Premise_classification_RoBERTa_baseline.ipynb

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      "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": 3,
      "metadata": {
        "id": "SvrRQU5qFFU4"
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      "outputs": [],
      "source": [
        "#logging \n",
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "6Eq7S1hlFIm3"
      },
      "outputs": [],
      "source": [
        "#read train dataset\n",
        "train = pd.read_csv('data/train.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9loM5oOTJ0I"
      },
      "source": [
        "# New section"
      ]
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      "execution_count": 6,
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            "text/plain": [
              "                 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",
              "2754     2400838291  @Leo_girl001 The D-Day is finally here. ...  Y...   \n",
              "2755         825397  @FOX10Phoenix the boys &amp; girls clubs are O...   \n",
              "\n",
              "                    Claim   Stance  labels  \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  \n",
              "...                   ...      ...     ...  \n",
              "2751      school closures    FAVOR       1  \n",
              "2752           face masks     NONE       1  \n",
              "2753      school closures    FAVOR       1  \n",
              "2754  stay at home orders     NONE       0  \n",
              "2755      school closures  AGAINST       1  \n",
              "\n",
              "[2756 rows x 5 columns]"
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          "metadata": {},
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      ],
      "source": [
        "#rename columns to match SimpleTransformers format\n",
        "column_names = {'Tweet': 'text', 'Premise':'labels'}\n",
        "train = train.rename(columns = column_names)\n",
        "\n",
        "train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
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      "source": [
        "# Optional model configuration\n",
        "model_args = ClassificationArgs(num_train_epochs=1)"
      ]
    },
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              "  "
            ],
            "text/plain": [
              "                 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",
              "2754     2400838291  @Leo_girl001 The D-Day is finally here. ...  Y...   \n",
              "2755         825397  @FOX10Phoenix the boys &amp; girls clubs are O...   \n",
              "\n",
              "                    Claim   Stance  labels  \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  \n",
              "...                   ...      ...     ...  \n",
              "2751      school closures    FAVOR       1  \n",
              "2752           face masks     NONE       1  \n",
              "2753      school closures    FAVOR       1  \n",
              "2754  stay at home orders     NONE       0  \n",
              "2755      school closures  AGAINST       1  \n",
              "\n",
              "[2756 rows x 5 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#rename columns to match SimpleTransformers format\n",
        "column_names = {'Tweet': 'text', 'Premise':'labels'}\n",
        "train = train.rename(columns = column_names)\n",
        "\n",
        "train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "CBC2uZ4MUWpA"
      },
      "outputs": [],
      "source": [
        "# Optional model configuration\n",
        "model_args = ClassificationArgs(num_train_epochs=1)"
      ]
    },
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "158ff769f6244937a1a1ccc11acfc65a",
              "version_major": 2,
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            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "78ef97fe4ea0453abc06a647898de2b6",
              "version_major": 2,
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            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/501M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
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          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight']\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.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
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              "Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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        {
          "data": {
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            "text/plain": [
              "Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
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        {
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              "model_id": "8e900aa58e7041c28cbe811e9fefb5ae",
              "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": "40e7096d8a6c43a28c1dc18345dfeaf4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "309e708e79ba452782d3f11f3dd890ff",
              "version_major": 2,
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            },
            "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 49.2 s, sys: 11.1 s, total: 1min\n",
            "Wall time: 1min 14s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(345, 0.5216631578362506)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "model = ClassificationModel(\n",
        "    \"roberta\", \"roberta-base\", args=model_args\n",
        ")\n",
        "model.train_model(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "f-F68yt2ZDzV"
      },
      "outputs": [],
      "source": [
        "#read validation dataset\n",
        "val = pd.read_csv('data/validation.csv')\n",
        "\n",
        "#rename columns and values to match the format\n",
        "val = val.rename(columns=column_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YSTirIuUZKWP",
        "outputId": "af8827d0-83d7-4fab-ecc3-35e23040829d"
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      "outputs": [
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              "          const element = document.querySelector('#df-59d6cdeb-0a79-4675-a011-e00ff6119155');\n",
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              "            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",
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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",
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              "4          1209366  Taylor not putting her album in physical store...   \n",
              "..             ...                                                ...   \n",
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              "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  labels  \n",
              "0             face masks    FAVOR       0  \n",
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              "2             face masks    FAVOR       1  \n",
              "3             face masks    FAVOR       0  \n",
              "4    stay at home orders    FAVOR       0  \n",
              "..                   ...      ...     ...  \n",
              "595           face masks  AGAINST       1  \n",
              "596           face masks    FAVOR       1  \n",
              "597  stay at home orders  AGAINST       1  \n",
              "598           face masks    FAVOR       0  \n",
              "599           face masks    FAVOR       0  \n",
              "\n",
              "[600 rows x 5 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
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        "val\n"
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      "cell_type": "code",
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        },
        "id": "LkLMDv2nZMt1",
        "outputId": "4b808190-1b9b-46fd-f4fa-4e79930563c1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a4ced0bcda1d42edbfbbcc73662264a0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/600 [00:00<?, ?it/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "de58eef38810434eb814d9183fd2046f",
              "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': 0.5879246237753912,\n",
              " 'tp': 170,\n",
              " 'tn': 313,\n",
              " 'fp': 67,\n",
              " 'fn': 50,\n",
              " 'auroc': 0.8820693779904305,\n",
              " 'auprc': 0.7866345993222517,\n",
              " 'eval_loss': 0.41199573516845706}"
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          },
          "execution_count": 13,
          "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": 14,
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        "id": "tUgBt8vUZPJD",
        "outputId": "7a3f2f36-b45e-46aa-c018-ff4f58beb0fc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "69baf03a147f48138c20de6ccf8a57d3",
              "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": "3d2a35e39f874b1592a720a005c37fce",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "  0%|          | 0/75 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ]
        }
      ],
      "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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "gLN2Ixq3ZReF",
        "outputId": "3075b7ea-a515-4eda-efbb-afd27eba2fbc"
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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  labels  predictions  \n",
              "0             face masks    FAVOR       0            0  \n",
              "1    stay at home orders     NONE       0            0  \n",
              "2             face masks    FAVOR       1            1  \n",
              "3             face masks    FAVOR       0            0  \n",
              "4    stay at home orders    FAVOR       0            0  \n",
              "..                   ...      ...     ...          ...  \n",
              "595           face masks  AGAINST       1            0  \n",
              "596           face masks    FAVOR       1            1  \n",
              "597  stay at home orders  AGAINST       1            1  \n",
              "598           face masks    FAVOR       0            0  \n",
              "599           face masks    FAVOR       0            0  \n",
              "\n",
              "[599 rows x 6 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "4TgL03azmt3-"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Y8A7W84RZTp-"
      },
      "outputs": [],
      "source": [
        "y_true = np.array(val['labels']).reshape(-1, 1)\n",
        "y_pred = np.array(val['predictions']).reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
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          "height": 279
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        "id": "O3jGp8_KZV1O",
        "outputId": "9ecf05dd-4f50-4bde-d94c-55b11232a6f2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "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": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZFski4cnZYXH",
        "outputId": "2ffddc14-2ec0-4c1b-c4a2-9f8b62bd1c50"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5c989b7d-01f3-4a6b-b074-fbafd8e93246\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>text</th>\n",
              "      <th>Claim</th>\n",
              "      <th>Premise</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7212131785749</td>\n",
              "      <td>Ordered a mask that had a cute chain attached ...</td>\n",
              "      <td>face masks</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2367433035</td>\n",
              "      <td>Who is ready for some #baseball?  #BaseballisB...</td>\n",
              "      <td>stay at home orders</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>496011</td>\n",
              "      <td>@Mystere07623203 @va_shiva @Liberty13046 130,0...</td>\n",
              "      <td>face masks</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1490686</td>\n",
              "      <td>When they ask me what I did with my life I wil...</td>\n",
              "      <td>face masks</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1209366</td>\n",
              "      <td>Taylor not putting her album in physical store...</td>\n",
              "      <td>stay at home orders</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>2949472</td>\n",
              "      <td>@BrianKempGA is one of the few #Governors that...</td>\n",
              "      <td>face masks</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>208269</td>\n",
              "      <td>I tested positive for Covid. Got it from a fri...</td>\n",
              "      <td>face masks</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>2316008</td>\n",
              "      <td>If you are under retirement age, you have a *m...</td>\n",
              "      <td>stay at home orders</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>659269</td>\n",
              "      <td>@TheLalasventure @doqholliday @realDonaldTrump...</td>\n",
              "      <td>face masks</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>1940282</td>\n",
              "      <td>@Caissie Obviously I was simply practicing my ...</td>\n",
              "      <td>face masks</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>599 rows × 4 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c989b7d-01f3-4a6b-b074-fbafd8e93246')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-5c989b7d-01f3-4a6b-b074-fbafd8e93246');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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  Premise  \n",
              "0             face masks        0  \n",
              "1    stay at home orders        0  \n",
              "2             face masks        1  \n",
              "3             face masks        0  \n",
              "4    stay at home orders        0  \n",
              "..                   ...      ...  \n",
              "595           face masks        0  \n",
              "596           face masks        1  \n",
              "597  stay at home orders        1  \n",
              "598           face masks        0  \n",
              "599           face masks        0  \n",
              "\n",
              "[599 rows x 4 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val = val.drop(columns=['labels', 'Stance']).rename(columns={'predictions':\n",
        "                                                      'Premise'})\n",
        "val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Opb3v6v-ZdOk"
      },
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      "source": [
        "# accuracy, precision, recall, f1-score, log loss\n",
        "\n",
        "def get_metrics(y_true, y_pred):\n",
        "    from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, log_loss\n",
        "    accuracy = accuracy_score(y_true, y_pred)\n",
        "    precision = precision_score(y_true, y_pred, average='weighted')\n",
        "    recall = recall_score(y_true, y_pred, average='weighted')\n",
        "    f1 = f1_score(y_true, y_pred, average='weighted')\n",
        "    logloss = log_loss(y_true, y_pred)\n",
        "    return accuracy, precision, recall, f1, logloss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "1gPBXrkmZfnV"
      },
      "outputs": [],
      "source": [
        "accuracy, precision, recall, f1, logloss = get_metrics(y_true, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QVACI_HZhg8",
        "outputId": "4e0905ea-de36-4153-a33e-89b504a6dde4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8046744574290484\n",
            "Precision:  0.8087776976204561\n",
            "Recall:  0.8046744574290484\n",
            "F1:  0.8060668506399683\n",
            "Log loss:  6.7463946765148135\n"
          ]
        }
      ],
      "source": [
        "print('Accuracy: ', accuracy)\n",
        "print('Precision: ', precision)\n",
        "print('Recall: ', recall)\n",
        "print('F1: ', f1)\n",
        "print('Log loss: ', logloss)"
      ]
    }
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