sample_code_training_results.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1k8MncYMmue"
      },
      "source": [
        "# 1. Introduction\n",
        "\n",
        "In this .ipynb we will train the models on the YoutubeAudit data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUHlcbMH1o2G",
        "outputId": "0699c355-93c6-49c3-e70d-0d3bcc2e88aa"
      },
      "outputs": [],
      "source": [
        "# Standard Imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "\n",
        "# Import ML Libraries (Check if cuda is available)\n",
        "import torch\n",
        "torch.cuda.is_available()\n",
        "\n",
        "# solution for pip install transformers encoding ANSI issue\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding\n",
        "\n",
        "# Import libraries\n",
        "import transformers\n",
        "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
        "import logging\n",
        "\n",
        "# sklearn standard helpers\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "73PlJuzv1x-y"
      },
      "outputs": [],
      "source": [
        "misinformation_df = pd.read_csv('path/to/misinf/df')\n",
        "not_misinf_df = pd.read_csv('path/to/not_misinf/df')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WD4ZaHcZOWNR"
      },
      "outputs": [],
      "source": [
        "import ast\n",
        "def get_transcript(transcript_list_str):\n",
        "    transcript_list = ast.literal_eval(transcript_list_str)\n",
        "    transcript_text = \"\"\n",
        "    for line in transcript_list:\n",
        "        transcript_text += \" \" + line[\"text\"]\n",
        "    return transcript_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FAWIrbhyOeYk"
      },
      "outputs": [],
      "source": [
        "misinformation_df['transcript_text'] = misinformation_df['transcript'].apply(get_transcript)\n",
        "not_misinf_df['transcript_text'] = not_misinf_df['transcript'].apply(get_transcript)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEQG36ePPLAq"
      },
      "source": [
        "# 2. Clean Transcripts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LI5m0rieOo-8"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "def is_timestamp(s):\n",
        "    return bool(re.match(r'\\d{2}:\\d{2}:\\d{2}\\.\\d{3} --> \\d{2}:\\d{2}:\\d{2}\\.\\d{3}', s))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OcDJ74KFPTIH"
      },
      "outputs": [],
      "source": [
        "misinformation_df = misinformation_df[~misinformation_df['transcript_text'].apply(is_timestamp)]\n",
        "not_misinf_df = not_misinf_df[~not_misinf_df['transcript_text'].apply(is_timestamp)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JwDY2CRQPfsB"
      },
      "outputs": [],
      "source": [
        "def is_mostly_text(transcript, threshold=0.7):\n",
        "    if not transcript:\n",
        "        return False\n",
        "    text_chars = sum(c.isalpha() or c.isspace() for c in transcript)\n",
        "    return (text_chars / len(transcript)) >= threshold\n",
        "\n",
        "misinformation_df['is_text'] = misinformation_df['transcript_text'].apply(is_mostly_text)\n",
        "not_misinf_df['is_text'] = not_misinf_df['transcript_text'].apply(is_mostly_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MFkSrNwXPxEI"
      },
      "outputs": [],
      "source": [
        "def has_enough_words(transcript, min_words=10):\n",
        "    return len(transcript.split()) >= min_words\n",
        "\n",
        "misinformation_df['has_enough_words'] = misinformation_df['transcript_text'].apply(has_enough_words)\n",
        "not_misinf_df['has_enough_words'] = not_misinf_df['transcript_text'].apply(has_enough_words)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9X58M-KRP06p"
      },
      "outputs": [],
      "source": [
        "from langdetect import detect\n",
        "\n",
        "def is_english(transcript):\n",
        "    try:\n",
        "        return detect(transcript) == 'en'\n",
        "    except:\n",
        "        return False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LPXOmeqtQEpD"
      },
      "outputs": [],
      "source": [
        "misinformation_df['is_english'] = misinformation_df['transcript_text'].apply(is_english)\n",
        "not_misinf_df['is_english'] = not_misinf_df['transcript_text'].apply(is_english)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7kxrPUmFQKLu"
      },
      "outputs": [],
      "source": [
        "misinformation_df = misinformation_df[misinformation_df['is_text'] & misinformation_df['has_enough_words'] & misinformation_df['is_english']]\n",
        "not_misinf_df = not_misinf_df[not_misinf_df['is_text'] & not_misinf_df['has_enough_words'] & not_misinf_df['is_english']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NqvmU7X_QQA0"
      },
      "outputs": [],
      "source": [
        "not_misinf_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iilssz7dQX0o",
        "outputId": "10f2b3d7-0082-415b-817d-c7b3dccb2698"
      },
      "outputs": [],
      "source": [
        "# Remove special characters\n",
        "misinformation_df['transcript_text'] = misinformation_df['transcript_text'].str.replace(r'[^\\w\\s]', '', regex=True)\n",
        "not_misinf_df['transcript_text'] = not_misinf_df['transcript_text'].str.replace(r'[^\\w\\s]', '', regex=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xI0jt5_XQ2U4"
      },
      "source": [
        "# 3. Modelling with Simple transformers\n",
        "We use the sliding_window: `sliding_window=True` which is suited for long document classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBC3oUF7SxLm"
      },
      "source": [
        "## Data Preparations for transformer model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLRtTaNlQnqt",
        "outputId": "b209a8df-33d8-46a8-f868-dce883606fe9"
      },
      "outputs": [],
      "source": [
        "# Add a 'label' column to each dataframe\n",
        "misinformation_df['label'] = 'fake'\n",
        "not_misinf_df['label'] = 'real'\n",
        "\n",
        "# Create copies of the dataframes\n",
        "fake = misinformation_df.copy()\n",
        "real = not_misinf_df.copy()\n",
        "\n",
        "# Add also numeric label\n",
        "fake['numeric_label'] = 1\n",
        "real['numeric_label'] = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z8J3wJD_Sago"
      },
      "outputs": [],
      "source": [
        "# Split the pseudoscience dataframe\n",
        "fake_train, fake_test = train_test_split(fake, test_size=0.2, random_state=42)\n",
        "\n",
        "# Split the science dataframe\n",
        "real_train, real_test = train_test_split(real, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x5kjB14gSgd9"
      },
      "outputs": [],
      "source": [
        "train = pd.concat([real_train, fake_train], ignore_index=True)\n",
        "test = pd.concat([real_test, fake_test], ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_LONcdBRpm7",
        "outputId": "99cdc7f7-c62e-4614-c590-19374b70d90a"
      },
      "outputs": [],
      "source": [
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)\n",
        "\n",
        "# Preparing train data\n",
        "train_set = train[['transcript_text', 'numeric_label']]\n",
        "test_set = test[['transcript_text', 'numeric_label']]\n",
        "train_set.numeric_label.value_counts(), test_set.numeric_label.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3z_qp4_Syyz"
      },
      "source": [
        "## Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qctl7gFcqBTO"
      },
      "source": [
        "\n",
        "### Bert"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rsvZKuCVSMR-"
      },
      "outputs": [],
      "source": [
        "# Optional model configuration\n",
        "## multiprocessing should be disabled\n",
        "model_args = ClassificationArgs(\n",
        "    num_train_epochs=5,\n",
        "    overwrite_output_dir=True,\n",
        "    use_multiprocessing=False,\n",
        "    use_multiprocessing_for_evaluation=False,\n",
        "    sliding_window=True\n",
        ")\n",
        "\n",
        "# Create a ClassificationModel\n",
        "model = ClassificationModel(\n",
        "    'bert',\n",
        "    'bert-base-uncased',\n",
        "    num_labels=2,\n",
        "    args=model_args,\n",
        ")\n",
        "\n",
        "\n",
        "# Train the model\n",
        "model.train_model(train_set)\n",
        "\n",
        "# Evaluate the model\n",
        "result, model_outputs, wrong_predictions = model.eval_model(test_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4xbzPGtuS28y"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "def f1_multiclass(labels, preds):\n",
        "    return f1_score(labels, preds, average='micro')\n",
        "\n",
        "result, model_outputs, wrong_predictions = model.eval_model(test_set, f1=f1_multiclass, acc=accuracy_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
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        },
        "id": "Z2xokBNcpUSH",
        "outputId": "521dc3bb-0343-4aad-9731-20f64b3f2a54"
      },
      "outputs": [],
      "source": [
        "y_preds, raw_outputs= model.predict(list(test_set['transcript_text']))\n",
        "y_true = test_set['numeric_label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w_smEkV-pefK"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_score, recall_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELtXjc8fphRV",
        "outputId": "d86c12ff-7ea6-4dce-f570-23282d35c853"
      },
      "outputs": [],
      "source": [
        "print('Precision: ' + str(precision_score(y_true, y_preds, average='micro')))\n",
        "print('Recall: ' + str(recall_score(y_true, y_preds, average='micro')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uV-all_HphtQ"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "from sklearn.metrics import precision_recall_curve, average_precision_score, roc_curve, auc\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "y_test = y_true"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "8jrHhTa6pyow",
        "outputId": "b0894043-c861-4158-d9e7-73be8b1d874e"
      },
      "outputs": [],
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, y_preds)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(fpr, tpr, lw=2, label='ROC curve (AUC = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Hypothetical Random clf: Random guess')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "jdQPlVqSp0oo",
        "outputId": "88ff4eb8-4f50-4c4f-c612-fead167deff8"
      },
      "outputs": [],
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_preds)\n",
        "avg_precision = average_precision_score(y_test, y_preds)\n",
        "\n",
        "plt.plot(recall, precision, lw=2, label='Precision-Recall curve (Avg Precision = %0.2f)' % avg_precision)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "fU0WTi54p2Hj",
        "outputId": "947fe8a0-d4e4-4796-eeb3-74cbf3660e9f"
      },
      "outputs": [],
      "source": [
        "cm = confusion_matrix(y_test, y_preds)\n",
        "labels=['Real', 'Misinformation']\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=labels, yticklabels=labels)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lLklyWhp7kS"
      },
      "source": [
        "### Electra"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "15TI2L3dp3Qv",
        "outputId": "4ece639a-cd16-43ff-aec3-c4084a426d6a"
      },
      "outputs": [],
      "source": [
        "electra_args = ClassificationArgs(\n",
        "    num_train_epochs=5,\n",
        "    overwrite_output_dir=True,\n",
        "    use_multiprocessing=False,\n",
        "    use_multiprocessing_for_evaluation=False,\n",
        "    sliding_window=True\n",
        ")\n",
        "\n",
        "# make sure we set the early stopping\n",
        "electra_args.use_early_stopping = True\n",
        "\n",
        "# Create a ClassificationModel\n",
        "electra = ClassificationModel(\n",
        "    'electra',\n",
        "    'google/electra-base-discriminator',\n",
        "    num_labels=2,\n",
        "    args=electra_args\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "electra.train_model(train_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
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            "c7d78077971644acac8eceee3d09c3e3",
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            "0045a660c2dc48b9a111a3e1f4f2d758",
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            "762b49d0957d4c418662a13e0eb552ae",
            "d1640a7590454748bf4e34fd0690d762",
            "704b7d26a9cb4d3e992e648af5aa773b",
            "2a9ab86c8bd143b0b880da8bacb877ac",
            "461f2b7e3851406680b31dfc53785228",
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            "b8108d95b32a43baaf8d53473d56343a",
            "bb3b6d79541443e0bf1413d6e4864e8f"
          ]
        },
        "id": "S4En3J1PtiFW",
        "outputId": "aa53f249-4761-4296-a563-14dcd8ff0707"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "def f1_multiclass(labels, preds):\n",
        "    return f1_score(labels, preds, average='micro')\n",
        "\n",
        "result, model_outputs, wrong_predictions = electra.eval_model(test_set, f1=f1_multiclass, acc=accuracy_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSOK9sZY1GMU",
        "outputId": "aa3f870f-146d-4061-96d3-fb2bf66f8466"
      },
      "outputs": [],
      "source": [
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
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        },
        "id": "tGJdywTI1Qnq",
        "outputId": "e68aee25-38f9-4284-cc56-508c20ef1909"
      },
      "outputs": [],
      "source": [
        "y_preds, raw_outputs= electra.predict(list(test_set['transcript_text']))\n",
        "y_true = test_set['numeric_label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rb7xUelK1YJp",
        "outputId": "ca5eb0af-5a19-46d5-b135-6395ef3f9e2e"
      },
      "outputs": [],
      "source": [
        "print('Precision: ' + str(precision_score(y_true, y_preds, average='micro')))\n",
        "print('Recall: ' + str(recall_score(y_true, y_preds, average='micro')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "39FemOAX1e3E"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "from sklearn.metrics import precision_recall_curve, average_precision_score, roc_curve, auc\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "y_test = y_true"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "kdbKGhUh1gLa",
        "outputId": "0526eeca-959e-47cd-c5c9-4cad35967422"
      },
      "outputs": [],
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, y_preds)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(fpr, tpr, lw=2, label='ROC curve (AUC = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Hypothetical Random clf: Random guess')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "6eQhWB1N1v8y",
        "outputId": "fd573bc5-a4e6-470f-a7fe-cdbd0f63253e"
      },
      "outputs": [],
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_preds)\n",
        "avg_precision = average_precision_score(y_test, y_preds)\n",
        "\n",
        "plt.plot(recall, precision, lw=2, label='Precision-Recall curve (Avg Precision = %0.2f)' % avg_precision)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "QDfDJ4x31xVa",
        "outputId": "850fcd11-45d0-489f-b529-d4537e20b0ff"
      },
      "outputs": [],
      "source": [
        "cm = confusion_matrix(y_test, y_preds)\n",
        "labels=['Real', 'Misinformation']\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=labels, yticklabels=labels)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkrTEckJ2Aws"
      },
      "source": [
        "### Roberta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "wIo24V8B1-P_",
        "outputId": "5ee571da-6e39-40c8-a3e8-1a05c15ef115"
      },
      "outputs": [],
      "source": [
        "roberta_args = ClassificationArgs(\n",
        "    num_train_epochs=5,\n",
        "    overwrite_output_dir=True,\n",
        "    use_multiprocessing=False,\n",
        "    use_multiprocessing_for_evaluation=False,\n",
        "    sliding_window=True\n",
        ")\n",
        "\n",
        "# make sure we set the early stopping\n",
        "roberta_args.use_early_stopping = True\n",
        "\n",
        "# Create a ClassificationModel\n",
        "roberta = ClassificationModel(\n",
        "    \"roberta\",\n",
        "    \"roberta-base\",\n",
        "    num_labels=2,\n",
        "    args=roberta_args\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "roberta.train_model(train_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "15454aee-cf37-40ca-bfcd-e58762a48eee"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "def f1_multiclass(labels, preds):\n",
        "    return f1_score(labels, preds, average='micro')\n",
        "\n",
        "result, model_outputs, wrong_predictions = roberta.eval_model(test_set, f1=f1_multiclass, acc=accuracy_score)"
      ]
    },
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      "execution_count": null,
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        "id": "JHOOhnwzAEkX",
        "outputId": "c6166a10-6b50-4279-ddc5-c1da8fd3b5f3"
      },
      "outputs": [],
      "source": [
        "y_preds, raw_outputs= roberta.predict(list(test_set['transcript_text']))\n",
        "y_true = test_set['numeric_label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SajWxQEUAHQ3",
        "outputId": "f5b7dc48-51ea-4df1-f2f0-e2dae350c871"
      },
      "outputs": [],
      "source": [
        "print('Precision: ' + str(precision_score(y_true, y_preds, average='micro')))\n",
        "print('Recall: ' + str(recall_score(y_true, y_preds, average='micro')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V76-LF6LAPHO"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "from sklearn.metrics import precision_recall_curve, average_precision_score, roc_curve, auc\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "y_test = y_true"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "XfPeVXJpARDY",
        "outputId": "1aac5d26-6947-45f6-b005-6a385c5ee598"
      },
      "outputs": [],
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, y_preds)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(fpr, tpr, lw=2, label='ROC curve (AUC = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Hypothetical Random clf: Random guess')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        },
        "id": "JFZlwoigATPp",
        "outputId": "53420deb-1d26-40b3-cbf8-b139c591f0f9"
      },
      "outputs": [],
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_preds)\n",
        "avg_precision = average_precision_score(y_test, y_preds)\n",
        "\n",
        "plt.plot(recall, precision, lw=2, label='Precision-Recall curve (Avg Precision = %0.2f)' % avg_precision)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "yNKIdKd9AV6E",
        "outputId": "c72984cc-efc7-44f0-c584-81f508ebc2e5"
      },
      "outputs": [],
      "source": [
        "cm = confusion_matrix(y_test, y_preds)\n",
        "labels=['Real', 'Misinformation']\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=labels, yticklabels=labels)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()"
      ]
    }
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