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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIoXgM54A0gW",
        "outputId": "86991ba4-40eb-4600-e489-a4ca30f7593d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.19.3-py3-none-any.whl (4.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.2 MB 4.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 29.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.4)\n",
            "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 43.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.7.0-py3-none-any.whl (86 kB)\n",
            "\u001b[K     |████████████████████████████████| 86 kB 3.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.2.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.5.18.1)\n",
            "Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.7.0 pyyaml-6.0 tokenizers-0.12.1 transformers-4.19.3\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()         \n",
        "import pandas as pd\n",
        "train_ori = pd.read_csv(\"fixed_train.csv\")\n",
        "valid_ori = pd.read_csv(\"fixed_valid.csv\")\n",
        "test_ori = pd.read_csv(\"fixed_test.csv\")\n",
        "train_ori.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "-YCNO41kA5lH",
        "outputId": "5474881a-34d7-4026-c317-1189f7c2697d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-94e3c713-02c3-48fa-bf55-89065dcb9bd0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-94e3c713-02c3-48fa-bf55-89065dcb9bd0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving fixed_test.csv to fixed_test.csv\n",
            "Saving fixed_train.csv to fixed_train.csv\n",
            "Saving fixed_valid.csv to fixed_valid.csv\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        conv_id  utterance_idx  \\\n",
              "0  hit:0_conv:1              1   \n",
              "1  hit:0_conv:1              2   \n",
              "2  hit:0_conv:1              3   \n",
              "3  hit:0_conv:1              4   \n",
              "4  hit:0_conv:1              5   \n",
              "\n",
              "                                              prompt  \\\n",
              "0  I remember going to the fireworks with my best...   \n",
              "1  I remember going to the fireworks with my best...   \n",
              "2  I remember going to the fireworks with my best...   \n",
              "3  I remember going to the fireworks with my best...   \n",
              "4  I remember going to the fireworks with my best...   \n",
              "\n",
              "                                           utterance  label  \n",
              "0  I remember going to see the fireworks with my ...     13  \n",
              "1  Was this a friend you were in love with_comma_...     13  \n",
              "2                This was a best friend. I miss her.     13  \n",
              "3                                Where has she gone?     13  \n",
              "4                                 We no longer talk.     13  "
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              "      <td>I remember going to the fireworks with my best...</td>\n",
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              "      <td>13</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "          const element = document.querySelector('#df-d15ad669-e544-4359-bd3e-bc8bfc489535');\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Integration"
      ],
      "metadata": {
        "id": "jBBf_UXueIWt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = train_ori.groupby(['conv_id', 'prompt', 'label'], as_index=False).agg({\n",
        "    'utterance': lambda u: ' '.join(u)\n",
        "    }).reindex(columns=['conv_id', 'prompt', 'utterance', 'label'])\n",
        "\n",
        "valid = valid_ori.groupby(['conv_id', 'prompt', 'label'], as_index=False).agg({\n",
        "    'utterance': lambda u: ' '.join(u)\n",
        "    }).reindex(columns=['conv_id', 'prompt', 'utterance', 'label'])\n",
        "\n",
        "test = test_ori.groupby(['conv_id', 'prompt'], as_index=False).agg({\n",
        "    'utterance': lambda u: ' '.join(u)\n",
        "    }).reindex(columns=['conv_id', 'prompt', 'utterance'])\n",
        "\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MZsxYhd-eOmN",
        "outputId": "6f9a7b83-4d69-4017-ef7a-bde70045b2dd"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    conv_id  \\\n",
              "0              hit:0_conv:1   \n",
              "1      hit:10000_conv:20000   \n",
              "2      hit:10000_conv:20001   \n",
              "3      hit:10001_conv:20002   \n",
              "4      hit:10002_conv:20004   \n",
              "...                     ...   \n",
              "19528   hit:9999_conv:19999   \n",
              "19529     hit:999_conv:1998   \n",
              "19530     hit:999_conv:1999   \n",
              "19531       hit:99_conv:198   \n",
              "19532         hit:9_conv:19   \n",
              "\n",
              "                                                  prompt  \\\n",
              "0      I remember going to the fireworks with my best...   \n",
              "1      My girlfriend got me a toad today! I was so sh...   \n",
              "2           I really like the new paint job on my house.   \n",
              "3      I went to the skating rink all by myself today...   \n",
              "4      I was walking on the road. I saw beggar and i ...   \n",
              "...                                                  ...   \n",
              "19528  I went skydiving for the first time recently. ...   \n",
              "19529    I recently took the Law School Admissions Test.   \n",
              "19530      I was shocked when Lebron left the Cavs again   \n",
              "19531           I am ashamed when I cheated on the test.   \n",
              "19532  one time when I was in preschool_comma_ I had ...   \n",
              "\n",
              "                                               utterance  label  \n",
              "0      I remember going to see the fireworks with my ...     13  \n",
              "1      My girlfriend got me a pet toad today! Do you ...      9  \n",
              "2      I really like the new paint job on my house. T...     12  \n",
              "3      I went to the skating rink all by myself today...     21  \n",
              "4      I was walking on the road. I saw a beggar and ...     10  \n",
              "...                                                  ...    ...  \n",
              "19528  So I went skydiving for the first time the oth...     24  \n",
              "19529  I believe I did rather well on my Law School A...     23  \n",
              "19530  I was shocked when Lebron left the cavs again!...     14  \n",
              "19531  I cheated on a test. I am very ashamed. oh no ...     10  \n",
              "19532  Once when I was in preschool_comma_ my pants f...     26  \n",
              "\n",
              "[19533 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c1547e93-565a-4166-b78c-946b4147a08c\">\n",
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              "      <th>0</th>\n",
              "      <td>hit:0_conv:1</td>\n",
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              "      <td>I remember going to see the fireworks with my ...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hit:10000_conv:20000</td>\n",
              "      <td>My girlfriend got me a toad today! I was so sh...</td>\n",
              "      <td>My girlfriend got me a pet toad today! Do you ...</td>\n",
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              "      <th>2</th>\n",
              "      <td>hit:10000_conv:20001</td>\n",
              "      <td>I really like the new paint job on my house.</td>\n",
              "      <td>I really like the new paint job on my house. T...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hit:10001_conv:20002</td>\n",
              "      <td>I went to the skating rink all by myself today...</td>\n",
              "      <td>I went to the skating rink all by myself today...</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hit:10002_conv:20004</td>\n",
              "      <td>I was walking on the road. I saw beggar and i ...</td>\n",
              "      <td>I was walking on the road. I saw a beggar and ...</td>\n",
              "      <td>10</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>19528</th>\n",
              "      <td>hit:9999_conv:19999</td>\n",
              "      <td>I went skydiving for the first time recently. ...</td>\n",
              "      <td>So I went skydiving for the first time the oth...</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19529</th>\n",
              "      <td>hit:999_conv:1998</td>\n",
              "      <td>I recently took the Law School Admissions Test.</td>\n",
              "      <td>I believe I did rather well on my Law School A...</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19530</th>\n",
              "      <td>hit:999_conv:1999</td>\n",
              "      <td>I was shocked when Lebron left the Cavs again</td>\n",
              "      <td>I was shocked when Lebron left the cavs again!...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19531</th>\n",
              "      <td>hit:99_conv:198</td>\n",
              "      <td>I am ashamed when I cheated on the test.</td>\n",
              "      <td>I cheated on a test. I am very ashamed. oh no ...</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19532</th>\n",
              "      <td>hit:9_conv:19</td>\n",
              "      <td>one time when I was in preschool_comma_ I had ...</td>\n",
              "      <td>Once when I was in preschool_comma_ my pants f...</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19533 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1547e93-565a-4166-b78c-946b4147a08c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \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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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c1547e93-565a-4166-b78c-946b4147a08c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c1547e93-565a-4166-b78c-946b4147a08c');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"# original training samples：\", len(train_ori))\n",
        "print(\"# original validation samples：\", len(valid_ori))\n",
        "print(\"# original test samples：\", len(test_ori))\n",
        "print(\"-------------------------------------\")\n",
        "print(\"# training samples：\", len(train))\n",
        "print(\"# validation samples：\", len(valid))\n",
        "print(\"# test samples：\", len(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_35PYQ7kCJov",
        "outputId": "0a2ab2a4-c305-4664-cb42-8064a8c75f18"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# original training samples： 84169\n",
            "# original validation samples： 12078\n",
            "# original test samples： 10973\n",
            "-------------------------------------\n",
            "# training samples： 19533\n",
            "# validation samples： 2770\n",
            "# test samples： 2547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "labels = [x for x in range(32)]\n",
        "num_labels = [sum(train.label == x) for  x in range(32)]\n",
        "plt.pie(num_labels, labels=labels, autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "-iVPWIYmCUFZ",
        "outputId": "ad24899f-e267-4599-ef8f-c9d6deb8619d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from torch.utils.data import Dataset\n",
        "from IPython.display import clear_output\n",
        "from transformers import RobertaTokenizer, TFRobertaModel\n",
        "tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
        "#tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "class EmotionDataset(Dataset):\n",
        "    def __init__(self, mode, df, tokenizer):\n",
        "        assert mode in ['train', 'valid', 'test']\n",
        "        self.mode = mode\n",
        "        self.df = df.fillna(\"\")\n",
        "        self.len = len(self.df)\n",
        "        self.tokenizer = tokenizer\n",
        "    \n",
        "    # 定義回傳一筆訓練 / 測試數據的函式\n",
        "    def __getitem__(self, idx):\n",
        "        if self.mode == 'test':\n",
        "            prompt, utterance = self.df.iloc[idx, 1:3].values\n",
        "            label_tensor = None\n",
        "        else:\n",
        "            prompt, utterance, label = self.df.iloc[idx, 1:].values\n",
        "            label_tensor = torch.tensor(label)\n",
        "            \n",
        "        # 建立第一個句子的 BERT tokens 並加入分隔符號 [SEP]\n",
        "        #prompt = prompt.replace(\"_comma_\", \",\")\n",
        "        #text =   prompt \n",
        "        #utterance = utterance.replace(\"_comma_\", \",\")\n",
        "        #text += utterance \n",
        "\n",
        "        word_pieces = self.tokenizer.encode_plus(prompt.replace(\"_comma_\", \",\")+utterance.replace(\"_comma_\", \",\"))\n",
        "        #print(word_pieces[\"input_ids\"])\n",
        "        # 將整個 token 序列轉換成索引序列\n",
        "        ids = word_pieces[\"input_ids\"]\n",
        "        tokens_tensor = torch.tensor(ids)[:512]\n",
        "        \n",
        "        # 將第一句包含 [SEP] 的 token 位置設為 0\n",
        "        segments_tensor = torch.tensor(\n",
        "            [0] * len(tokens_tensor),\n",
        "            dtype=torch.long\n",
        "        )[:512]\n",
        "        \n",
        "        return (tokens_tensor, segments_tensor, label_tensor)\n",
        "    \n",
        "    def __len__(self):\n",
        "        return self.len\n",
        "    \n",
        "    \n",
        "# # Initialize Datasets for Transformation\n",
        "# trainset = EmotionDataset('train', train, True, tokenizer=tokenizer)\n",
        "# validset = EmotionDataset('valid', valid, True, tokenizer=tokenizer)\n",
        "\n",
        "# print('trainset size:', trainset.__len__())\n",
        "# print('validset size:', validset.__len__())"
      ],
      "metadata": {
        "id": "G1X8fmV1DaUt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "b587305c3fed48ca93923ff162b7ccf8",
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            "0552feac83a54c87af725135bb1106cd",
            "e02cdb91853e49549d8b3efbe1768931",
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            "0bfe398154c74eb0ab4b3c5a2e832321",
            "8d465d24b8594e938d6e3239ce13a6cf",
            "e6d75876bb0643148ea14763faefb3f0",
            "a7b83d043e994d41867584353d3ea20a",
            "1da38854f5b74a05b4e607952105390a",
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          ]
        },
        "outputId": "d306c5bb-1e13-4e7c-f886-3991172833b9"
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          "output_type": "display_data",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Datasets for Transformation\n",
        "trainset = EmotionDataset('train', train, tokenizer=tokenizer)\n",
        "validset = EmotionDataset('valid', valid, tokenizer=tokenizer)\n",
        "\n",
        "# print('trainset size:', trainset.__len__())\n",
        "# print('validset size:', validset.__len__())\n",
        "\n",
        "# 選擇第一個樣本\n",
        "sample_idx = 0\n",
        "\n",
        "# 將原始文本拿出做比較\n",
        "prompt, utterance, label = trainset.df.iloc[sample_idx, 1:].values\n",
        "\n",
        "# 利用剛剛建立的 Dataset 取出轉換後的 id tensors\n",
        "tokens_tensor, segments_tensor, label_tensor = trainset[sample_idx]\n",
        "\n",
        "# 將 tokens_tensor 還原成文本\n",
        "tokens = tokenizer.convert_ids_to_tokens(tokens_tensor.tolist())\n",
        "combined_text = \" \".join(tokens)\n",
        "\n",
        "# 渲染前後差異，毫無反應就是個 print。可以直接看輸出結果\n",
        "print(f\"\"\"[Original Statement]\n",
        "prompt:     {prompt}\n",
        "utterance:  {utterance}\n",
        "label:      {label}\n",
        "\n",
        "tokens: {tokens}\n",
        "\n",
        "--------------------\n",
        "\n",
        "[Tensors]\n",
        "tokens_tensor:\n",
        "    {tokens_tensor}\n",
        "\n",
        "segments_tensor:\n",
        "    {segments_tensor}\n",
        "\n",
        "label_tensor:\n",
        "    {label_tensor}\n",
        "\n",
        "--------------------\n",
        "\n",
        "[Recovered Text]\n",
        "{combined_text}\n",
        "\"\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "InNnLCQfIjU5",
        "outputId": "27fcafd8-22ad-4533-cb44-9ee0cb99fbd7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Original Statement]\n",
            "prompt:     I remember going to the fireworks with my best friend. There was a lot of people_comma_ but it only felt like us in the world.\n",
            "utterance:  I remember going to see the fireworks with my best friend. It was the first time we ever spent time alone together. Although there was a lot of people_comma_ we felt like the only people in the world. Was this a friend you were in love with_comma_ or just a best friend? This was a best friend. I miss her. Where has she gone? We no longer talk. Oh was this something that happened because of an argument?\n",
            "label:      13\n",
            "\n",
            "tokens: ['<s>', 'I', 'Ġremember', 'Ġgoing', 'Ġto', 'Ġthe', 'Ġfireworks', 'Ġwith', 'Ġmy', 'Ġbest', 'Ġfriend', '.', 'ĠThere', 'Ġwas', 'Ġa', 'Ġlot', 'Ġof', 'Ġpeople', ',', 'Ġbut', 'Ġit', 'Ġonly', 'Ġfelt', 'Ġlike', 'Ġus', 'Ġin', 'Ġthe', 'Ġworld', '.', 'I', 'Ġremember', 'Ġgoing', 'Ġto', 'Ġsee', 'Ġthe', 'Ġfireworks', 'Ġwith', 'Ġmy', 'Ġbest', 'Ġfriend', '.', 'ĠIt', 'Ġwas', 'Ġthe', 'Ġfirst', 'Ġtime', 'Ġwe', 'Ġever', 'Ġspent', 'Ġtime', 'Ġalone', 'Ġtogether', '.', 'ĠAlthough', 'Ġthere', 'Ġwas', 'Ġa', 'Ġlot', 'Ġof', 'Ġpeople', ',', 'Ġwe', 'Ġfelt', 'Ġlike', 'Ġthe', 'Ġonly', 'Ġpeople', 'Ġin', 'Ġthe', 'Ġworld', '.', 'ĠWas', 'Ġthis', 'Ġa', 'Ġfriend', 'Ġyou', 'Ġwere', 'Ġin', 'Ġlove', 'Ġwith', ',', 'Ġor', 'Ġjust', 'Ġa', 'Ġbest', 'Ġfriend', '?', 'ĠThis', 'Ġwas', 'Ġa', 'Ġbest', 'Ġfriend', '.', 'ĠI', 'Ġmiss', 'Ġher', '.', 'ĠWhere', 'Ġhas', 'Ġshe', 'Ġgone', '?', 'ĠWe', 'Ġno', 'Ġlonger', 'Ġtalk', '.', 'ĠOh', 'Ġwas', 'Ġthis', 'Ġsomething', 'Ġthat', 'Ġhappened', 'Ġbecause', 'Ġof', 'Ġan', 'Ġargument', '?', '</s>']\n",
            "\n",
            "--------------------\n",
            "\n",
            "[Tensors]\n",
            "tokens_tensor:\n",
            "    tensor([    0,   100,  2145,   164,     7,     5, 10756,    19,   127,   275,\n",
            "         1441,     4,   345,    21,    10,   319,     9,    82,     6,    53,\n",
            "           24,   129,  1299,   101,   201,    11,     5,   232,     4,   100,\n",
            "         2145,   164,     7,   192,     5, 10756,    19,   127,   275,  1441,\n",
            "            4,    85,    21,     5,    78,    86,    52,   655,  1240,    86,\n",
            "         1937,   561,     4,  2223,    89,    21,    10,   319,     9,    82,\n",
            "            6,    52,  1299,   101,     5,   129,    82,    11,     5,   232,\n",
            "            4,  6871,    42,    10,  1441,    47,    58,    11,   657,    19,\n",
            "            6,    50,    95,    10,   275,  1441,   116,   152,    21,    10,\n",
            "          275,  1441,     4,    38,  2649,    69,     4,  4820,    34,    79,\n",
            "         1613,   116,   166,   117,  1181,  1067,     4,  5534,    21,    42,\n",
            "          402,    14,  1102,   142,     9,    41,  4795,   116,     2])\n",
            "\n",
            "segments_tensor:\n",
            "    tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
            "\n",
            "label_tensor:\n",
            "    13\n",
            "\n",
            "--------------------\n",
            "\n",
            "[Recovered Text]\n",
            "<s> I Ġremember Ġgoing Ġto Ġthe Ġfireworks Ġwith Ġmy Ġbest Ġfriend . ĠThere Ġwas Ġa Ġlot Ġof Ġpeople , Ġbut Ġit Ġonly Ġfelt Ġlike Ġus Ġin Ġthe Ġworld . I Ġremember Ġgoing Ġto Ġsee Ġthe Ġfireworks Ġwith Ġmy Ġbest Ġfriend . ĠIt Ġwas Ġthe Ġfirst Ġtime Ġwe Ġever Ġspent Ġtime Ġalone Ġtogether . ĠAlthough Ġthere Ġwas Ġa Ġlot Ġof Ġpeople , Ġwe Ġfelt Ġlike Ġthe Ġonly Ġpeople Ġin Ġthe Ġworld . ĠWas Ġthis Ġa Ġfriend Ġyou Ġwere Ġin Ġlove Ġwith , Ġor Ġjust Ġa Ġbest Ġfriend ? ĠThis Ġwas Ġa Ġbest Ġfriend . ĠI Ġmiss Ġher . ĠWhere Ġhas Ġshe Ġgone ? ĠWe Ġno Ġlonger Ġtalk . ĠOh Ġwas Ġthis Ġsomething Ġthat Ġhappened Ġbecause Ġof Ġan Ġargument ? </s>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "\n",
        "# create_mini_batch 的參數 `samples` 是一個 list，裡頭的每個 element 都是\n",
        "# 剛剛定義的 `FakeNewsDataset` 回傳的一個樣本，每個樣本都包含 3 tensors：\n",
        "# - tokens_tensor\n",
        "# - segments_tensor\n",
        "# - label_tensor\n",
        "# 它會對前兩個 tensors 作 zero padding，並產生前面說明過的 masks_tensors\n",
        "def create_mini_batch(samples):\n",
        "    tokens_tensors = [s[0] for s in samples]\n",
        "    segments_tensors = [s[1] for s in samples]\n",
        "    \n",
        "    # 測試集有 labels\n",
        "    if samples[0][2] is not None:\n",
        "        label_ids = torch.stack([s[2] for s in samples])\n",
        "    else:\n",
        "        label_ids = None\n",
        "    \n",
        "    # Zero Padding\n",
        "    tokens_tensors = pad_sequence(tokens_tensors, batch_first=True)\n",
        "    segments_tensors = pad_sequence(segments_tensors, batch_first=True)\n",
        "    \n",
        "    # 先製作一條長度和 token_tensors 一樣的 0 張量\n",
        "    # 接著將不為 zero padding 的位置設為1\n",
        "    masks_tensors = torch.zeros(tokens_tensors.shape, dtype=torch.long)\n",
        "    masks_tensors = masks_tensors.masked_fill(tokens_tensors != 0, 1)\n",
        "    \n",
        "    return tokens_tensors, segments_tensors, masks_tensors, label_ids\n",
        "\n",
        "\n",
        "# 初始化一個每次回傳 BATCH_SIZE 個訓練樣本的 DataLoader\n",
        "# 利用 `collate_fn` 將 list of samples 合併成一個 mini-batch 是關鍵\n",
        "trainloader = DataLoader(trainset, batch_size=16, \n",
        "                         collate_fn=create_mini_batch)\n",
        "\n",
        "validloader = DataLoader(validset, batch_size=16, \n",
        "                         collate_fn=create_mini_batch)"
      ],
      "metadata": {
        "id": "8ddBmOC-MHgp"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = next(iter(trainloader))\n",
        "\n",
        "tokens_tensors, segments_tensors, masks_tensors, label_ids = data\n",
        "\n",
        "print(f\"\"\"\n",
        "tokens_tensors.shape   = {tokens_tensors.shape} \n",
        "{tokens_tensors}\n",
        "------------------------\n",
        "segments_tensors.shape = {segments_tensors.shape}\n",
        "{segments_tensors}\n",
        "------------------------\n",
        "masks_tensors.shape    = {masks_tensors.shape}\n",
        "{masks_tensors}\n",
        "------------------------\n",
        "label_ids.shape        = {label_ids.shape}\n",
        "{label_ids}\n",
        "\"\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLNej6EcNG5b",
        "outputId": "ca3c9173-577c-49cf-a40f-f0ba8a55ae1d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "tokens_tensors.shape   = torch.Size([16, 119]) \n",
            "tensor([[   0,  100, 2145,  ..., 4795,  116,    2],\n",
            "        [   0, 2387, 6096,  ...,    0,    0,    0],\n",
            "        [   0,  100,  269,  ...,    0,    0,    0],\n",
            "        ...,\n",
            "        [   0,  100,  437,  ...,    0,    0,    0],\n",
            "        [   0,  100,  524,  ...,    0,    0,    0],\n",
            "        [   0,  100,  269,  ...,    0,    0,    0]])\n",
            "------------------------\n",
            "segments_tensors.shape = torch.Size([16, 119])\n",
            "tensor([[0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0]])\n",
            "------------------------\n",
            "masks_tensors.shape    = torch.Size([16, 119])\n",
            "tensor([[0, 1, 1,  ..., 1, 1, 1],\n",
            "        [0, 1, 1,  ..., 0, 0, 0],\n",
            "        [0, 1, 1,  ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 1, 1,  ..., 0, 0, 0],\n",
            "        [0, 1, 1,  ..., 0, 0, 0],\n",
            "        [0, 1, 1,  ..., 0, 0, 0]])\n",
            "------------------------\n",
            "label_ids.shape        = torch.Size([16])\n",
            "tensor([13,  9, 12, 21, 10, 25, 31,  9, 20, 19, 11,  6, 31, 18, 19, 22])\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import RobertaForSequenceClassification\n",
        "from IPython.display import clear_output\n",
        "\n",
        "PRETRAINED_MODEL_NAME = \"roberta-base\"\n",
        "NUM_LABELS = 32\n",
        "\n",
        "model = RobertaForSequenceClassification.from_pretrained(\n",
        "    PRETRAINED_MODEL_NAME, num_labels=NUM_LABELS)\n",
        "\n",
        "clear_output()\n",
        "\n",
        "print(\"\"\"\n",
        "name             module\n",
        "-----------------------\"\"\")\n",
        "for name, module in model.named_children():\n",
        "    if name == \"roberta\":\n",
        "        for n, _ in module.named_children():\n",
        "            print(f\"{name}:{n}\")\n",
        "    else:\n",
        "        print(\"{:16} {}\".format(name, module))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
            "93794f2f3ed148889a41ca7079a5e8a0",
            "b89e0667911b44cea44e307fabe60ad2",
            "fb29360459cf472bb62b538cb536e7e2",
            "026166a69e284dfeb9f86944252a85a7",
            "78e7ae8b95864405b103606e6ab3b4f0",
            "5c0698adb849409cba2691501845912f",
            "1706e4ed1893470aa1a63ec0e848d280",
            "9c8dd294ba0440d98e5308a63a196ae0",
            "60442b4e775f4502ae120e7aed9431de",
            "cd0cabb349e74f93a2bf8d588e50787a",
            "8358583ed5fc435a81555507f47b4780"
          ]
        },
        "id": "miy6uDJLNg5U",
        "outputId": "c568d5fc-0e49-4040-c329-acb576162829"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "name             module\n",
            "-----------------------\n",
            "roberta:embeddings\n",
            "roberta:encoder\n",
            "classifier       RobertaClassificationHead(\n",
            "  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "  (dropout): Dropout(p=0.1, inplace=False)\n",
            "  (out_proj): Linear(in_features=768, out_features=32, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.config"
      ],
      "metadata": {
        "id": "ZdS449a6OQ6S"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_predictions(model, dataloader, compute_acc=False):\n",
        "    predictions = None\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    running_loss = 0\n",
        "    with torch.no_grad():\n",
        "        for data in tqdm(dataloader):\n",
        "            if next(model.parameters()).is_cuda:\n",
        "                data = [t.to(\"cuda:0\") for t in data if t is not None]\n",
        "            \n",
        "            if not compute_acc:\n",
        "                # 只是單純要回傳預測值的話，不用計算準確度也不用紀錄 loss\n",
        "                tokens_tensors, segments_tensors, masks_tensors = data[:3]\n",
        "                outputs = model(input_ids=tokens_tensors, \n",
        "                                token_type_ids=segments_tensors, \n",
        "                                attention_mask=masks_tensors)\n",
        "                logits = outputs[0]\n",
        "                _, pred = torch.max(logits.data, 1)\n",
        "            else:\n",
        "                # 否則就要計算 loss，這邊有一個小細節是 model 如果有吃 label 的話，\n",
        "                # output[0]會變成是 loss，沒有吃 label 時 output[0] 會是 logits\n",
        "                tokens_tensors, segments_tensors, masks_tensors, labels = data[:4]\n",
        "                outputs = model(input_ids=tokens_tensors, \n",
        "                                token_type_ids=segments_tensors, \n",
        "                                attention_mask=masks_tensors,\n",
        "                                labels=labels)\n",
        "                loss = outputs[0]\n",
        "                logits = outputs[1]\n",
        "                _, pred = torch.max(logits.data, 1)\n",
        "                running_loss += loss.item()\n",
        "                total += labels.size(0)\n",
        "                correct += (pred == labels).sum().item()\n",
        "                \n",
        "            if predictions is None:\n",
        "                predictions = pred\n",
        "            else:\n",
        "                predictions = torch.cat((predictions, pred))\n",
        "    \n",
        "    if compute_acc:\n",
        "        acc = correct / total\n",
        "        loss = running_loss / total\n",
        "        return predictions, acc, loss\n",
        "    \n",
        "    return predictions"
      ],
      "metadata": {
        "id": "5pY3RX_eOUdu"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(\"device:\", device)\n",
        "model = model.to(device)\n",
        "\n",
        "model.train()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n",
        "EPOCHS = 3\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    running_loss = 0.0\n",
        "    for data in tqdm(trainloader):\n",
        "        \n",
        "        tokens_tensors, segments_tensors, \\\n",
        "        masks_tensors, labels = [t.to(device) for t in data]\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        outputs = model(input_ids=tokens_tensors, \n",
        "                        token_type_ids=segments_tensors, \n",
        "                        attention_mask=masks_tensors, \n",
        "                        labels=labels)\n",
        "\n",
        "        loss = outputs[0]\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # 紀錄當前 batch loss\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    # 計算分類準確率\n",
        "    _, train_acc, train_loss = get_predictions(model, trainloader, compute_acc=True)\n",
        "    _, valid_acc, valid_loss = get_predictions(model, validloader, compute_acc=True)\n",
        "    \n",
        "    print('[epoch %d] train loss: %.3f, train acc: %.3f, valid loss: %.3f, valid acc: %.3f' %\n",
        "          (epoch + 1, train_loss, train_acc, valid_loss, valid_acc))\n",
        "#import os\n",
        "#os.environ['CUDA_LAUNCH_BLOCKING']=\"1\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450,
          "referenced_widgets": [
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        },
        "id": "ZgbFiAlPPUPg",
        "outputId": "0bca16a3-3054-400b-be87-372816a81a94"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "device: cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1221 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a711b4f1ced409a966d972813a0b717"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (731 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1221 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c1e65967ee448b3bdcfdd5fd51f3dd7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/174 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f55a9c292054501a71dfaaa894c25f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[epoch 1] train loss: 0.077, train acc: 0.626, valid loss: 0.084, valid acc: 0.588\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1221 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3cabf0e3029f4daba87eac2c89ac4306"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1221 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f7f01279e9c4176ab47c771f534e60a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/174 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adc77cc7eacc49689160df8a79e4e6e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[epoch 2] train loss: 0.062, train acc: 0.685, valid loss: 0.076, valid acc: 0.621\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1221 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e53501673c5d44abbfb8bb84bc12d486"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1221 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9466769b20dc46178de853e332929d55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/174 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d6298d6a1334ee9b5acb3154cdf9a12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[epoch 3] train loss: 0.052, train acc: 0.727, valid loss: 0.076, valid acc: 0.611\n",
            "CPU times: user 31min 53s, sys: 10min 32s, total: 42min 26s\n",
            "Wall time: 42min 32s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), 'checkpoint.pth')\n",
        "print('Model saved!')"
      ],
      "metadata": {
        "id": "MF6TpFyOPbFd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b14dda36-46fc-49ec-f177-fbd663b43844"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test"
      ],
      "metadata": {
        "id": "I9I3xHU6PnU_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RobertaForSequenceClassification.from_pretrained(\n",
        "    PRETRAINED_MODEL_NAME, num_labels=NUM_LABELS)\n",
        "model.load_state_dict(torch.load(\"checkpoint.pth\"))\n",
        "model.to(device)\n",
        "model.eval()\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "5UA3sfvCPhEF"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testset = EmotionDataset(\"test\", test, tokenizer=tokenizer)\n",
        "testloader = DataLoader(testset, batch_size=16, collate_fn=create_mini_batch)\n",
        "\n",
        "predictions = get_predictions(model, testloader)\n",
        "predictions = predictions.cpu().numpy()"
      ],
      "metadata": {
        "id": "9I_4evN1P2Ua",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d8419f8edbb9470fb50f9e640b2fa5cf",
            "e43e5ed38c0f4cc8975608cbd50b9da6",
            "b2fde4cb48ae486a87120d260062ebda",
            "557bef7ae8a34af19c4a4c963066006b",
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          ]
        },
        "outputId": "d96b0017-40b7-4a22-8b5f-873bd47cc72c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/160 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8419f8edbb9470fb50f9e640b2fa5cf"
            }
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_df = pd.DataFrame({'conv_id': test.conv_id, 'pred': predictions})\n",
        "reverted_df = test_ori.merge(prediction_df)"
      ],
      "metadata": {
        "id": "J_nkqAQbTmvR"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reverted_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tMfUH2AstfwU",
        "outputId": "20e07e2e-3521-4561-e2db-6acdda04a16b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    conv_id  utterance_idx  \\\n",
              "0              hit:0_conv:0              1   \n",
              "1              hit:0_conv:0              2   \n",
              "2              hit:0_conv:0              3   \n",
              "3              hit:0_conv:0              4   \n",
              "4              hit:0_conv:0              5   \n",
              "...                     ...            ...   \n",
              "10968  hit:12416_conv:24832              4   \n",
              "10969  hit:12423_conv:24847              1   \n",
              "10970  hit:12423_conv:24847              2   \n",
              "10971  hit:12423_conv:24847              3   \n",
              "10972  hit:12423_conv:24847              4   \n",
              "\n",
              "                                                  prompt  \\\n",
              "0      I felt guilty when I was driving home one nigh...   \n",
              "1      I felt guilty when I was driving home one nigh...   \n",
              "2      I felt guilty when I was driving home one nigh...   \n",
              "3      I felt guilty when I was driving home one nigh...   \n",
              "4      I felt guilty when I was driving home one nigh...   \n",
              "...                                                  ...   \n",
              "10968  I saw a huge cockroach outside my house today....   \n",
              "10969  I have a big test on Monday. I am so nervous_c...   \n",
              "10970  I have a big test on Monday. I am so nervous_c...   \n",
              "10971  I have a big test on Monday. I am so nervous_c...   \n",
              "10972  I have a big test on Monday. I am so nervous_c...   \n",
              "\n",
              "                                               utterance  pred  \n",
              "0      Yeah about 10 years ago I had a horrifying exp...    25  \n",
              "1                           Did you suffer any injuries?    25  \n",
              "2      No I wasn't hit. It turned out they were drunk...    25  \n",
              "3      Why did you feel guilty? People really shouldn...    25  \n",
              "4      I don't know I was new to driving and hadn't e...    25  \n",
              "...                                                  ...   ...  \n",
              "10968           I live in Texas to so i know those feels     8  \n",
              "10969  I have a big test on Monday_comma_ I am so ner...    18  \n",
              "10970                               What is the test on?    18  \n",
              "10971  It's for my Chemistry class. I haven't slept m...    18  \n",
              "10972  Chemistry is quite difficult_comma_have you st...    18  \n",
              "\n",
              "[10973 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a27c6d56-1485-4d3f-b783-5aeeaf2fae8d\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>conv_id</th>\n",
              "      <th>utterance_idx</th>\n",
              "      <th>prompt</th>\n",
              "      <th>utterance</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hit:0_conv:0</td>\n",
              "      <td>1</td>\n",
              "      <td>I felt guilty when I was driving home one nigh...</td>\n",
              "      <td>Yeah about 10 years ago I had a horrifying exp...</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hit:0_conv:0</td>\n",
              "      <td>2</td>\n",
              "      <td>I felt guilty when I was driving home one nigh...</td>\n",
              "      <td>Did you suffer any injuries?</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hit:0_conv:0</td>\n",
              "      <td>3</td>\n",
              "      <td>I felt guilty when I was driving home one nigh...</td>\n",
              "      <td>No I wasn't hit. It turned out they were drunk...</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hit:0_conv:0</td>\n",
              "      <td>4</td>\n",
              "      <td>I felt guilty when I was driving home one nigh...</td>\n",
              "      <td>Why did you feel guilty? People really shouldn...</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hit:0_conv:0</td>\n",
              "      <td>5</td>\n",
              "      <td>I felt guilty when I was driving home one nigh...</td>\n",
              "      <td>I don't know I was new to driving and hadn't e...</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10968</th>\n",
              "      <td>hit:12416_conv:24832</td>\n",
              "      <td>4</td>\n",
              "      <td>I saw a huge cockroach outside my house today....</td>\n",
              "      <td>I live in Texas to so i know those feels</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10969</th>\n",
              "      <td>hit:12423_conv:24847</td>\n",
              "      <td>1</td>\n",
              "      <td>I have a big test on Monday. I am so nervous_c...</td>\n",
              "      <td>I have a big test on Monday_comma_ I am so ner...</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10970</th>\n",
              "      <td>hit:12423_conv:24847</td>\n",
              "      <td>2</td>\n",
              "      <td>I have a big test on Monday. I am so nervous_c...</td>\n",
              "      <td>What is the test on?</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10971</th>\n",
              "      <td>hit:12423_conv:24847</td>\n",
              "      <td>3</td>\n",
              "      <td>I have a big test on Monday. I am so nervous_c...</td>\n",
              "      <td>It's for my Chemistry class. I haven't slept m...</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10972</th>\n",
              "      <td>hit:12423_conv:24847</td>\n",
              "      <td>4</td>\n",
              "      <td>I have a big test on Monday. I am so nervous_c...</td>\n",
              "      <td>Chemistry is quite difficult_comma_have you st...</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10973 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a27c6d56-1485-4d3f-b783-5aeeaf2fae8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a27c6d56-1485-4d3f-b783-5aeeaf2fae8d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a27c6d56-1485-4d3f-b783-5aeeaf2fae8d');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# textfile = open('prediction.txt', 'w')\n",
        "# textfile.write(',pred' + '\\n')\n",
        "# for Id, pred in enumerate(predictions):\n",
        "#     textfile.write('{},{}\\n'.format(Id, pred))\n",
        "# textfile.close()"
      ],
      "metadata": {
        "id": "TU05zOFHRiuB"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_submission = pd.DataFrame({'': test_ori.index, 'pred': reverted_df.pred})\n",
        "# you could use any filename. We choose submission here\n",
        "my_submission.to_csv('submission.csv', index=False)"
      ],
      "metadata": {
        "id": "V4j3GSjXaAt_"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "XOuX9keU1IWU",
        "outputId": "e6fec5a3-258a-4b79-cd0b-a63eb18f59ff"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              pred\n",
              "0          0    25\n",
              "1          1    25\n",
              "2          2    25\n",
              "3          3    25\n",
              "4          4    25\n",
              "...      ...   ...\n",
              "10968  10968     8\n",
              "10969  10969    18\n",
              "10970  10970    18\n",
              "10971  10971    18\n",
              "10972  10972    18\n",
              "\n",
              "[10973 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7fbc1b77-692b-46f6-966e-8105ff29ba45\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10968</th>\n",
              "      <td>10968</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10969</th>\n",
              "      <td>10969</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10970</th>\n",
              "      <td>10970</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10971</th>\n",
              "      <td>10971</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10972</th>\n",
              "      <td>10972</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10973 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fbc1b77-692b-46f6-966e-8105ff29ba45')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \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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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7fbc1b77-692b-46f6-966e-8105ff29ba45 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7fbc1b77-692b-46f6-966e-8105ff29ba45');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('checkpoint.pth')\n",
        "# files.download('prediction.txt')\n",
        "files.download('submission.csv')"
      ],
      "metadata": {
        "id": "QUy9iHTtfn4h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ebcdb8a2-68d2-44f7-f28f-a824f8359db2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_151be059-e19f-4d0a-babe-0e715a360e46\", \"checkpoint.pth\", 498767341)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_91f2cedb-3970-4d2f-a3ee-df6756337b53\", \"submission.csv\", 84056)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}