Stance_detection_RoBERTa_baseline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "L0lN3vS45Jv1",
      "metadata": {
        "id": "L0lN3vS45Jv1"
      },
      "source": [
        "## Stance detection"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xWh5Ype45Jv7",
      "metadata": {
        "id": "xWh5Ype45Jv7"
      },
      "source": [
        "Here is a simple baseline for stance detection using RoBERTa model from [Simple Transformers](https://simpletransformers.ai/) library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "nJUFJb-S5nEC",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJUFJb-S5nEC",
        "outputId": "8ddef86d-af8b-49b8-f7de-bbe232199e3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "%pip install simpletransformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "dgVuIvT75Jv8",
      "metadata": {
        "id": "dgVuIvT75Jv8"
      },
      "outputs": [],
      "source": [
        "#import libs\n",
        "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
        "import pandas as pd\n",
        "import logging\n",
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "WHKZ7_DU5Jv_",
      "metadata": {
        "id": "WHKZ7_DU5Jv_"
      },
      "outputs": [],
      "source": [
        "#logging \n",
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "s7rUhb1f5JwC",
      "metadata": {
        "id": "s7rUhb1f5JwC"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "YfQVKaea5JwD",
      "metadata": {
        "id": "YfQVKaea5JwD"
      },
      "outputs": [],
      "source": [
        "#read train dataset\n",
        "train = pd.read_csv('data/train.csv', sep=',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "7c991500",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7c991500",
        "outputId": "ea717f45-3b52-4406-961c-629668188a03"
      },
      "outputs": [
        {
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              "          document.querySelector('#df-662cdbee-23c6-44ac-b8f6-dc4ad21a0ad5 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-662cdbee-23c6-44ac-b8f6-dc4ad21a0ad5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              id                                              Tweet  \\\n",
              "0        3054935  @realDonaldTrump It's too dangerous to hold th...   \n",
              "1         453444  If masks are so important why is the public no...   \n",
              "2         211600  But if schools close who is gonna occupy those...   \n",
              "3        3049718  @GavinNewsom be the Governor I thought you wer...   \n",
              "4  5039679580998  @BBCNews 1. You are reporting 78% uptake on ma...   \n",
              "\n",
              "                 Claim   Stance  Premise  \n",
              "0      school closures    FAVOR        1  \n",
              "1           face masks  AGAINST        1  \n",
              "2      school closures    FAVOR        1  \n",
              "3  stay at home orders    FAVOR        1  \n",
              "4           face masks    FAVOR        1  "
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "gnWVdxSf5JwF",
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "gnWVdxSf5JwF",
        "outputId": "318a2f54-20f2-4a6e-d741-97ccde86dbb4"
      },
      "outputs": [
        {
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              "      <td>@Leo_girl001 The D-Day is finally here. ...  Y...</td>\n",
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              "      <td>825397</td>\n",
              "      <td>@FOX10Phoenix the boys &amp;amp; girls clubs are O...</td>\n",
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              "<p>2756 rows × 5 columns</p>\n",
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              "                 id                                               text  \\\n",
              "0           3054935  @realDonaldTrump It's too dangerous to hold th...   \n",
              "1            453444  If masks are so important why is the public no...   \n",
              "2            211600  But if schools close who is gonna occupy those...   \n",
              "3           3049718  @GavinNewsom be the Governor I thought you wer...   \n",
              "4     5039679580998  @BBCNews 1. You are reporting 78% uptake on ma...   \n",
              "...             ...                                                ...   \n",
              "2751        2747057  @JohnCornyn Schools won't be safe until the vi...   \n",
              "2752     2120406711  @MorrisseyHelena @hughosmond @allisonpearson S...   \n",
              "2753        3006200  My family is living in fear because my brother...   \n",
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              "2755         825397  @FOX10Phoenix the boys &amp; girls clubs are O...   \n",
              "\n",
              "                    Claim labels  Premise  \n",
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              "3     stay at home orders      1        1  \n",
              "4              face masks      1        1  \n",
              "...                   ...    ...      ...  \n",
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              "2754  stay at home orders      2        0  \n",
              "2755      school closures      0        1  \n",
              "\n",
              "[2756 rows x 5 columns]"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#rename columns to match SimpleTransformers format\n",
        "column_names = {'Tweet': 'text', 'Stance':'labels'}\n",
        "train = train.rename(columns = column_names)\n",
        "\n",
        "#change labels to match SimpleTransformers format\n",
        "train.loc[train[\"labels\"] == \"AGAINST\", \"labels\"] = 0\n",
        "train.loc[train[\"labels\"] == \"FAVOR\", \"labels\"] = 1\n",
        "train.loc[train[\"labels\"] == \"NONE\", \"labels\"] = 2\n",
        "\n",
        "train"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4725BiMb5JwG",
      "metadata": {
        "id": "4725BiMb5JwG"
      },
      "source": [
        "Let's train our model!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "yYB3XHAw5JwI",
      "metadata": {
        "colab": {
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        },
        "id": "yYB3XHAw5JwI",
        "outputId": "ec1d21a6-1348-4315-a417-ac7579d567c4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.bias', 'roberta.pooler.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.layer_norm.bias']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a95e77e08d14c9e8f3435f5891e5159",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2756 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "806cad2c73344971951a335c6ca682c8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86c404b514e6466bb126ffafdddfb652",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/345 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 44.8 s, sys: 7.46 s, total: 52.3 s\n",
            "Wall time: 1min 6s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(345, 0.9275238202965778)"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "model = ClassificationModel(\n",
        "    \"roberta\", \"roberta-base\", num_labels=3, use_cuda=True, args={\"overwrite_output_dir\": True}\n",
        ")\n",
        "model.train_model(train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "N1mamPoq5JwK",
      "metadata": {
        "id": "N1mamPoq5JwK"
      },
      "source": [
        "### Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "54vFfAsr5JwM",
      "metadata": {
        "id": "54vFfAsr5JwM"
      },
      "outputs": [],
      "source": [
        "#read validation dataset\n",
        "val = pd.read_csv('data/validation.csv', sep=',')\n",
        "\n",
        "#rename columns and values to match the format\n",
        "val = val.rename(columns=column_names)\n",
        "val.loc[val[\"labels\"] == \"AGAINST\", \"labels\"] = 0\n",
        "val.loc[val[\"labels\"] == \"FAVOR\", \"labels\"] = 1\n",
        "val.loc[val[\"labels\"] == \"NONE\", \"labels\"] = 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "E9uw5vJc5JwO",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
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        "id": "E9uw5vJc5JwO",
        "outputId": "6744abbf-2bea-4895-c59c-936972a1fe2a"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ac8f8e96dbd4078a5ca356ad25d4e54",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "  0%|          | 0/600 [00:00<?, ?it/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11d4182a04e846029f45df58b735d6a9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Running Evaluation:   0%|          | 0/75 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'mcc': 0.4289560035198193, 'eval_loss': 0.8451575215657552}"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "result, model_outputs, wrong_predictions = model.eval_model(val)\n",
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "id": "frX89usd5JwP",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "frX89usd5JwP",
        "outputId": "168b59d4-a205-4c8c-f163-a307fe6425c6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a72a56a9e8a940b9abd0f069b6a701cf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/599 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17a2f3db9be84e7292369371a30997ef",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "  0%|          | 0/75 [00:00<?, ?it/s]"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "#Make predictions on validation tweets\n",
        "val = val.dropna()\n",
        "tweets_val = list(val['text'])\n",
        "stance_predictions = model.predict(tweets_val)[0]\n",
        "val['predictions'] = stance_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "y0GeDzXt93mv",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "y0GeDzXt93mv",
        "outputId": "4ae95117-7e93-4e4d-f4e2-774c013839e6"
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            "text/plain": [
              "                id                                               text  \\\n",
              "0    7212131785749  Ordered a mask that had a cute chain attached ...   \n",
              "1       2367433035  Who is ready for some #baseball?  #BaseballisB...   \n",
              "2           496011  @Mystere07623203 @va_shiva @Liberty13046 130,0...   \n",
              "3          1490686  When they ask me what I did with my life I wil...   \n",
              "4          1209366  Taylor not putting her album in physical store...   \n",
              "..             ...                                                ...   \n",
              "595        2949472  @BrianKempGA is one of the few #Governors that...   \n",
              "596         208269  I tested positive for Covid. Got it from a fri...   \n",
              "597        2316008  If you are under retirement age, you have a *m...   \n",
              "598         659269  @TheLalasventure @doqholliday @realDonaldTrump...   \n",
              "599        1940282  @Caissie Obviously I was simply practicing my ...   \n",
              "\n",
              "                   Claim labels  Premise  predictions  \n",
              "0             face masks      1        0            1  \n",
              "1    stay at home orders      2        0            2  \n",
              "2             face masks      1        1            2  \n",
              "3             face masks      1        0            1  \n",
              "4    stay at home orders      1        0            2  \n",
              "..                   ...    ...      ...          ...  \n",
              "595           face masks      0        1            1  \n",
              "596           face masks      1        1            1  \n",
              "597  stay at home orders      0        1            1  \n",
              "598           face masks      1        0            1  \n",
              "599           face masks      1        0            1  \n",
              "\n",
              "[599 rows x 6 columns]"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "id": "rv1zjMkd8qsY",
      "metadata": {
        "id": "rv1zjMkd8qsY"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "1A-nqv4V7DaQ",
      "metadata": {
        "id": "1A-nqv4V7DaQ"
      },
      "outputs": [],
      "source": [
        "y_true = list(val['labels'].to_numpy())\n",
        "y_pred = list(val['predictions'].to_numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "yCn6wRbN5JwQ",
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        },
        "id": "yCn6wRbN5JwQ",
        "outputId": "b350ada9-8dec-4f33-cd8c-0204fbacdc50"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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D+127dWXevAUpRlR+euzenZt//RMAZv1nDuOe/RcAXXbqzEEH7EvH7TsAcNjB/Zn26ht8vJ//j6dQvfr1pu9R/dn/iANp2aolbdq15cLh32eXj3Rj5uT8dZp/jnmG791xRcqRll4hq0WaaDTwZeCa5M8Ha/WfJ+ke4GPA0s3Nb0NhUyXb1feZjUmaUtchPpjXKQsTJk5mr732ZI89ujN37gJOOeVEzviSV5YU07uLl7BDx+3J5XL84Y57OOWkIQAcOuAgbrv7z6xavZqWLVoycfK/OePUk1OONlvu+/nd3PfzuwHo/fGPcuzXT+D6qp/xmwm30mXPrix8cz77fuIA5s3YbAFYFopZKSZTyoOAzpLmAFeST9gjJZ0FzAZOST7+EDAEmAGsBOqclVivkIr78Yg4sr6+jXQBjgEWbzwc8Gx958ySmpoavn3BFTz0tz9RWVHB7Xfcy7Rpr6UdVmZddOU1THhxCkuWLOPIk77IN846g5WrVnHP/WMAOOrwQzj5uKMB6NB+O7502mc47axvI4nDDu7P4YcMSDP8spCryXHbZTdx/k0XERGsWLqcWy+6Me2wSq7Iq0qG1nHov/JmspqkQdWeoo4ZeUmtgbbAWPK/OdZ/V+2BhyOid52DSrcCt0XEf13ElPSniPhCfYFlZaoky1bNezrtEMre1/pdlHYIW4U7Zo1qctZ9ZufPFZxzDl3w51Rvs9xcxX02cAGwC/ACHyTuZcBmF3RGxFmbOVZv0jYza24Zesj7Zvfjvh64XtL5EfHbZozJzKzZBeW1V0lO0vbr30jqKOkbJYzJzKzZVYcKbmkrJHF/PSKWrH8TEYuBr5cuJDOz5heo4Ja2QrZ1rZSk5MonkiqBbUoblplZ8yqLOe5aHgbulfSH5P3ZwN9LF5KZWfPbEirpQhWSuC8hv9Xg+n02pwDlv8ejmW1VyqrijoicpH8CHyF/p09nYNTmv8rMLFtqyqHiltQLGJq0d4B7ASLiiOYJzcys+WToyWWbrbhfAZ4Gjo+IGQCS/MgyMytLuQxV3JtbDvgZYD4wVtLNko6EDH1nZmYNEA1oaaszcUfEAxFxGtCb/H4lFwA7SbpJ0tHNFaCZWXPINaClrZCHBa+IiD9FxKfJb/D9Ik3bj9vMbIuTkwpuaStkOeAGyV2TTXpkj5nZlqgm7QAaoEGJ28ysXJXLqhIzs61GllaVOHGbmbFlrBYplBO3mRnFmyqRtDfJDYuJHsD/AtuT31n17aT/+xHxUGPO4cRtZkbxlvlFxKtAH9iwm+pc4C/kHwL8q4i4tqnncOI2MwNqSjPFfSTwRkTMVhGXERbyIAUzs7JXohtwTgNG1Hp/nqQpkoZL6tjYWJ24zcxoWOKWVCVpYq1WtfF4krYBTgDuS7puIr/Lah/y24lc19hYPVViZgY05FGSEVHIjYjHApMiYmHyNQvXH5B0MzCm4VHmueI2M6MkUyVDqTVNIqlrrWMnA1MbG6srbjMzinvLu6RtgU+Rf9Tjej+X1If8kvFZGx1rECduMzOKe8t7RKwAdtio74xije/EbWbGlrFda6GcuM3McOI2M8sc71ViZpYx3tbVzCxj/CCFIsjQL7/MGnTA19IOoew9duPgtEOwAuUyNFmyxSZuM7Pm5IuTZmYZk51624nbzAxwxW1mljnVyk7N7cRtZoanSszMMsdTJWZmGePlgGZmGZOdtO3EbWYGeKrEzCxzajJUcztxm5nhitvMLHPCFbeZWba44jYzy5hiLgeUNAt4n/xusdUR0U9SJ+BeYA/yDws+JSIWN2b8iuKEaWaWbdGAVqAjIqJPRPRL3l8KPB4RPYHHk/eN4sRtZgZUEwW3RjoRuCN5fQdwUmMHcuI2MyN/cbLQfyRVSZpYq1X913DwiKQXah3rEhHzk9cLgC6NjdVz3GZmNOziZEQMA4Zt5iOfiIi5knYCHpX0ykZfH1LjtyN0xW1mRsMq7nrHipib/LkI+AswAFgoqStA8ueixsbqxG1mRr7iLrRtjqRtJW23/jVwNDAVGA18OfnYl4EHGxurp0rMzICaKNpywC7AXyRBPsf+KSIeljQBGCnpLGA2cEpjT+DEbWZG8dZxR8RM4IBN9L8LHFmMczhxm5nhW97NzDLHt7ybmWWMn4BjZpYxnioxM8uYIq4qKTknbjMzPFViZpY5vjhpZpYxnuM2M8sYT5VsZW4edh1DhhzForffoW/fotwYZcA2rVryu1HX07JVS1pUVjL2b09x63V30LX7zlx14w/o0LE9r/77Na7+1k+pXleddriZceXIcYyb/had2rVm1Hc/u6F/xDMvc++z06moEIf17s6Fxw0A4LX57/GjUeNZvmYdFRJ3n38CrVqWX+oIX5zcutxx50huvPE2ht92fdqhlJW1a9bxrVO+w6qVq6lsUclNf/kNz4/9F6dWfZ57b/4zj48ey0XXXMDxQ4fwwJ2j0w43M07o15PTDtmHK+59akPfhBnzePLl/zDywpPZpkUl7y1fBUB1TY7LRzzJj047nL132YElK1bTorI896aryVDFXZ7/BprZ+PH/5L3FS9IOoyytWrkagBYtWtCiZQsigoMO7cuTf8snnYfue4SBxxyaZoiZc1CPrrRv2+pDfSOff4WvHrE/27SoBKBTuzYAPPfaXHp27cTeu+wAwPbbtqayojzTRo4ouKXNFbdt0SoqKhj+8O/ptkc37r/9AebOmsfypcupqcmvAXh7/tvsuHPnlKPMvtlvL2XSmwu54eEXaNWikguPH8C+3Xdk9jtLkeDcWx5m8fLVHNOnB18dtH/a4ZZElqZKSvarU1JvSUdKardR/+BSndPKTy6X4ytHV3Fyv1PYp29vdt9rt7RDKks1uRzLVq7hrvM+zQXHDeDiPz5BRFCTC158cyE/GTqI275xPGOnzuKfr89LO9ySyFLFXZLELelb5DcJPx+YKunEWod/spmv2/Act1xuRSlCs4xavmwFk56ZzL4HfZR2HdpRmcyz7th1R95e8E7K0WVflw7bcuS+uyOJ/XbbkQqJxStW06VDWw7ssTMdt21Nm21a8Ine3Zk+tzx/3sV8Ak6plari/jpwUEScBAwCfiDp28kx1fVFETEsIvpFRL+Kim1LFJplxfadOtCuff7vwTatt6H/wIOYNWM2k56dzKDjDgdgyOeP5ulHnkkzzLJwxEd3Z8Ib+efYzn57KetqcnTctjWH9NqVGfMXs2ptNdU1OV6YuYAeXbZPOdrSqIkouKWtVHPcFRGxHCAiZkkaBPxZ0u5sJnFn1V13/Y7DBx5M586deHPmRK6++lpuu/2etMPKvB267MAVv76EiooKKioqeOKvT/LsY88z67XZXHXjD6i6+Exee3kGY0b8Pe1QM+XSu8cyceZ8lqxYzdE/HsG5nzqQk/r34sr7nuaz142iZWUlPzx1IJJo37YVZwzcl9N/+yACPtG7OwP/pzynq7aEKZBCqRQT8pKeAL4TEZNr9bUAhgOnR0RlfWO03KZbdn6KGTVgx73TDqHsPXajL+k0hzYnXtzkgvDgbkcUnHOemzs21QK0VBX3l4AP3REREdXAlyT9oUTnNDNrtK1+VUlEzImIBXUc84SkmW1xirWqRFJ3SWMlTZP08vrre5L+T9JcSZOTNqSxsXodt5kZRd1kqhr4bkRMkrQd8IKkR5Njv4qIa5t6AiduMzOgJoqzsWtEzAfmJ6/flzQd6FaUwRPlee+qmVkDRUTBrfY9J0mr2tSYkvYA+gL/TLrOkzRF0nBJHRsbqxO3mRkNm+Oufc9J0oZtPF5y1/go4IKIWAbcBHwE6EO+Ir+usbF6qsTMjOI+SEFSS/JJ++6IuB8gIhbWOn4zMKax4ztxm5kBuSItB5Qk4FZgekT8slZ/12T+G+BkYGpjz+HEbWZGUSvuQ4EzgH9LWn8T4veBoZL6AAHMAs5u7AmcuM3MKOqqkvFsemuPh4pyApy4zcyA4k2VNAcnbjMz/JR3M7PMccVtZpYxrrjNzDKmJmrSDqFgTtxmZmRrW1cnbjMzsvUEHCduMzNccZuZZY5XlZiZZYxXlZiZZUyxbnlvDk7cZmZ4jtvMLHM8x21mljGuuM3MMsbruM3MMsYVt5lZxnhViZlZxvjipJlZxmRpqqQi7QDMzLYE0YB/6iNpsKRXJc2QdGmxY3XFbWZG8SpuSZXA74BPAXOACZJGR8S0opwAJ24zM6Coc9wDgBkRMRNA0j3AiUD5J+51a+du6vH2WzRJVRExLO04ypl/xqW3tf6MqxuQcyRVAVW1uobV+pl1A96qdWwO8LGmR/gBz3EXV1X9H7Em8s+49PwzrkdEDIuIfrVas/6ic+I2MyuuuUD3Wu93TfqKxonbzKy4JgA9Je0paRvgNGB0MU+wxc5xZ9RWNy+YAv+MS88/4yaIiGpJ5wH/ACqB4RHxcjHPoSwtOjczM0+VmJlljhO3mVnGOHEXQalvbzWQNFzSIklT046lXEnqLmmspGmSXpb07bRjsk3zHHcTJbe3vkat21uBocW8vdVA0kBgOXBnROybdjzlSFJXoGtETJK0HfACcJL/Lm95XHE33YbbWyNiLbD+9lYroogYB7yXdhzlLCLmR8Sk5PX7wHTydwHaFsaJu+k2dXur/7JbpknaA+gL/DPdSGxTnLjN7EMktQNGARdExLK047H/5sTddCW/vdWsuUhqST5p3x0R96cdj22aE3fTlfz2VrPmIEnArcD0iPhl2vFY3Zy4mygiqoH1t7dOB0YW+/ZWA0kjgOeAvSXNkXRW2jGVoUOBM4BPSpqctCFpB2X/zcsBzcwyxhW3mVnGOHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxW0lIqkmWk02VdJ+ktk0Y63ZJn0te3yJpn818dpCkQxpxjlmSOjc2RrPm5MRtpbIqIvokO/mtBc6pfVBSox6bFxFfq2e3ukFAgxO3WZY4cVtzeBrYK6mGn5Y0GpgmqVLSLyRNkDRF0tmQv4NP0g3JHuePATutH0jSk5L6Ja8HS5ok6SVJjycbI50DXJhU+4dJ2lHSqOQcEyQdmnztDpIeSfadvgVQ8/5IzBrPDwu2kkoq62OBh5OuA4F9I+JNSVXA0ojoL6kV8IykR8jvSrc3sA/QBZgGDN9o3B2Bm4GByVidIuI9Sb8HlkfEtcnn/gT8KiLGS9qN/B2u/wNcCYyPiKslHQf4TkzLDCduK5U2kiYnr58mvwfGIcC/IuLNpP9oYP/189dAB6AnMBAYERE1wDxJT2xi/I8D49aPFRF17dV9FLBPfhsOANonu98NBD6TfO3fJC1u5Pdp1uycuK1UVkVEn9odSfJcUbsLOD8i/rHR54q5P0YF8PGIWL2JWMwyyXPclqZ/AOcmW4kiqZekbYFxwKnJHHhX4IhNfO3zwEBJeyZf2ynpfx/YrtbnHgHOX/9G0vpfJuOALyR9xwIdi/ZdmZWYE7el6Rby89eTkocA/4H8/wX+BXg9OXYn+V0BPyQi3gaqgPslvQTcmxz6K3Dy+ouTwLeAfsnFz2l8sLrlKvKJ/2XyUyb/KdH3aFZ03h3QzCxjXHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWXM/wNNeSVRK7ZxlAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Let's look at model's predictions distribution\n",
        "confusion_matrix = pd.crosstab(val['labels'], val['predictions'], rownames=['Actual'], colnames=['Predicted'])\n",
        "\n",
        "sn.heatmap(confusion_matrix, annot=True, fmt='d')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "id": "j19nek7G5JwR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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              "                id                                               text  \\\n",
              "0    7212131785749  Ordered a mask that had a cute chain attached ...   \n",
              "1       2367433035  Who is ready for some #baseball?  #BaseballisB...   \n",
              "2           496011  @Mystere07623203 @va_shiva @Liberty13046 130,0...   \n",
              "3          1490686  When they ask me what I did with my life I wil...   \n",
              "4          1209366  Taylor not putting her album in physical store...   \n",
              "..             ...                                                ...   \n",
              "595        2949472  @BrianKempGA is one of the few #Governors that...   \n",
              "596         208269  I tested positive for Covid. Got it from a fri...   \n",
              "597        2316008  If you are under retirement age, you have a *m...   \n",
              "598         659269  @TheLalasventure @doqholliday @realDonaldTrump...   \n",
              "599        1940282  @Caissie Obviously I was simply practicing my ...   \n",
              "\n",
              "                   Claim Stance  \n",
              "0             face masks  FAVOR  \n",
              "1    stay at home orders   NONE  \n",
              "2             face masks   NONE  \n",
              "3             face masks  FAVOR  \n",
              "4    stay at home orders   NONE  \n",
              "..                   ...    ...  \n",
              "595           face masks  FAVOR  \n",
              "596           face masks  FAVOR  \n",
              "597  stay at home orders  FAVOR  \n",
              "598           face masks  FAVOR  \n",
              "599           face masks  FAVOR  \n",
              "\n",
              "[599 rows x 4 columns]"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Mapping back integer labels to words\n",
        "val.loc[val[\"predictions\"] == 0, \"predictions\"] = \"AGAINST\"\n",
        "val.loc[val[\"predictions\"] == 1, \"predictions\"] = \"FAVOR\"\n",
        "val.loc[val[\"predictions\"] == 2, \"predictions\"] = \"NONE\"\n",
        "#Final submission is need to be in the following format\n",
        "val = val.drop(columns=['labels', 'Premise']).rename(columns={'predictions':\n",
        "                                                      'Stance'})\n",
        "val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "id": "rzaHBJMyY9ZK",
      "metadata": {
        "id": "rzaHBJMyY9ZK"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "id": "0yo2brpdms2R",
      "metadata": {
        "id": "0yo2brpdms2R"
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      "source": [
        "y_true = list(y_true)\n",
        "y_pred = [i for i in y_pred]"
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    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "id": "RN0ykjWj89je",
      "metadata": {
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        "outputId": "bb05b085-e4e9-4300-ba6f-e4cd677e8e11"
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          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.05      0.10       158\n",
            "           1       0.61      0.80      0.70       244\n",
            "           2       0.61      0.84      0.71       197\n",
            "\n",
            "    accuracy                           0.62       599\n",
            "   macro avg       0.71      0.57      0.50       599\n",
            "weighted avg       0.69      0.62      0.54       599\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_true, y_pred, target_names=['0', '1', '2']))"
      ]
    }
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