stance_detector.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#### Code to train the model <br>"
      ],
      "metadata": {
        "id": "thdBP2dEHt43"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extracting the files"
      ],
      "metadata": {
        "id": "uOxNuHfCBPfa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!tar -xjf rumoureval2019.tar.bz2"
      ],
      "metadata": {
        "id": "53d4nBTwkd2n"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip rumoureval2019/rumoureval-2019-test-data.zip -d 'rumoureval2019/'"
      ],
      "metadata": {
        "id": "Y60OFg6RgpJ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip rumoureval2019/rumoureval-2019-training-data.zip -d 'rumoureval2019/'"
      ],
      "metadata": {
        "id": "fpNjczyvko2f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Execute the code to process twitter data"
      ],
      "metadata": {
        "id": "R-JVPldLAwlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python Process_Twitter_Data.py"
      ],
      "metadata": {
        "id": "UCzd_D9rx7aE"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Execute the code to process reddit data"
      ],
      "metadata": {
        "id": "IbMX2XWuAzfi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python Process_Reddit_Data.py"
      ],
      "metadata": {
        "id": "_o4nraT7yELw"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading the cleaned data"
      ],
      "metadata": {
        "id": "3neR-2nqBKG3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from Data_Cleaning_functions import processStanceData\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "\n",
        "\n",
        "#Seeding for deterministic results\n",
        "RANDOM_SEED = 16\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "HIDDEN_LAYER_UNITS = 128\n",
        "\n",
        "CLASS_NAMES = ['support', 'deny', 'query', 'comment']\n",
        "EPOCHS      = 50\n",
        "\n",
        "\n",
        "\n",
        "#Reading Twitter and Reddit data (train, dev and test) as dataFrames\n",
        "twitterTrainDf = pd.read_csv('TwitterTrainDataSrc.csv')\n",
        "redditTrainDf  = pd.read_csv('RedditTrainDataSrc.csv')\n",
        "\n",
        "twitterDevDf   = pd.read_csv('TwitterDevDataSrc.csv')\n",
        "redditDevDf    = pd.read_csv('RedditDevDataSrc.csv')\n",
        "\n",
        "twitterTestDf  = pd.read_csv('TwitterTestDataSrc.csv')\n",
        "redditTestDf   = pd.read_csv('RedditTestDataSrc.csv')\n",
        "\n",
        "#Processing Twitter and Reddit dataframe containig training data\n",
        "trainDf = processStanceData(twitterTrainDf, redditTrainDf)\n",
        "trainDf.drop(np.where(trainDf!= trainDf)[0][0], inplace=True) # this line contains Nan Value\n",
        "\n",
        "devDf = processStanceData(twitterDevDf, redditDevDf)\n",
        "testDf = processStanceData(twitterTestDf, redditTestDf)\n",
        "\n",
        "x_train = trainDf['TextSrcInre'].tolist()\n",
        "y_train = trainDf['labelValue'].tolist()\n",
        "\n",
        "\n",
        "x_dev  = devDf['TextSrcInre'].tolist()\n",
        "y_dev  = devDf['labelValue'].tolist()\n",
        "\n",
        "x_test = testDf['TextSrcInre'].tolist()\n",
        "y_test = testDf['labelValue'].tolist()"
      ],
      "metadata": {
        "id": "TRC74VIL0oZF"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from StanceDetectorModel import StanceDetector, Tfidf_Nn,tfidf\n",
        "\n",
        "model = Tfidf_Nn() # the model uses the TfidfVectorizer\n",
        "stanceDetector = StanceDetector(model,tfidf) # the class takes in as arguments : the model, and the vectorizer\n",
        "history = stanceDetector.fit(x_train,y_train,x_dev,y_dev,epochs=100,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRpze0OwyMXg",
        "outputId": "7d72c765-ab57-4280-929e-611e7dcbc5f8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1/100..  Training Loss: 1.388..  Training Accuracy: 0.177 Dev Loss: 1.379..  Dev Accuracy: 0.078\n",
            "Epoch: 2/100..  Training Loss: 1.327..  Training Accuracy: 0.234 Dev Loss: 1.367..  Dev Accuracy: 0.082\n",
            "Epoch: 3/100..  Training Loss: 1.243..  Training Accuracy: 0.218 Dev Loss: 1.356..  Dev Accuracy: 0.079\n",
            "Epoch: 4/100..  Training Loss: 1.185..  Training Accuracy: 0.217 Dev Loss: 1.354..  Dev Accuracy: 0.081\n",
            "Epoch: 5/100..  Training Loss: 1.154..  Training Accuracy: 0.235 Dev Loss: 1.360..  Dev Accuracy: 0.081\n",
            "Epoch: 6/100..  Training Loss: 1.133..  Training Accuracy: 0.257 Dev Loss: 1.371..  Dev Accuracy: 0.081\n",
            "Epoch: 7/100..  Training Loss: 1.113..  Training Accuracy: 0.281 Dev Loss: 1.383..  Dev Accuracy: 0.083\n",
            "Epoch: 8/100..  Training Loss: 1.092..  Training Accuracy: 0.306 Dev Loss: 1.395..  Dev Accuracy: 0.106\n",
            "Epoch: 9/100..  Training Loss: 1.071..  Training Accuracy: 0.332 Dev Loss: 1.400..  Dev Accuracy: 0.187\n",
            "Epoch: 10/100..  Training Loss: 1.050..  Training Accuracy: 0.370 Dev Loss: 1.394..  Dev Accuracy: 0.304\n",
            "Epoch: 11/100..  Training Loss: 1.031..  Training Accuracy: 0.405 Dev Loss: 1.389..  Dev Accuracy: 0.407\n",
            "Epoch: 12/100..  Training Loss: 1.018..  Training Accuracy: 0.441 Dev Loss: 1.387..  Dev Accuracy: 0.468\n",
            "Epoch: 13/100..  Training Loss: 1.008..  Training Accuracy: 0.473 Dev Loss: 1.389..  Dev Accuracy: 0.511\n",
            "Epoch: 14/100..  Training Loss: 0.996..  Training Accuracy: 0.505 Dev Loss: 1.392..  Dev Accuracy: 0.542\n",
            "Epoch: 15/100..  Training Loss: 0.983..  Training Accuracy: 0.527 Dev Loss: 1.396..  Dev Accuracy: 0.565\n",
            "Epoch: 16/100..  Training Loss: 0.973..  Training Accuracy: 0.546 Dev Loss: 1.399..  Dev Accuracy: 0.578\n",
            "Epoch: 17/100..  Training Loss: 0.964..  Training Accuracy: 0.558 Dev Loss: 1.401..  Dev Accuracy: 0.587\n",
            "Epoch: 18/100..  Training Loss: 0.956..  Training Accuracy: 0.565 Dev Loss: 1.402..  Dev Accuracy: 0.579\n",
            "Epoch: 19/100..  Training Loss: 0.950..  Training Accuracy: 0.571 Dev Loss: 1.404..  Dev Accuracy: 0.588\n",
            "Epoch: 20/100..  Training Loss: 0.946..  Training Accuracy: 0.575 Dev Loss: 1.407..  Dev Accuracy: 0.595\n",
            "Epoch: 21/100..  Training Loss: 0.941..  Training Accuracy: 0.585 Dev Loss: 1.410..  Dev Accuracy: 0.613\n",
            "Epoch: 22/100..  Training Loss: 0.934..  Training Accuracy: 0.600 Dev Loss: 1.412..  Dev Accuracy: 0.631\n",
            "Epoch: 23/100..  Training Loss: 0.929..  Training Accuracy: 0.614 Dev Loss: 1.413..  Dev Accuracy: 0.636\n",
            "Epoch: 24/100..  Training Loss: 0.925..  Training Accuracy: 0.624 Dev Loss: 1.413..  Dev Accuracy: 0.638\n",
            "Epoch: 25/100..  Training Loss: 0.921..  Training Accuracy: 0.634 Dev Loss: 1.414..  Dev Accuracy: 0.654\n",
            "Epoch: 26/100..  Training Loss: 0.917..  Training Accuracy: 0.643 Dev Loss: 1.414..  Dev Accuracy: 0.655\n",
            "Epoch: 27/100..  Training Loss: 0.913..  Training Accuracy: 0.655 Dev Loss: 1.415..  Dev Accuracy: 0.658\n",
            "Epoch: 28/100..  Training Loss: 0.910..  Training Accuracy: 0.666 Dev Loss: 1.416..  Dev Accuracy: 0.662\n",
            "Epoch: 29/100..  Training Loss: 0.906..  Training Accuracy: 0.673 Dev Loss: 1.415..  Dev Accuracy: 0.661\n",
            "Epoch: 30/100..  Training Loss: 0.904..  Training Accuracy: 0.674 Dev Loss: 1.415..  Dev Accuracy: 0.661\n",
            "Epoch: 31/100..  Training Loss: 0.902..  Training Accuracy: 0.678 Dev Loss: 1.415..  Dev Accuracy: 0.660\n",
            "Epoch: 32/100..  Training Loss: 0.900..  Training Accuracy: 0.683 Dev Loss: 1.415..  Dev Accuracy: 0.662\n",
            "Epoch: 33/100..  Training Loss: 0.900..  Training Accuracy: 0.689 Dev Loss: 1.414..  Dev Accuracy: 0.666\n",
            "Epoch: 34/100..  Training Loss: 0.897..  Training Accuracy: 0.692 Dev Loss: 1.415..  Dev Accuracy: 0.667\n",
            "Epoch: 35/100..  Training Loss: 0.894..  Training Accuracy: 0.698 Dev Loss: 1.415..  Dev Accuracy: 0.669\n",
            "Epoch: 36/100..  Training Loss: 0.892..  Training Accuracy: 0.705 Dev Loss: 1.415..  Dev Accuracy: 0.672\n",
            "Epoch: 37/100..  Training Loss: 0.891..  Training Accuracy: 0.710 Dev Loss: 1.415..  Dev Accuracy: 0.673\n",
            "Epoch: 38/100..  Training Loss: 0.889..  Training Accuracy: 0.717 Dev Loss: 1.415..  Dev Accuracy: 0.675\n",
            "Epoch: 39/100..  Training Loss: 0.887..  Training Accuracy: 0.719 Dev Loss: 1.414..  Dev Accuracy: 0.679\n",
            "Epoch: 40/100..  Training Loss: 0.886..  Training Accuracy: 0.723 Dev Loss: 1.413..  Dev Accuracy: 0.679\n",
            "Epoch: 41/100..  Training Loss: 0.884..  Training Accuracy: 0.726 Dev Loss: 1.413..  Dev Accuracy: 0.681\n",
            "Epoch: 42/100..  Training Loss: 0.883..  Training Accuracy: 0.729 Dev Loss: 1.412..  Dev Accuracy: 0.679\n",
            "Epoch: 43/100..  Training Loss: 0.882..  Training Accuracy: 0.731 Dev Loss: 1.412..  Dev Accuracy: 0.688\n",
            "Epoch: 44/100..  Training Loss: 0.881..  Training Accuracy: 0.736 Dev Loss: 1.412..  Dev Accuracy: 0.690\n",
            "Epoch: 45/100..  Training Loss: 0.881..  Training Accuracy: 0.737 Dev Loss: 1.412..  Dev Accuracy: 0.691\n",
            "Epoch: 46/100..  Training Loss: 0.879..  Training Accuracy: 0.740 Dev Loss: 1.412..  Dev Accuracy: 0.692\n",
            "Epoch: 47/100..  Training Loss: 0.878..  Training Accuracy: 0.741 Dev Loss: 1.412..  Dev Accuracy: 0.690\n",
            "Epoch: 48/100..  Training Loss: 0.877..  Training Accuracy: 0.742 Dev Loss: 1.413..  Dev Accuracy: 0.691\n",
            "Epoch: 49/100..  Training Loss: 0.876..  Training Accuracy: 0.743 Dev Loss: 1.413..  Dev Accuracy: 0.693\n",
            "Epoch: 50/100..  Training Loss: 0.875..  Training Accuracy: 0.748 Dev Loss: 1.413..  Dev Accuracy: 0.689\n",
            "Epoch: 51/100..  Training Loss: 0.875..  Training Accuracy: 0.751 Dev Loss: 1.413..  Dev Accuracy: 0.692\n",
            "Epoch: 52/100..  Training Loss: 0.874..  Training Accuracy: 0.754 Dev Loss: 1.413..  Dev Accuracy: 0.692\n",
            "Epoch: 53/100..  Training Loss: 0.873..  Training Accuracy: 0.757 Dev Loss: 1.413..  Dev Accuracy: 0.692\n",
            "Epoch: 54/100..  Training Loss: 0.872..  Training Accuracy: 0.758 Dev Loss: 1.413..  Dev Accuracy: 0.693\n",
            "Epoch: 55/100..  Training Loss: 0.872..  Training Accuracy: 0.759 Dev Loss: 1.413..  Dev Accuracy: 0.691\n",
            "Epoch: 56/100..  Training Loss: 0.871..  Training Accuracy: 0.761 Dev Loss: 1.413..  Dev Accuracy: 0.690\n",
            "Epoch: 57/100..  Training Loss: 0.870..  Training Accuracy: 0.761 Dev Loss: 1.414..  Dev Accuracy: 0.690\n",
            "Epoch: 58/100..  Training Loss: 0.870..  Training Accuracy: 0.763 Dev Loss: 1.414..  Dev Accuracy: 0.690\n",
            "Epoch: 59/100..  Training Loss: 0.869..  Training Accuracy: 0.764 Dev Loss: 1.415..  Dev Accuracy: 0.692\n",
            "Epoch: 60/100..  Training Loss: 0.868..  Training Accuracy: 0.767 Dev Loss: 1.416..  Dev Accuracy: 0.693\n",
            "Epoch: 61/100..  Training Loss: 0.867..  Training Accuracy: 0.770 Dev Loss: 1.417..  Dev Accuracy: 0.695\n",
            "Epoch: 62/100..  Training Loss: 0.867..  Training Accuracy: 0.771 Dev Loss: 1.418..  Dev Accuracy: 0.694\n",
            "Epoch: 63/100..  Training Loss: 0.866..  Training Accuracy: 0.773 Dev Loss: 1.418..  Dev Accuracy: 0.696\n",
            "Epoch: 64/100..  Training Loss: 0.865..  Training Accuracy: 0.774 Dev Loss: 1.418..  Dev Accuracy: 0.694\n",
            "Epoch: 65/100..  Training Loss: 0.865..  Training Accuracy: 0.772 Dev Loss: 1.418..  Dev Accuracy: 0.694\n",
            "Epoch: 66/100..  Training Loss: 0.864..  Training Accuracy: 0.774 Dev Loss: 1.418..  Dev Accuracy: 0.694\n",
            "Epoch: 67/100..  Training Loss: 0.864..  Training Accuracy: 0.776 Dev Loss: 1.418..  Dev Accuracy: 0.692\n",
            "Epoch: 68/100..  Training Loss: 0.863..  Training Accuracy: 0.778 Dev Loss: 1.418..  Dev Accuracy: 0.691\n",
            "Epoch: 69/100..  Training Loss: 0.863..  Training Accuracy: 0.780 Dev Loss: 1.418..  Dev Accuracy: 0.696\n",
            "Epoch: 70/100..  Training Loss: 0.862..  Training Accuracy: 0.782 Dev Loss: 1.419..  Dev Accuracy: 0.695\n",
            "Epoch: 71/100..  Training Loss: 0.862..  Training Accuracy: 0.783 Dev Loss: 1.419..  Dev Accuracy: 0.696\n",
            "Epoch: 72/100..  Training Loss: 0.862..  Training Accuracy: 0.783 Dev Loss: 1.419..  Dev Accuracy: 0.688\n",
            "Epoch: 73/100..  Training Loss: 0.861..  Training Accuracy: 0.785 Dev Loss: 1.420..  Dev Accuracy: 0.687\n",
            "Epoch: 74/100..  Training Loss: 0.861..  Training Accuracy: 0.788 Dev Loss: 1.420..  Dev Accuracy: 0.686\n",
            "Epoch: 75/100..  Training Loss: 0.861..  Training Accuracy: 0.788 Dev Loss: 1.421..  Dev Accuracy: 0.686\n",
            "Epoch: 76/100..  Training Loss: 0.860..  Training Accuracy: 0.791 Dev Loss: 1.421..  Dev Accuracy: 0.688\n",
            "Epoch: 77/100..  Training Loss: 0.860..  Training Accuracy: 0.793 Dev Loss: 1.421..  Dev Accuracy: 0.689\n",
            "Epoch: 78/100..  Training Loss: 0.860..  Training Accuracy: 0.794 Dev Loss: 1.421..  Dev Accuracy: 0.689\n",
            "Epoch: 79/100..  Training Loss: 0.859..  Training Accuracy: 0.795 Dev Loss: 1.421..  Dev Accuracy: 0.688\n",
            "Epoch: 80/100..  Training Loss: 0.858..  Training Accuracy: 0.794 Dev Loss: 1.421..  Dev Accuracy: 0.686\n",
            "Epoch: 81/100..  Training Loss: 0.858..  Training Accuracy: 0.796 Dev Loss: 1.421..  Dev Accuracy: 0.682\n",
            "Epoch: 82/100..  Training Loss: 0.858..  Training Accuracy: 0.798 Dev Loss: 1.421..  Dev Accuracy: 0.679\n",
            "Epoch: 83/100..  Training Loss: 0.857..  Training Accuracy: 0.796 Dev Loss: 1.421..  Dev Accuracy: 0.676\n",
            "Epoch: 84/100..  Training Loss: 0.857..  Training Accuracy: 0.797 Dev Loss: 1.421..  Dev Accuracy: 0.674\n",
            "Epoch: 85/100..  Training Loss: 0.857..  Training Accuracy: 0.799 Dev Loss: 1.422..  Dev Accuracy: 0.673\n",
            "Epoch: 86/100..  Training Loss: 0.857..  Training Accuracy: 0.800 Dev Loss: 1.422..  Dev Accuracy: 0.674\n",
            "Epoch: 87/100..  Training Loss: 0.857..  Training Accuracy: 0.800 Dev Loss: 1.423..  Dev Accuracy: 0.673\n",
            "Epoch: 88/100..  Training Loss: 0.856..  Training Accuracy: 0.801 Dev Loss: 1.423..  Dev Accuracy: 0.673\n",
            "Epoch: 89/100..  Training Loss: 0.856..  Training Accuracy: 0.804 Dev Loss: 1.423..  Dev Accuracy: 0.673\n",
            "Epoch: 90/100..  Training Loss: 0.855..  Training Accuracy: 0.806 Dev Loss: 1.423..  Dev Accuracy: 0.677\n",
            "Epoch: 91/100..  Training Loss: 0.855..  Training Accuracy: 0.806 Dev Loss: 1.422..  Dev Accuracy: 0.677\n",
            "Epoch: 92/100..  Training Loss: 0.855..  Training Accuracy: 0.809 Dev Loss: 1.422..  Dev Accuracy: 0.677\n",
            "Epoch: 93/100..  Training Loss: 0.855..  Training Accuracy: 0.809 Dev Loss: 1.422..  Dev Accuracy: 0.675\n",
            "Epoch: 94/100..  Training Loss: 0.854..  Training Accuracy: 0.809 Dev Loss: 1.423..  Dev Accuracy: 0.673\n",
            "Epoch: 95/100..  Training Loss: 0.854..  Training Accuracy: 0.811 Dev Loss: 1.423..  Dev Accuracy: 0.671\n",
            "Epoch: 96/100..  Training Loss: 0.854..  Training Accuracy: 0.810 Dev Loss: 1.423..  Dev Accuracy: 0.670\n",
            "Epoch: 97/100..  Training Loss: 0.854..  Training Accuracy: 0.810 Dev Loss: 1.424..  Dev Accuracy: 0.667\n",
            "Epoch: 98/100..  Training Loss: 0.854..  Training Accuracy: 0.811 Dev Loss: 1.424..  Dev Accuracy: 0.668\n",
            "Epoch: 99/100..  Training Loss: 0.854..  Training Accuracy: 0.812 Dev Loss: 1.424..  Dev Accuracy: 0.670\n",
            "Epoch: 100/100..  Training Loss: 0.854..  Training Accuracy: 0.813 Dev Loss: 1.425..  Dev Accuracy: 0.671\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.title(\"Learning curve\")\n",
        "plt.plot(history[\"train_loss\"], label=\"loss\")\n",
        "plt.plot(history[\"dev_loss\"], label=\"val_loss\")\n",
        "# plt.plot( np.argmin(history[\"v_loss\"]), np.min(history.history[\"val_loss\"]), marker=\"x\", color=\"r\", label=\"best model\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kvdNvHyZAqdr",
        "outputId": "246244f0-2a98-4d25-d045-717e6f9c56dc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Learning curve\")\n",
        "plt.plot(history[\"train_acc\"], label=\"accuracy\")\n",
        "plt.plot(history[\"dev_acc\"], label=\"val_accuracy\")\n",
        "# plt.plot( np.argmin(history[\"v_loss\"]), np.min(history.history[\"val_loss\"]), marker=\"x\", color=\"r\", label=\"best model\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Acuuracy\")\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "orHBa6z-AtbG",
        "outputId": "17ec09ee-a544-48f7-d03b-3dfbaf4f7ae5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "y_pred_dev, y_pred_probs, y_true_dev = stanceDetector.get_predictions(x_test, y_test)\n",
        "#Printing the classifictaion report for the Development set\n",
        "print(classification_report(y_true_dev, y_pred_dev ,digits =4, target_names=CLASS_NAMES))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1pnQwCOj_uLc",
        "outputId": "d9cba1e7-015d-4afb-e430-0c609eab6bf5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     support     0.1071    0.0382    0.0563       157\n",
            "        deny     0.2523    0.2673    0.2596       101\n",
            "       query     0.1557    0.2043    0.1767        93\n",
            "     comment     0.8424    0.8801    0.8608      1476\n",
            "\n",
            "    accuracy                         0.7395      1827\n",
            "   macro avg     0.3394    0.3475    0.3384      1827\n",
            "weighted avg     0.7117    0.7395    0.7236      1827\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving the model"
      ],
      "metadata": {
        "id": "yeXZNqIn5Mj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Saving the model\n",
        "model_save_name = 'trainedTfidf.pt'\n",
        "path = F\"/content/{model_save_name}\" \n",
        "torch.save(model.state_dict(), path)"
      ],
      "metadata": {
        "id": "b0zort5e5MMT"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Loading the saved model, you can use any pretraied model <br>\n",
        "**You can go directly to this phase in case you have a pretrained model**"
      ],
      "metadata": {
        "id": "Y0lR9Kje5JA2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from StanceDetectorModel import StanceDetector,tfidf,Tfidf_Nn\n",
        "import torch\n",
        "model = Tfidf_Nn()\n",
        "model.load_state_dict(torch.load('trainedTfidf.pt'))\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bauYpb7xy5IQ",
        "outputId": "318d11b7-1dfb-4e17-8887-1ffee59e2eb9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tfidf_Nn(\n",
              "  (hidden): Linear(in_features=5343, out_features=128, bias=True)\n",
              "  (output): Linear(in_features=128, out_features=4, bias=True)\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (tanh): Tanh()\n",
              "  (softmax): Softmax(dim=1)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stanceDetector = StanceDetector(model,tfidf) # the class takes in as arguments : the model, and the vectorizer"
      ],
      "metadata": {
        "id": "2Oo8crIUESnj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The example txt file used is as in the image :\n",
        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ZRm7QJLOd/R1dXVWkDHRSiMzKLknv6lyeI2F+zh5qTMzQXbXYaJgcybLbw/qG21tbdYxS5QPzLzHz4WF3+Nk19/fH/PYsbkj3dLSkvBOsNfym8+vqqoa80rDeBNdr/nj3BKtOHDz0rf4bWfJmBUf8SaZY7nozKR0c/V56Y+TMYESdxB0LMxj4ol+U3t7u7USLdVFTzx+2qf9Qjhe/2GejHE/xTKWC9iRkZGUK4v89C3uixFzseInYGPajwmOGOkGGsZivORLdR8n06+avsVPnaVqZ4Z73mKOub2dRaNRlZSUqKOjI2XbM/13ujel4qmpqVFjY2PCdmxWrBUXF2vRokVWnkk/0rnhmYr7fDLzm7KysoSr/swxTzdQlWqsSzSuJGsj8fgdk5PNlST5Gg8TnSfuOotX//bP8jPup6rXbMhUezTHyvwGM5a4g1Re+LmOSbf88dphqjHMq0zOVRNJdB5lsg1l8zosEXf53XM9L9cUdmbBRap5g5/NDIM4T1ONFenM781CBPdxnT59ugoKChyvtT85uXfv3qR1He/az10W95zCvMeUsaysTHl5eQqFQlZw2M7ruZlorlFcXBwTBA+FQr5vkqbqfxLtP5Jq3gXAm+zlkM4Qd54pk5zfb+6g4uJi5efnO/I/Ge5gc7w8gPHMmDHDKmO2Nwvbv3+/6uvrVV9fr/3796u/v18LFiyIG/DLJPOolXncymxyaO5Smrva7jxjxljyJ6aS6jj5qbNEg42ZMLkv4oNgBnJ7/afLz3FKlCPS/hhjJBLRmjVr1NHRYeWOc9/B9lP+trY2K6eaKV+6ebh6e3tVW1tr5fE1kxIvj6ylw0/fkqqd+blocgf27IIMyKXDT5354b5YHutFl2mT7pXthslpu2bNGusmkl/xVmuORbLgkJkkZ/JiNFdjQLJVP9m6IZMo5+3ION3sLhqNqrS01PrvbNeZPbhnbNiwwcrP+vjjj0tKnp/V3X+PhTleeXl5jnzvxlVXXaUf/OAHjhzy41qrZiwAACAASURBVJUJ2EgfBhpKSkrinnNjSRWSjMmjuWDBAkUiEWsFcDaexPMr6PEwW2PYeGMWFtjHlN7eXrW0tDj2kfEi09cxueiPMzlXnWjjSTZ5uabwy577PNUCiXhPXmdavKfrEvHTnyW6xnEHaqUP80ab6zw7L9d+5vsaGxslnV8hbHIvG9FoVJWVlaqpqdGMGTP0+9//3jG/yNZNfPOUYGFhoV588UWFQiHrmKaaX2dyzuiedwFIbdwHpN22b9+uyspKa0LjlekgUq1kDofDKi8vdwRd43XckhyJ7js6OjK6eaCbuQDJz8+3LurGOlh7keo7zEXSoUOHAr2g83KcMlFn8VaUBeXEiRMZO8Zej5Opw2PHjjkCBibwZmce1bS/z9R/d3e37/Kbcpk8gyZ3md92dfjwYcdO8PbHTbPBS9+SKr1KpttZpoKb2eK1P05Hqs0NvTJPg9jT9EjOVVJ9fX3WOSClF1wea/47P7w+uulHrsaAZLJxwWPfKNjcgJA+3KRnPCouLvZ8zDNRZ4kuuDs7O63AlbmRHA6H4walvWxu6LUsq1evVn5+vrq6uhxjkGmzd9xxR8ziBpOSLFfjfiL2vsUEGswmvUExK1ztwbixbq4WhGyMh5kY9yeCUCiUcGGB33M0k9cxueyPMzFXnYjjSbaluqbwyzwpl+oJL7OZYbb7MPfTGOl+n70/MzcF413jmO8rLS1NuRDBz7Xf8uXLlZ+fr3//939XbW2tqqqq1Nra6hhHCwoKNGPGDL333nt64YUXtGDBAoVCIRUUFGS8P7av5K6pqdHQ0JCeeeYZLV682PrOZMy8xdxYHes+NH7mXQDOG/cpOxLxcofRzsuj4OZuqp88Ut3d3dq9e7dKSkrS3mDCCzOw2jdUsOdmczObIrg3K8o0LykfMv2ZXo+TlzpzBy8NrxthZFM6qTkS8XqcTD34TT3Q3d2tLVu2SPqwvOmWv7+/X+vXr9fg4GBav93keywtLdXdd9/teNzUj3iBQjP5s/PSt2SynSXL+ZfoMeJMydT5PtbUHMn42dzQpLhwMys6EwVbent7NTo6GnOepFP/pk7TeQTaLVH+vUSPyGfiOGRjDEilt7c37oa9idIEZYJpK/b8p+OZOeZmrhREnSVKTWTX1tamvr6+pGkl/GxuaAJmdiZQVFJSEvdpOvMkxeDgYEzOzXTG/Wg0GvOIrtkgMVPMOR2NRsecmiMd9nzI9s3L0kkLl6jPaGhoSJjSIZGxjode04e4P8tL3+l1DjFembpxb2xn9qXwm3fY73VMqrLluj9ONlf1ch2T6/In43VcT/eGeqrUFfGuKexM6olEKSfGsplhPF7nSqY9J9o0ORqNxh2HDb/9Waq0QIk2enfzeu3X2tpqLcYIh8PatWuXRkdHHRuOmoDzrFmzJEnPPvuszpw5o1mzZunMmTMZufkd73cXFhZq7ty56u3t1eHDh63vTMWM1ZlIZeOedwHwZsIFpE0+T78XT/v379epU6e0ZMmShB2zGYDtA4HZITvZJLmzs1M9PT2qqqpy7EabSX4DB2ayk2zgywQT/MvkbzcXW4kuSL0eJy91Zi+/fWKzdOlSFRUVpZUTNlPMBifp7oxu5/U4mTqzb/q4bdu2mNQora2tMZ9jv2D2U/6amhqFw2HHhbWXwEYyZsI2f/58DQ8Pp/Uongla2POZ3n///SooKHA8kuulb8l0OzOP5pmd0aXzE+Ha2lodOXIkawHpVOemV17qLF32GxL2zz5x4oRjYm6Czm6pgtH28t97771WuzUrqu2bXPkpr/umZkVFhb773e/6CjiZHLzuXcpNO3E/0mnfmDZd2RgDUolX/+Yx0mxtTGRy+NqD/eFwWHV1dRn/rkwwx9xsepftOrNvOm02qquoqFA4HHach14u2ExZ3TdW3Bfz5tF5dzmSBaONAwcOqKioSEuXLrX+Zvp3+0aBXpgN10zArbm52cpbnQmmb7dvEGzKn82FEHaZDIKboIt9HGlvb1dpaWla6S7GMh4m6hvt4n2WlzHM6xxivEoWrPMa6LLL5I3oXPTHfuaqqeZK43088TrXM9djfvL+m6CmPRjv5ZrCMMFm+2vczOaByebW5uaQl/HP61zJ3CBNdG6Yzfjc/cY3v/lNq5799Gdmc9hE1zhmA8JUubm9XPvZ92MwY6TZbDE/P98a/6LRqPLy8nTHHXeot7fX6u/vuOMOTZkyZcxPH9nnGvbfXVBQoIKCAkUiEes7586da5UpHvt83+sGu8m4510AvMleyo5Zy/S9tk/qCvvf5q7WY3Ml6W39LvxtbXsp9ceYTQ2NkZERrVu3znfAxTwKFA6HY3K+mXyG/f392rRpk9auXWu9ZnBwUD//+c+1ePHipJ+/YcMGazJhHkWNd7FkfovfnGOdnZ0KhUJxH5uLl48xEomosbEx5WYOmdDZ2aloNKoVK1Y48hmOJd3Eo48+qo6ODsfvNXXm9Th5rTNzwWrylknn21lHR0fOVkcbph25f4M9WOa1nXk5Tp2dnSorK3M8ktvV1aVQKOR4bKuzs9NaoWDEqzMv5Y9EIpo5c6Y2bdrkuKHgN0+8nbkQrKqqUk9PT0zgw2udmXZo2llPT4+VEsLw0rdImW1nkUhEDz30kNauXes4BmOpM6+SnZteea2zdJn+z+Q6lWS1bVP/IyMjeuSRRxwBKfvqNffYYy+bWRnV0dHheI27/uO1M1Nv9nMg3rlpzsv+/n5fAaANGzbEnHODg4OOx4KN7u5uFRcXq6WlxfrekZER36lUsjEGJJOo/tPN5elFvLrq6elRNBp1PGLttW9x74Fgzwfs9zw2eZft55L7mGe6zkweWftnuc/d/v5+bd26NSZPaqrvNBeTJshh2qKZZ9nPoUceeUT33Xef9V77ymR7Xys526Npk/Y262778XK8VlVVxRwnd9swn2MPKHhlxi53TnZ3m+ju7tbx48djxgD3azPZzsyxdPeL6XyW+1j29fVZY5pffsZDd97nRH1jqvr3OoZ5mUN4lclrCi+SrWI1Y6yftB1+r2OS8dofe+XlPPE7V002V8p0+bPBy1wv3vWYFFsn9r7YvReR5P2awnzn0aNHky5O87LRrLmZcPDgwZR14XWuZMbiRE9m2HNl2+urp6fHqg+v/Zm5uZsq3UhbW5sVeLWPh/YN+VJd+5WVlWnlypVWCiz7bzOpVOvq6tTa2mo9qWevA1Mv6aaycM/H3f1FNBpVVVWV4/PNd7qlaoteeZl3AfDmkgcffPADSfrFL36R67IgCZMz2X1xbwaZbF6IT1TUGXDxMrnvxnt+UwCxTE7L3bt3Z/0mG1Kz57x196mJ5lpjVV9fryNHjujtt9/OyOeZ/LzJbriZ3zkRNrqcaJiTX3xy0Y+bc3jfvn1Jv7O9vV2VlZUTdo7Y2tqqxsbGrOzFMp4wlwcufBMuZcfFyJ1w38489gUn6gy4uJlH2ufPn5/rogDwyaR8qq+vDyxPMhIzmyrGy7OZjU1EcWFhTo6g+NnMMBN5g3Mh2WaGADDRZC9lBzLGnstrzpw51uBp8ihJysomThMZdQZc3OyP5QOYeMaaugeZYzYvM5uvmjmVyW2a6vF4XNyYkyMoXlbZRyIRLVq0KIDSZIdJGQQAFwIC0hNEW1ubI9+eMTg4qNWrV0/IO7zZRp0BAACMjT2vqTuHNKkW4AVzcgAA4EYOaQAAAADjRqZzSAMAAGB8YYU0AAAAAAAXmK985Su65557cl0M4KJx+PBhrV27NtfFACYEAtIAAAAAAFxgfvrTn+qnP/1prosBAECMSbkuAAAAAAAAAADg4kBAGgAAAAAAAAAQCALSAAAAAAAAAIBAEJAGAAAAAAAAAASCgPQ4EA6HtW3bNlVUVGTsM5ubm9Xd3a2dO3eqpqYm7nfu3bvX+l9ra2vGvnu8qKmp0c6dO9Xe3p7rolxw2tvbE7YtxEedjQ+p+sbW1lZH3xgOh2NeU1FRoW3btlmv6e7uVnNzc9plGq/9MW32QxNhPEnVtrPBnC+ZnsMAuTCez/OJ0B+bsWw81l9QJsJxyjT3vCnd35+LMexiwbmZfab9xpvDM1fKnnA4PObrMOQWAemLVFtbm5qamrR582aNjo7mujgAMCaZugjs7OxUU1OTVq1apcHBwbiv6e/v17Jly9TU1KSurq4xfZ9EfwyM50AgLhy5aGfJghTARNfa2qqWlhb19PSoqalJTU1NWrRokSKRSK6LBuAiMTo6quPHj+e6GEjTpVn99FnL9L22T+oK6w9v63fhb2vbS97e3t7errq6OsffRkdHtWXLFnV3d2eypFnT3NysFStWKC8vT1Jw5e/u7h53dRQOh1VVVeX420Q7nkFrb29XbW0tdYQJoaamRmvXrlV+fn7Mvw0ODmr9+vXq7+/PQck+NB77RiATctG2Ozs71dnZGeh3Ahif2tracl0EZIgJNKe6TguFQhoZGdGePXvG/J0TfX7mtc5ygXMzt5grAYllLyA9a5m+13aj/hS+zwpAN97/mBa2fU/yEZQeGRnRQw89pEgkooqKCnV0dGjFihWSNK46+njsd403bNgg6fyj3vfff7+OHz9+0d09dg+Gra2tWrhwYY5KAyDTIpGIFi1aJOnD83u8TcoBAACQWCgU0muvvaYpU6YoFAolnccNDw/r5MmTAZZufPJTZwAyIxqNqrS0NNfFwBhkLyD90jZ9+z7nn/bt+Z0+1fZJ3XjrjdJLf/L9kf39/Tp48KBaWlpiOnoT/DX6+vocAdDm5mYtWbJEO3bsUENDg7VSN1ur9ioqKlRfX6/BwUFt377d8RsS3aWcN2+eNm3aZK2m7urqirmb5l41bg92S7Erssf6+8xNgJKSkoTfmWl+VpWnqo9kn22vX/fKzkTf6f6+XLczw32cEpXf/TvtN3zSMWPGDMfnxat/98p4d50lel067cz8vn379qmsrMw6VvF+p9dj7pXX8o+1zsyxPnr0qIqLi1VVVaWenh6dOHEi7sqMTB/zoLn7A3v5463I3rhxo/X/7a/NdN+YC17643A4rMLCQsdvM7999+7djjEl3lMrUuw56qXNepXsPDHlPHToUMznx3taJNW4b9rHkSNHrPPDiDe+emUfB+KdT1764/b2dlVXV+uxxx7T0qVLrdem0y69tG0/faOUuj9LVfeJ6sv9Wve/1dXVae/evQm/N1V/5ncM9vI7/PzWRMzvdLc701YkOcrn5Tu9zJX8tLNMjMGJPstL+Q3zO0yAK9kY4JXfduZ+j5fz3P057jpoaWnJSB+Uqj/2MgZ47RvtN5bt55O7Pty/Ndlv8zIncbd/92v89Gde58eZaGd2mZrrSYo5V+ONlZmc69XU1Ki0tNSq38rKSlVUVPieL3ltZ37mZ16Op9e5kiQdOHDA8d3pnpep6qy1tVWNjY3at2+fFi5cqNHRUUe/7O4jU13fXgjnpleprr2NVGOYn74xnevIeLyO5V7nLanGTa/XsH6+M9UxN+dSNBpVXV2d+vr6HOeVvc3lqj/G+DZhc0ifOHHC+v/t7e1auHChNm/erKamJq1bt06lpaUxm1Hl5eVp5cqVkmS9rqCgQPfff3/WyllQUKDp06enfF1JSYkWL16sLVu2qKmpST09PWpsbHTkQw2Hw6qurta6deusfKO1tbWOXHgmqXtTU5P6+vrGXP7Vq1dr165dVl6wrq4u1dXVZS3/Xk1NjRoaGrR69WrrO48dO6YVK1Y4ktWbTcVqa2ut497U1KR33303YVJ707mdOnVKq1atiglGDwwMWJ/T0dGhhoYGa+MB8332+jftzL1BgWln0Wg0kHZWU1OjH/zgBxoaGrLKf+jQoZg6a25u1vr163XkyBHrdQMDA1q7dm1aeXfz8/P11a9+Vfv27bPaRm1treM729vb1dvba33f5s2bVV5eHnNuhsNhlZaWWnXb1NSkadOmpb1BQUtLi4qLi61cwMPDw1q9erV1nFpbW7Vx40ZHXRw6dEgrV65MK8+j1/Jnss5mzZqlaDSqnp4eVVdX684779RPfvITjY6OKhQKScr8MQ+a/aLLlH/Hjh36x3/8R0kfrsg2febIyIjjGNjzGGa6b8yFTPbH7jZrcmH39PQ4Jste2qwXJndrYWGhVq1a5RjDTNs+fPiwTp06ZV3IGRUVFaqsrNSxY8esSavXcV86H3xqbGy0fmtPT48WLlyYVv9SV1enyspKrVq1yupbli9f7vidXvpj6XzdPvDAAzp69OiYxgo/bTtV3yh568+85FqXPryRYB+no9Go1c9u2LDB+u0jIyOOPKRNTU0xN9y99Gdex+BwOKyWlhZ1dXVZn9fb2+sYA/y0s2T27NmjkZERq2825syZo6KiIh09etS6APTynV7nSpK3dpbJMdjLGObu2007GhkZUUdHh3WeP/DAA1q5cqV2797tOObr16/3VTY/7UxKfZ5Lqftj994A9nbW1NSUVtArU/2x/Xem6hvd107x+g2v+yCYuZd9vv3QQw/pH/7hH6zXNDc3a/bs2da/m++LN29J1Z/5mR8nm2v4lYm5Xn9/v44ePaqioiLNmTPH8fmhUEijo6NWmoxMz/XMeyKRiHp7e1VQUOAoQ3t7u7WBYVVVlUpKSvT4449bf3PPSVK1M69jWHNzszZt2iRJ1jxi9erVmjt3rmMM8zpXqqqq0pIlS9TR0THmuUGqOpPOn0v19fX62c9+Jkn68pe/rKNHj6qnp0elpaXWZ3i53jefN1HPTa9SzSFM3aeaX/qVqvzxzrl4dex1ruRl3uJ13PTK63d6OealpaWaNm2aurq6VF5ersWLF+uJJ57Q4OCgNe/JVX+M8S/YgHTF1bpC0sk3/K+Ols435MbGRo2MjDjuxFVXV+vQoUPWiRiJRLRv3z6Vl5fHDCr2O1ORSERHjhxxDAKZYlZz5+fna+PGjSk7RPcdot7eXuXl5Vnlam5uVnl5ufbt2+cIsBw6dEjV1dVZCy6tWbPG0cHt379fg4ODMQGDTIlEImpra3PcmTtw4IAkOS7k5s+fr6Kiopi7ag8//HDcDrmmpkZLlizRqVOnYu78zZw5U3l5eert7U1YDvN9O3bssOrftLOSkhLNnz/f8X19fX3WBU4kEtHAwIAKCwuzUmcLFiyQJG3dutX62/bt23Xq1Ck1NDRYf2toaNCpU6ccK/bNe8xn+GW/6xmJRBzBUOn8RaD9oqu7u1vHjh1znHMVFRUqLCzUwMCA486nu+35MTg4qEcffVTSh5N6c3PIPL1gP0bS+TobHBxUfX29r+Pkt/yZqDNJmjJlinUxkp+fr9/85jd67bXXJEnFxcWSsnPMg2R+h/3c7O7u1po1a3JVpJzKVH9sVvIcOXLEarPJPitVm/XCtLdNmzZZ/aoZw8xYnegC3ATtTDvwO+4PDg5qzZo11m81n2Palx/21SOmvPZz02t/bHR1dQU2VpjyJ+obpcz3x8XFxTp16pQOHz5s/c3dx3nlpz9LNQabOZV7tZg9z6PfdpaMKYO7H3cHlrx+p9e5kpGsnWX6mHsZw9y/2z5nNq+5/vrrNXv2bPX09Dg+b+vWrRodHY17PmVKqvNcCn5+bGSiPza89o32G5Xx+g0v7HMv+03PSCSi73znO9Z/u8f4eG3DXv5k/ZnX/jgbc41MzPVMeezvMzdo7edrpud6oVDI+nyzUZi77CZQ1NfXp8HBQSsQGO8GT6bGYPM77ddx/f39+s53vuPoC72em6Ojo47rurHMDVLVmXQ+AHj06FFrrn7mzBnrmOXl5WnmzJm+r/cn6rnplZc5hJf5pV+pyh/vnBurVPMWL+Nmpr/T6zHPy8vTM888Y/3/l156Sc8++6wkWZ+Xy/4Y41uAAelG3X/39ZJe0R+f9P4uE9Ddu3evdefOvuqtpqYmJpgonX9sQIodVNyvO3HihDUIZFpnZ6d196qqqkp79+7Vzp0743Ya7s7WzXRC7kcVsln+ePr7+zU0NBTIdxnHjx+PueMYCoVS1pkxbdo0rV27VsPDw3EfATOf39LSknB1rKl/9w6ukUhEIyMjKisrc/zd3c6yJd7EVPrwOJlBwASg7KuwJOnkyZMaHh5Oa/JlvzHkhzk33WWtqqpK+062m/t32pngVrxypDNh8lP+TNWZJMcxHx0djXlNNo550MxvirfqD+n3x+YmXDxDQ0OO9pJum7Uz/VS8PJPuC8A9e/bEXLi7xz+/4777N5nVWOkERZP1LV77YyPeedvW1qZly5ZlLZVMsvLby5qp/jgajaqkpEQdHR1jCtL57c9SjcGJ5lTu7/TTzlLp7e11XMTFay9j+c54cyUpdTvLxhjs5v7+RL/DXtbKykrl5+c7noiUgrlxk+o8iSeI+XEm+mM7r32j+xhs2LDBcS3mhfvGoh/RaDRu285Uf5zpuUam5nomSG0PpMa7QZvJuZ75PPP55nwby42WTIzBiX6nF4nOTa/XkV7LlqrORkdHHe3fXS+S/+v9iXhu+v38ZHMIP/NLP5KVfyxtMZlU9e9l3Mz0d8YT75i7zyV3uzRpbHLRH2P8y14OaYdG3f/YQl2vt/W78KPa5+OdI/8vZ8zJkyfV0dFh3ekydz7LysqsRw7MYyvjSX9/v5YtWybpwxw8Gzdu9J2jqri42ArOuyV7BGesEuUqSvbYyVi5c0W5mdU88QbyeGbPni3pfAAv3usjkYjWrFmjjo4OK8dfvJxGyTbtyFVwb/r06SooKFBJSYkjH6JhjpMJQLnzJhrZuohKlF9rZGTE8d9tbW3WcTfly3aucvdgaZgJn5/JXCbL77XOUsnVMc+k7u5uHT9+XGvXrrX6+JGLOI9Ypvrj7u5uNTQ0WKttIpGI5s+fr5KSEh08eDCTRXZI1mebm3rxLuQqKysdq7nH67jvtT8e7zLZn23YsMGa+zz++OOS0svBnOn+rLi4OOVGXJluZ/v371d9fb11s2XOnDkqKCjQrl270vrOVHMlP4Iew0x+yQULFlibltfX18dc1J49ezbhhba5eZyrfQByMT+eyMw82UvgJNH+Bn746Y9zMdfwOtfr7e3VwoULNWfOHPX391sLcvbv3y8p832juSlmP069vb1qaWnR/Pnz0957YazM70w0d7cL+tzMZJ3l4no/6HPTD69zCC/zy4nO67iZaZk45tOmTctqf8zGqhNbAAHpG7Xsewt1vaRX/u+3te2l9D6lv79fu3bt0ooVK9TQ0GAFCk+cODHmzciC0tnZqUgkorVr16q+vl779+/3PJE2O4gGGYgxA/qxY8ccHb/ZFCAb4m38YyZthvtuWqo6tG/4Fg6H414I228cmN+9YsUKSfLUrtK9MzlWZgXEwMBA0gv86dOna3R0NO5mYdlijtvw8LDWrFnjyI9ZXV0d83pTLrPhgcn3FlR5jXgr4b3IRPn91lkyZrVckMc8G0yeaOnDm3pr16696ILSmeyPzU0990XPWDb6Gyv7Rab9Alw6H3iyr+IYr+O+1/54Ishkf2xPg2EubBKNxYlkuj8zc6pkAc1MtzPzFI65ERQKhTQ8PByzqsjLd3qZK/kV5BhWXFwcE0Tzu6FnLi9AczE/nui8PlkQDodVXl6uzZs3W+eAqW8//PbHQc41/Mz1IpGIGhsbFQqFdPjwYVVWVjpWZma6bwyFQglviqWbGiYTzO9MFVzMxbmZyTrLxfV+0OemX2OdQ3i5iTERZGLc9CtTx/yvf/3ruO2PkXtZTtlxo5Z9b7U+eYX0yv+9T4/6SNURj3l0yZ4PKN1HJ41EqR9aW1u1d+/emA3rciXo1BzSh4OoyUuYaWVlZY7gn3nsZnBw0Lrzn0g0Go270UcinZ2d6unpUVVVVcoNwLq7u7VlyxZJzjxG8b4v0eO1QUn0KLhbLtI01NTUKD8/XwcPHvQ1UPb392v9+vUaHBzMSnnNxmnuSWKixzv9Gkv5062zeNI55uFwOO6GNONFZ2enurq6Ej62GHQfGaSx9MfmYskwOfHtm8Sku8mWF4lysJqyuR9P3b9/v3WOxgvajXXczxav/fFEkun+uK2tTX19fTF1lKq/yvQYduLEiZR5F7PRzkzux3nz5sUElrx+p5+5UjqyPYbZ85Xa+x93qpqjR4/qzJkzMWP1WB6XzlQ78tMfew2kZZN7DMgFUw/JAnTm2No3sE3XWPrjZHONTPAz1zP7HVVWVmrevHkxN2gz2Tea+ndv+mlyRWdjzyWvzO9MlTok29eubpmus1zMZYM+N8fCPYfwO790S6dvTHTONTQ0ZK2f9TpuZlImj/mJEyey1h9v2LAhq/WA7MtiQDqzwWjDDDAm+bkJUqezK25ra6uqqqriTghMp+wn6GlXU1OjcDgcc9ItWLAgrYCTuUBfsmRJYJMBMyiauqioqNC2bdvSemyjoqJC3/3udx07X1dXVzuCf2byaK9zs4ut+5E2k2fUXR/f/OY3E7aDDRs2qK+vT3V1dY5c0a2trTEBOPObzcWhqf97773X8RsaGxtzPjgfOHBARUVFjl1x3cwmBF4C8plickzZJzjhcDjmEeN450qiPM+ZYK8LeztYunSpioqKfE9iM1l+r3Xmhd9jbiYekrK+KZNX7e3tMXndE+VzN3WdzY2ucslrf+y+WRfvsf5cBHRNIG758uXW35qbm1VbW+tIxyF9GMCeNWuWZs2aFTNejmXczzYv/fF4lsn+rKKiQuFw2DFGm37G/XitGf8TbUCU6THMbHLlbkOtra1Wn5ONdmZS0nzmM59RQUGBdV4YXr7Tz1zJi6DHMK+BwldeeUUvvviiamtrHXVh+hB33XmRqp155Wd+bIIX2dyA3M7LGJALJrDqPodramr03e9+V9KHdWUPLrW2tmrlypVpBXq89sd+5hqZ4Heu19vbq4KCAt1xxx0aGBhwXHNksm9MtsjGnQM/aOZ3lpSUOI6n+/oyk9euXmS6znJxvZ+Lc9MLr3MIr/PLTPWNZo5qH0fa29tVj004JAAACihJREFUWlqatbQquVjwMBH6Y3N+m7z0mJiyl7LjroX65BXn/+/1dz+mx+62/+Pb+l04vfQdJvelPZe0yX3nflQm3mMMJkewdP7RfPsjCHa9vb2qqqpKOy9PJBLRzJkztWnTJsdJOzIyonXr1vlegWlSSoTD4Zi8UvZcSvE6V5NzyeQDjJe3zNSLPUdPZ2enysrKHI+GdHV1KRQK+X7sqb+/X88//7yjPuLlgHr00UfV0dHhyBn0s5/9TF/84hcdr7PnfbbXR09PT9LgsPn8lpYWlZWVWTv1tre3O3IajYyMqKOjwxEsX79+vTo6Oqz6tNdpNsV7DMz+SK8915I7L5P9cd7Ozk5Fo1GtWLHC8bpsPfre3d2t4uJitbS0OHJSRqNRxyOJic6VbKYPMJ9r7w/cx9yrTJbfa5155eeYm0BJVVVV2ht1xOtbTNtN57GyDRs2WKu2DbNbu/tz4tWdvT/z0jdKsbnS7PnOzDGNl58w3d+Z6f7Y1Jm9PI888ojuu+8+R12FQiFH+7fXbzYe/4tEInrooYdi+qlE50lvb69qa2sTrm7xM+4HyWt/nEle27YXXvszL+dJf3+/tm7dGtO+E5XJPf67X5vJMcw+p7J/n3teko12ZuaXiZ7G8fKdXudKXuRiDDPtwj6five9jzzyiL7+9a876iLRGOBVqnbmhZ/5cX9/vzZt2qS1a9cGkiLJyxiQSV7HMFM28/i1e5yW4tfV4OCgfv7zn2vx4sW+y+a1P/Yz18gEv3O9w4cP695779WMGTPU1dUV8++Z6huTbRhvTx2SaV7HsHi/0/xGc5wyee3qhdc68/okrdfrfS/G87nphdc5hNf5ZSb7Rvdn9fX1WWWw8zJX8srruJkpF0t/jNy75MEHH/xAkn7xi1/kuixZZQIIu3fvzlmOTAAAci1e/lkpce5FAJljLvpzmbM9l+z5c91BfZOvcsuWLTp58qSOHDmit99+O4elBQAgt7yOm+NpXxXAqyznkAYAAONJcXGxRkZGYlZnmjyCALInFAplLf/zRDBz5kzl5eXFfRInVxtEAwAwXjFu4kJGQBoAgItINBqNm9dw+fLlys/Pz9kmrcCFrr29XeXl5dq1a9dF++ipufHl3qvA5PxMN1UeAAAXIsZNXMhI2QEAwEUmXs5Gd05BAJlh8khma8+GiSZeblPJmRu0vr6elB0AAMjbuAlMRBdNQBoAAADA+EdAGgAA4MJGyg4AAAAAAAAAQCAISAMAAAAAAAAAAkFAGgAAAAAAAAAQCALSAAAAAAAAAIBAEJAGAAAAAAAAAASCgDQAAAAAAAAAIBAEpAEAAAAAAAAAgSAgDQAAAAAAAAAIBAFpAAAAAAAAAEAgCEgDAAAAAAAAAAJBQBoAAAAAAAAAEAgC0gAAAAAAAACAQBCQBgAAAAAAAAAEgoA0AAAAAAAAACAQBKQBAAAAAAAAAIEgIA0AAAAAAAAACAQBaQAAAAAAAABAIAhIAwAAAAAAAAACQUAaAAAAAAAAABAIAtIAAAAAAAAAgEAQkAYAAAAAAAAABIKANAAAAAAAAAAgEASkAQAAAAAAAACBICANAAAAAAAAAAgEAWkAAAAAAAAAQCAISAMAAAAAAAAAAkFAGgAAAAAAAAAQCALSAAAAAAAAAIBAEJAGAAAAAAAAAASCgDQAAAAAAAAAIBAEpAEAAAAAAAAAgSAgDQAAAAAAAAAIBAFpAAAAAAAAAEAgCEgDAAAAAAAAAAJBQBoAAAAAAAAAEAgC0gAAAAAAAACAQBCQBgAAAAAAAAAEgoA0AAAAAAAAACAQBKQBAAAAAAAAAIEgIA0AAAAAAAAACAQBaQAAAAAAAABAIAhIAwAAAAAAAAACQUAaAAAAAAAAABAIAtIAAAAAAAAAgEAQkAYAAAAAAAAABIKANAAAAAAAAAAgEASkAQAAAAAAAACBICANAAAAAAAAAAgEAWkAAAAAAAAAQCAISAMAAAAAAAAAAkFAGgAAAAAAAAAQCALSAAAAAAAAAIBAEJAGAAAAAAAAAASCgDQAAAAAAAAAIBAEpAEAAAAAAAAAgSAgDQAAAAAAAAAIBAFpAAAAAAAAAEAgCEgDAAAAAAAAAAJBQBoAAAAAAAAAEAgC0gAAAAAAAACAQBCQBgAAAAAAAAAEgoA0AAAAAAAAACAQBKQBAAAAAAAAAIEgIA0AAAAAAAAACAQBaQAAAAAAAABAIAhIAwAAAAAAAAACQUAaAAAAAAAAABAIAtIAAAAAAAAAgEAQkAYAAAAAAAAABIKANAAAAAAAAAAgEASkAQAAAAAAAACBICANAAAAAAAAAAgEAWkAAAAAAAAAQCAISAMAAAAAAAAAAkFAGgAAAAAAAAAQCALSAAAAAAAAAIBAEJAGAAAAAAAAAASCgDQAAAAAAAAAIBAEpAEAAAAAAAAAgSAgDQAAAAAAAAAIBAFpAAAAAAAAAEAgCEgDAAAAAAAAAAJBQBoAAAAAAAAAEAgC0gAAAAAAAACAQBCQBgAAAAAAAAAEgoA0AAAAAAAAACAQBKQBAAAAAAAAAIEgIA0AAAAAAAAACAQBaQAAAAAAAABAIAhIAwAAAAAAAAACQUAaAAAAAAAAABAIAtIAAAAAAAAAgEAQkAYAAAAAAAAABIKANAAAAAAAAAAgEASkAQAAAAAAAACBICANAAAAAAAAAAgEAWkAAAAAAAAAQCAISAMAAAAAAAAAAkFAGgAAAAAAAAAQCALSAAAAAAAAAIBAEJAGAAAAAAAAAASCgDQAAAAAAAAAIBAEpAEAAAAAAAAAgSAgDQAAAAAAAAAIBAFpAAAAAAAAAEAgCEgDAAAAAAAAAAJBQBoAAAAAAAAAEAgC0gAAAAAAAACAQBCQBgAAAAAAAAAEgoA0AAAAAAAAACAQBKQBAAAAAAAAAIEgIA0AAAAAAAAACAQBaQAAAAAAAABAIAhIAwAAAAAAAAACQUAaAAAAAAAAABAIAtIAAAAAAAAAgEAQkAYAAAAAAAAABIKANAAAAAAAAAAgEASkAQAAAAAAAACBICANAAAA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      ],
      "metadata": {
        "id": "1m_93DuQEwzP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions,label_predictions = stanceDetector.predict_new(model,'example.txt')\n",
        "predictions,label_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSAjy70P_XZ6",
        "outputId": "3338b1fc-f274-4fe6-b51d-d75659bc4c4a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([0, 3]), ['support', 'comment'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    }
  ]
}