fe2e27"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 1.04929 - val_loss: 1.55406 - train_acc: 0.61079 - valid_acc: 0.54839\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "75e16eb752c74c3e81e3c4de607c2108"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "  train_loss: 0.92295 - val_loss: 1.48418 - train_acc: 0.67702 - valid_acc: 0.59140\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "name": "stdout",
          "text": [
            "  train_loss: 0.74768 - val_loss: 1.55121 - train_acc: 0.73426 - valid_acc: 0.57706\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            }
          },
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.69757 - val_loss: 1.49546 - train_acc: 0.74652 - valid_acc: 0.60932\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ml_things/plot_functions.py:409: DeprecationWarning: `magnify` needs to have value in [0,1]! `1.2` will be converted to `0.1` as default.\n",
            "  warnings.warn(f'`magnify` needs to have value in [0,1]! `{magnify}` will be converted to `0.1` as default.',\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ml_things/plot_functions.py:409: DeprecationWarning: `magnify` needs to have value in [0,1]! `1.2` will be converted to `0.1` as default.\n",
            "  warnings.warn(f'`magnify` needs to have value in [0,1]! `{magnify}` will be converted to `0.1` as default.',\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Svsqk7roQJ2"
      },
      "source": [
        "## **Evaluation**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Sifp6ocoSng",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "cab7d4ff46c34c3f9871ffa2bf4675a2",
            "fafaf918584b4891b6f685791016e150",
            "3e7ae73e56da41a1b9edf3ee38e4dd98",
            "ba76f51efdee4657a704433a2d618a18",
            "d7b1d55faa9e4b76bc1fb6054080935b",
            "3653908cd5074aa39b19d16622d4df57",
            "3f9e5928170844f4af6210f4c8d554ea",
            "89bfbf7f227e4681b50fb7b38d161d88",
            "dbccd4336d3647c9934665923cfb7a94",
            "4f25a4ac9b0c4c37a0ef712cc34de357",
            "247b86800dad49b0bdc0fa29fc55c484"
          ]
        },
        "outputId": "dde283d7-666a-43c2-9773-ee149bef11aa"
      },
      "source": [
        "# Get prediction form model on validation data.\n",
        "true_labels, predictions_labels, avg_epoch_loss = validation(valid_dataloader, device)\n",
        "\n",
        "# Create evaluation report.\n",
        "evaluation_report = classification_report(true_labels, predictions_labels, labels=list(labels_ids.values()), target_names=list(labels_ids.keys()))\n",
        "# Show evaluation report.\n",
        "print(evaluation_report)\n",
        "print(f\"epochs: {epochs}, learning rate:{learning_rate}, max length:{max_length}\")\n",
        "# Plot confusion matrix.\n",
        "plot_confusion_matrix(y_true=true_labels, y_pred=predictions_labels, classes=list(labels_ids.keys()), normalize=True, title=\"Normalised Confusion Matrix of GPT2-based cassifier on Task 2.\", magnify=0.1);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cab7d4ff46c34c3f9871ffa2bf4675a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           precision    recall  f1-score   support\n",
            "\n",
            " appeal_to_fear_prejudice       0.75      0.56      0.64        43\n",
            "causal_oversimplification       0.48      0.65      0.55        31\n",
            "                    doubt       0.52      0.58      0.55        38\n",
            "exaggeration,minimisation       0.43      0.43      0.43        28\n",
            "              flag_waving       0.75      0.85      0.80        39\n",
            "          loaded_language       0.69      0.59      0.64        37\n",
            "    name_calling,labeling       0.66      0.74      0.70        31\n",
            "               repetition       0.58      0.44      0.50        32\n",
            "\n",
            "                 accuracy                           0.61       279\n",
            "                macro avg       0.61      0.60      0.60       279\n",
            "             weighted avg       0.62      0.61      0.61       279\n",
            "\n",
            "epochs: 8, learning rate:0.0001, max length:160\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ml_things/plot_functions.py:646: DeprecationWarning: `title` will be deprecated in future updates. Use `use_title` in stead!\n",
            "  warnings.warn(\"`title` will be deprecated in future updates. Use `use_title` in stead!\", DeprecationWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}