COG403_Final_Roberta.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2Vys470zkK6",
        "outputId": "1ce5d869-7f0a-4df8-a662-c82b53f96d80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "Y2Vys470zkK6"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wEysoFQl5lM",
        "outputId": "d8cc9377-f06f-4149-e47e-d28162db1d31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.27.4\n",
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            "Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.11.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.8.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets"
      ],
      "id": "_wEysoFQl5lM"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "W-0X-GCMgvAI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np # linear algebra\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "id": "W-0X-GCMgvAI"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHUbzgKOkKYk"
      },
      "source": [
        "# Data Pre-Processing"
      ],
      "id": "bHUbzgKOkKYk"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J79tE56vkMlE"
      },
      "source": [
        "## Poem Data Split"
      ],
      "id": "J79tE56vkMlE"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "FSnQbex7gvAJ",
        "outputId": "10fecbfc-5e9a-4dfc-b40e-c7a3f97e9453"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  text  emotion\n",
              "0    A Tree\\nA tree beside the sandy river-beach \\n...  sadness\n",
              "1    Sri Krishna\\n\\nO immense Light and thou, O spi...     love\n",
              "3    Revelation\\n\\n\\nSomeone leaping from the rocks...  sadness\n",
              "4    The Silver Call\\n\\n\\nThere is a godhead of unr...      joy\n",
              "5    Surrender\\n\\nO THOU of whom I am the instrumen...     love\n",
              "..                                                 ...      ...\n",
              "711  Daughter Taken By Mothers Lies\\n\\nHave you any...  sadness\n",
              "712  Involuntary Acceptance\\n\\nEven though\\nWe’re f...  sadness\n",
              "713  Victim Of Poverty\\n\\nPoverty stricken youth ju...  sadness\n",
              "714  Rain\\n\\nI sit and watch\\nas the rain falls \\nf...  sadness\n",
              "715  The Face Of Sadness\\n\\nIts happened again.\\n\\n...  sadness\n",
              "\n",
              "[536 rows x 2 columns]"
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              "      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-ee11e6e4-e0a4-4420-a848-319aa315564b 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-ee11e6e4-e0a4-4420-a848-319aa315564b');\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": 4
        }
      ],
      "source": [
        "# poem_data = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/PERC_mendelly.xlsx')\n",
        "poem_data = pd.read_excel('./PERC_mendelly.xlsx')\n",
        "labels_to_drop = [\"peace\", \"courage\", \"hate\"]\n",
        "indices = poem_data[poem_data[\"Emotion\"].isin(labels_to_drop)].index\n",
        "poem_data[\"Emotion\"] = poem_data[\"Emotion\"].replace(\"sad\", \"sadness\")\n",
        "poem_data.drop(indices, inplace=True)\n",
        "# poem_data.to_csv(\"poem_cleaned.csv\")\n",
        "poem_data = poem_data.rename(columns={\"Poem\": 'text', \"Emotion\": 'emotion'})\n",
        "poem_data"
      ],
      "id": "FSnQbex7gvAJ"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "pF-bgPh3kIlL"
      },
      "outputs": [],
      "source": [
        "# Split the dataset into training and testing sets\n",
        "poem_train_data, poem_test_data = train_test_split(poem_data, test_size=0.9, random_state=42)\n",
        "\n",
        "# Split the training set into training and validation sets\n",
        "poem_test_data, poem_val_data = train_test_split(poem_test_data, test_size=0.5, random_state=42)"
      ],
      "id": "pF-bgPh3kIlL"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "GMa1xTOyDoXo"
      },
      "outputs": [],
      "source": [
        "poem_test_data.to_csv('poem_test.txt', index=False)"
      ],
      "id": "GMa1xTOyDoXo"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_cRW8yHkQoS"
      },
      "source": [
        "## Twitter Message Data Split"
      ],
      "id": "K_cRW8yHkQoS"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YnGCWTX_gvAK",
        "outputId": "e0c8c4d8-41fa-4004-9d46-e407ede939a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text  emotion\n",
              "0                                i didnt feel humiliated  sadness\n",
              "1      i can go from feeling so hopeless to so damned...  sadness\n",
              "2       im grabbing a minute to post i feel greedy wrong    anger\n",
              "3      i am ever feeling nostalgic about the fireplac...     love\n",
              "4                                   i am feeling grouchy    anger\n",
              "...                                                  ...      ...\n",
              "15995  i just had a very brief time in the beanbag an...  sadness\n",
              "15996  i am now turning and i feel pathetic that i am...  sadness\n",
              "15997                     i feel strong and good overall      joy\n",
              "15998  i feel like this was such a rude comment and i...    anger\n",
              "15999  i know a lot but i feel so stupid because i ca...  sadness\n",
              "\n",
              "[16000 rows x 2 columns]"
            ],
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>i didnt feel humiliated</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>i can go from feeling so hopeless to so damned...</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>im grabbing a minute to post i feel greedy wrong</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>i am ever feeling nostalgic about the fireplac...</td>\n",
              "      <td>love</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>i am feeling grouchy</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15995</th>\n",
              "      <td>i just had a very brief time in the beanbag an...</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15996</th>\n",
              "      <td>i am now turning and i feel pathetic that i am...</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15997</th>\n",
              "      <td>i feel strong and good overall</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15998</th>\n",
              "      <td>i feel like this was such a rude comment and i...</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15999</th>\n",
              "      <td>i know a lot but i feel so stupid because i ca...</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16000 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1546f4f-be63-4fba-b5a4-d34c2d4269e7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "      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-f1546f4f-be63-4fba-b5a4-d34c2d4269e7 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-f1546f4f-be63-4fba-b5a4-d34c2d4269e7');\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": 7
        }
      ],
      "source": [
        "# text_data_train = pd.read_table('/content/drive/MyDrive/Colab Notebooks/train.txt', sep=';', header=None)\n",
        "text_data_train = pd.read_table('./train.txt', sep=';', header=None)\n",
        "text_data_train = text_data_train.rename(columns={0: 'text', 1: 'emotion'})\n",
        "text_data_train.to_csv(\"train_cleaned.txt\")\n",
        "text_data_train"
      ],
      "id": "YnGCWTX_gvAK"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qB_lGyY_gvAK",
        "outputId": "e3630ccc-d3f0-4f73-d5fe-c26a91ba4769"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'anger', 'fear', 'joy', 'sadness', 'love', 'surprise'}\n"
          ]
        }
      ],
      "source": [
        "# text_data_test = pd.read_table('/content/drive/MyDrive/Colab Notebooks/test.txt', sep=';', header=None)\n",
        "text_data_test = pd.read_table('./test.txt', sep=';', header=None) \n",
        "text_data_test = text_data_test.rename(columns={0: 'text', 1: 'emotion'})\n",
        "text_data_test.to_csv('text_test.txt', index=False)\n",
        "print(set(text_data_train[\"emotion\"]))"
      ],
      "id": "qB_lGyY_gvAK"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ObshiwxtgvAK",
        "outputId": "01d57bdc-5687-440c-95f4-77f4e4aeb494",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'surprise', 'fear', 'joy', 'sadness', 'love', 'anger'}\n"
          ]
        }
      ],
      "source": [
        "# text_data_val = pd.read_table('/content/drive/MyDrive/Colab Notebooks/val.txt', sep=';', header=None)\n",
        "text_data_val = pd.read_table('./val.txt', sep=';', header=None)\n",
        "text_data_val = text_data_val.rename(columns={0: 'text', 1: 'emotion'})\n",
        "text_data_val.to_csv('text_val_cleaned.txt', index=False)\n",
        "print(set(text_data_val[\"emotion\"]))"
      ],
      "id": "ObshiwxtgvAK"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJw8TBswkW4b"
      },
      "source": [
        "## Reddit Data Split"
      ],
      "id": "JJw8TBswkW4b"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
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        "colab": {
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          "height": 858,
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            "45dbbda0c55b42be8b188a8b4b5c6208",
            "d5f228f43df34a3991a4032058853843",
            "fec3649184db453ca9c49cfc5c8f4bb0",
            "0d2f32a05e31458287f7044122e8b525",
            "1d53b21f95af40329a2afdc70a0685f2",
            "85ca28762dba4dc399f70a9281b800f4",
            "ddd54dfe84204d9897a49c322a26b886"
          ]
        },
        "id": "-hj7pF9XkaTS",
        "outputId": "c9f76f07-a99c-4ec3-a671-e20a872c7b96"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d425fa7eb30a4c1eaa942117b2c3522d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/7.03k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6dea118dda64ad3a13d85ae68d51e77"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/9.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d184240348af4208bd0e890b0b25f01e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset go_emotions/raw to /root/.cache/huggingface/datasets/go_emotions/raw/0.0.0/2637cfdd4e64d30249c3ed2150fa2b9d279766bfcd6a809b9f085c61a90d776d...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59b6be44fce24d70bf71f539ca78a17c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65a4c4cae59246a8af81b3c5fa3617b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "073ad33ad019471db3166c1481c58b66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c892c28b64d343bbbe9630d794dd81f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d80f3eaee784ee3b5074ef503f92717"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/211225 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c4cd3347f9e4f16a0fad1c77c80bbcb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset go_emotions downloaded and prepared to /root/.cache/huggingface/datasets/go_emotions/raw/0.0.0/2637cfdd4e64d30249c3ed2150fa2b9d279766bfcd6a809b9f085c61a90d776d. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e46f1ef81c9d4a899c6d0d2b328c8fc0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"go_emotions\", \"raw\")\n",
        "columns_to_remove = [\"id\", \"author\", \"subreddit\", \"link_id\", \"parent_id\", \"created_utc\", \"rater_id\", \"example_very_unclear\",\n",
        "                    'admiration', 'amusement', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'gratitude', 'grief', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'neutral']\n",
        "\n",
        "dataset_dict = dataset.remove_columns(columns_to_remove)\n",
        "\n",
        "# Convert dataset to pandas DataFrame\n",
        "df = pd.DataFrame(dataset_dict[\"train\"])\n",
        "\n",
        "# Count the number of 1s in each row\n",
        "counts = df.iloc[:, 1:].sum(axis=1)\n",
        "\n",
        "# Filter the rows where the count is less than or equal to 1\n",
        "reddit_data = df[counts == 1]"
      ],
      "id": "-hj7pF9XkaTS"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NC8mYOM6nu02",
        "outputId": "330f7c51-adad-4e90-a11c-f36887d23e2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                     text  anger  fear  joy  \\\n",
            "0                                         That game hurt.      0     0    0   \n",
            "3                                      Man I love reddit.      0     0    0   \n",
            "37      I just came home, what the fuck is this lineup...      0     0    0   \n",
            "43      By far the coolest thing I've seen on this thr...      0     0    1   \n",
            "49                     Sending love and strength vibes <3      0     0    1   \n",
            "...                                                   ...    ...   ...  ...   \n",
            "211187  I just called the Capitol Police. They are not...      1     0    0   \n",
            "211212    What a great photo and you two look so happy. 😍      0     0    1   \n",
            "211219  Well, I'm glad you're out of all that now. How...      0     0    1   \n",
            "211220                             Everyone likes [NAME].      0     0    0   \n",
            "211223  The FDA has plenty to criticize. But like here...      1     0    0   \n",
            "\n",
            "        love  sadness  surprise  \n",
            "0          0        1         0  \n",
            "3          1        0         0  \n",
            "37         1        0         0  \n",
            "43         0        0         0  \n",
            "49         0        0         0  \n",
            "...      ...      ...       ...  \n",
            "211187     0        0         0  \n",
            "211212     0        0         0  \n",
            "211219     0        0         0  \n",
            "211220     1        0         0  \n",
            "211223     0        0         0  \n",
            "\n",
            "[36822 rows x 7 columns]\n"
          ]
        }
      ],
      "source": [
        "print(reddit_data)"
      ],
      "id": "NC8mYOM6nu02"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "id": "jnF-LMXmmfPR",
        "outputId": "8e7ff781-2de8-462f-e79d-c336372a33fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text   emotion\n",
              "0      \"Oh, how DARE you discuss the disgustingly unh...     anger\n",
              "1      Press fucken charges the moment they use your ...     anger\n",
              "2                                    Shut up 15 year-old     anger\n",
              "3      I thought it was very good. The guy was one si...     anger\n",
              "4      Lol. Build your own or make some tweaks to the...     anger\n",
              "...                                                  ...       ...\n",
              "36817  > you're probably a little bit of a closeted n...  surprise\n",
              "36818  > you're probably a little bit of a closeted n...  surprise\n",
              "36819                                          OH YEAH!!  surprise\n",
              "36820  Yeh? It just changed on me last night after I ...  surprise\n",
              "36821                               Guess I hit a triple  surprise\n",
              "\n",
              "[36822 rows x 2 columns]"
            ],
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              "      <th>36819</th>\n",
              "      <td>OH YEAH!!</td>\n",
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              "      <td>Yeh? It just changed on me last night after I ...</td>\n",
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              "    <tr>\n",
              "      <th>36821</th>\n",
              "      <td>Guess I hit a triple</td>\n",
              "      <td>surprise</td>\n",
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              "</table>\n",
              "<p>36822 rows × 2 columns</p>\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Reshape the dataframe\n",
        "melted_df = pd.melt(reddit_data, id_vars=['text'], var_name='emotion')\n",
        "\n",
        "# Filter the rows where the value is 1\n",
        "filtered_df = melted_df[melted_df['value'] == 1]\n",
        "\n",
        "# Drop the 'value' column\n",
        "reddit_df = filtered_df.drop('value', axis=1)\n",
        "\n",
        "reddit_df = reddit_df.reset_index(drop=True)\n",
        "\n",
        "reddit_df"
      ],
      "id": "jnF-LMXmmfPR"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "btBep7agl1JJ"
      },
      "outputs": [],
      "source": [
        "# Split the dataset into training and testing sets\n",
        "reddit_train_data, reddit_test_data = train_test_split(reddit_df, test_size=0.9, random_state=42)\n",
        "\n",
        "# Split the training set into training and validation sets\n",
        "reddit_test_data, reddit_val_data = train_test_split(reddit_test_data, test_size=0.5, random_state=42)"
      ],
      "id": "btBep7agl1JJ"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "FWSj2xwbDhvT"
      },
      "outputs": [],
      "source": [
        "reddit_test_data.to_csv('reddit_test.txt', index=False)"
      ],
      "id": "FWSj2xwbDhvT"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5sj1DYmkeCX"
      },
      "source": [
        "## Combine Train and Val"
      ],
      "id": "P5sj1DYmkeCX"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "BMkbf90GozNt"
      },
      "outputs": [],
      "source": [
        "def combine_df(d1, d2, d3, filename):\n",
        "  # Concatenate the dataframes vertically\n",
        "  combined_df = pd.concat([d1, d2, d3], ignore_index=True)\n",
        "\n",
        "  # Save the result to a new file\n",
        "  combined_df.to_csv(filename, index=False)"
      ],
      "id": "BMkbf90GozNt"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "SZt38CFtpFf7"
      },
      "outputs": [],
      "source": [
        "combine_df(text_data_train, poem_train_data, reddit_train_data, \"final_train.txt\")"
      ],
      "id": "SZt38CFtpFf7"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "UIo7OAjcpk2s"
      },
      "outputs": [],
      "source": [
        "combine_df(text_data_val, poem_val_data, reddit_val_data, \"final_val.txt\")"
      ],
      "id": "UIo7OAjcpk2s"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQ3mRQbukazI"
      },
      "source": [
        "# Model - RoBERTa "
      ],
      "id": "tQ3mRQbukazI"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 814
        },
        "id": "tGxd4rIE9xPC",
        "outputId": "293a9fd9-16cf-4745-b569-e2c2ccb14877"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.27.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.10.7)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.3)\n",
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          ]
        }
      ],
      "source": [
        "!pip install -U transformers\n",
        "!pip install -U datasets\n",
        "\n",
        "import torch\n",
        "from transformers import RobertaTokenizer, RobertaForSequenceClassification, TrainingArguments, Trainer\n",
        "from torch.utils.data import Dataset\n",
        "from datasets import load_metric\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "id": "tGxd4rIE9xPC"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Dataset for Training or Testing"
      ],
      "metadata": {
        "id": "Mz47xYluFTqT"
      },
      "id": "Mz47xYluFTqT"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "i4MTGdXlviDr"
      },
      "outputs": [],
      "source": [
        "class EmotionDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: val[idx] for key, val in self.encodings.items()}\n",
        "        item['labels'] = self.labels[idx]\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "def get_ds(filename):\n",
        "  # Load the cleaned poem dataset\n",
        "  train_data = pd.read_csv(filename, delimiter=',')\n",
        "  train_texts = train_data[\"text\"].tolist()\n",
        "  train_labels = train_data[\"emotion\"].tolist()\n",
        "\n",
        "  # Tokenize the cleaned poem dataset\n",
        "  model_name = \"roberta-base\"\n",
        "  tokenizer = RobertaTokenizer.from_pretrained(model_name)\n",
        "  train_encodings = tokenizer(train_texts, truncation=True, padding=True, return_tensors=\"pt\")\n",
        "\n",
        "  # Encode the labels for the cleaned poem dataset\n",
        "  encoder = LabelBinarizer()\n",
        "  train_labels = encoder.fit_transform(train_labels)\n",
        "  train_tensor = torch.tensor(train_labels).argmax(-1)\n",
        "\n",
        "  # Create the dataset\n",
        "  train_ds = EmotionDataset(train_encodings, train_tensor)\n",
        "\n",
        "  label_to_emotion = {i: label for i, label in enumerate(encoder.classes_)}\n",
        "\n",
        "  return train_ds, label_to_emotion"
      ],
      "id": "i4MTGdXlviDr"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sonpA_PNv20C"
      },
      "source": [
        "## Test Model"
      ],
      "id": "sonpA_PNv20C"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "OJaaLN46Bjf2"
      },
      "outputs": [],
      "source": [
        "def test_model(filepath):\n",
        "  # Load the saved fine-tuned model and tokenizer\n",
        "  model_test = RobertaForSequenceClassification.from_pretrained(filepath)\n",
        "  tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
        "\n",
        "  # Define the compute_metrics function for calculating accuracy\n",
        "  def compute_metrics(pred):\n",
        "      labels = pred.label_ids\n",
        "      preds = pred.predictions.argmax(-1)\n",
        "      acc = accuracy_score(labels, preds)\n",
        "      return {\"accuracy\": acc}\n",
        "\n",
        "  # Create a Trainer object with the loaded model\n",
        "  test_model = Trainer(\n",
        "      model=model_test,\n",
        "      compute_metrics=compute_metrics,\n",
        "  )\n",
        "\n",
        "  # Evaluate the model on the test dataset\n",
        "  test_ds, label_to_emotion = get_ds(\"./text_test.txt\")\n",
        "  test_results = test_model.evaluate(test_ds)\n",
        "  print(\"Test accuracy:\", test_results[\"eval_accuracy\"])\n",
        "  get_label_wise(test_model, test_ds, label_to_emotion)\n",
        "\n",
        "  print(\" -----------------------------POEM DATASET--------------------------------------- \")\n",
        "  poem_test_ds, label_to_emotion = get_ds(\"./poem_test.txt\")\n",
        "  poem_test_results = test_model.evaluate(poem_test_ds)\n",
        "  print(\"Test accuracy:\", poem_test_results[\"eval_accuracy\"])\n",
        "  get_label_wise(test_model, poem_test_ds, label_to_emotion)\n",
        "\n",
        "  print(\" -----------------------------REDDIT DATASET--------------------------------------- \")\n",
        "  reddit_test_ds, label_to_emotion = get_ds(\"./reddit_test.txt\")\n",
        "  reddit_test_results = test_model.evaluate(reddit_test_ds)\n",
        "  print(\"Test accuracy:\", reddit_test_results[\"eval_accuracy\"])\n",
        "  get_label_wise(test_model, reddit_test_ds, label_to_emotion)"
      ],
      "id": "OJaaLN46Bjf2"
    },
    {
      "cell_type": "code",
      "source": [
        "def get_label_wise(trainer, dataset, label_to_emotion):\n",
        "  # # Perform predictions on the test dataset\n",
        "  predictions = trainer.predict(dataset)\n",
        "  \n",
        "  # Get the predicted class indices\n",
        "  predicted_class_indices = torch.argmax(torch.tensor(predictions.predictions), dim=-1)\n",
        "  unique_labels = set(label_to_emotion.values())\n",
        "  unique_labels = sorted(list(unique_labels))\n",
        "  # emotion_to_label = {emotion: label for label, emotion in label_to_emotion.items()}\n",
        "  print(unique_labels)\n",
        "\n",
        "  # Initialize dictionaries to store the true positive, false negative, and false positive counts for each label\n",
        "  true_positive_count = {label: 0 for label in unique_labels}\n",
        "  false_negative_count = {label: 0 for label in unique_labels}\n",
        "  false_positive_count = {label: 0 for label in unique_labels}\n",
        "\n",
        "  # # Calculate the true positive, false negative, and false positive counts for each label\n",
        "  for true_label_idx, predicted_label_idx in zip(dataset.labels, predicted_class_indices):\n",
        "    true_label = label_to_emotion[int(true_label_idx)]\n",
        "    predicted_label = label_to_emotion[int(predicted_label_idx)]\n",
        "    \n",
        "    if true_label == predicted_label:\n",
        "        true_positive_count[true_label] += 1\n",
        "    else:\n",
        "        false_negative_count[true_label] += 1\n",
        "        false_positive_count[predicted_label] += 1\n",
        "\n",
        "  # Calculate the true positive rate (sensitivity) and positive predictive value (precision) for each label\n",
        "  label_wise_ppv = {\n",
        "      label: (true_positive_count[label] / (true_positive_count[label] + false_negative_count[label])) * 100\n",
        "      for label in unique_labels\n",
        "  }\n",
        "\n",
        "  label_wise_tpr = {\n",
        "      label: (true_positive_count[label] / (true_positive_count[label] + false_positive_count[label])) * 100\n",
        "      for label in unique_labels if (true_positive_count[label] + false_positive_count[label]) > 0\n",
        "  }\n",
        "\n",
        "  for label in unique_labels:\n",
        "    if label not in label_wise_ppv:\n",
        "      label_wise_ppv[label] = 0\n",
        "\n",
        "  # Print the true positive rate (sensitivity) and positive predictive value (precision)\n",
        "  print(\"Label-wise true positive rate:\")\n",
        "  for label, tpr in label_wise_tpr.items():\n",
        "      print(f\"{label}: {tpr:.2f}%\")\n",
        "\n",
        "  print(\"\\nLabel-wise accuracy:\")\n",
        "  for label, ppv in label_wise_ppv.items():\n",
        "      print(f\"{label}: {ppv:.2f}%\")\n",
        "  \n",
        "  # Calculate and print the confusion matrix\n",
        "  true_labels = [label_to_emotion[int(label_idx)] for label_idx in dataset.labels]\n",
        "  predicted_labels = [label_to_emotion[int(label_idx)] for label_idx in predicted_class_indices]\n",
        "  cm = confusion_matrix(true_labels, predicted_labels, labels=list(unique_labels))\n",
        "  print(\"\\nConfusion matrix:\")\n",
        "  print(cm)"
      ],
      "metadata": {
        "id": "_1RRe09YFbnj"
      },
      "id": "_1RRe09YFbnj",
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Model"
      ],
      "metadata": {
        "id": "_BnAS7H4Fc0d"
      },
      "id": "_BnAS7H4Fc0d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jZsYu1gBgvAL"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = torch.argmax(torch.tensor(logits), dim=-1)\n",
        "    return {\"accuracy\": accuracy_score(labels, predictions)}\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    logging_dir=\"./logs\",\n",
        "    logging_steps=1000,\n",
        "    save_steps=1000,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    load_best_model_at_end=True,\n",
        ")"
      ],
      "id": "jZsYu1gBgvAL"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Only Twitter Message Dataset\n"
      ],
      "metadata": {
        "id": "H7akKFLrB5q7"
      },
      "id": "H7akKFLrB5q7"
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = get_ds(\"./train_cleaned.txt\")\n",
        "val_ds = get_ds(\"./text_val_cleaned.txt\")\n",
        "model1 = RobertaForSequenceClassification.from_pretrained(\"roberta-base\", num_labels=6)\n",
        "\n",
        "trainer1 = Trainer(\n",
        "    model=model1,\n",
        "    args=training_args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=val_ds,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer1.train()\n",
        "\n",
        "model1.save_pretrained('/content/drive/MyDrive/Models/roberta_model1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "kNW2qfPHB5Jq",
        "outputId": "b9dd9346-5f54-4170-915d-43cc273fbe2b"
      },
      "id": "kNW2qfPHB5Jq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.dense.bias', 'lm_head.bias', 'lm_head.decoder.weight', 'roberta.pooler.dense.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.bias']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3000' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3000/3000 13:51, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.501700</td>\n",
              "      <td>0.223983</td>\n",
              "      <td>0.935000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.183100</td>\n",
              "      <td>0.149710</td>\n",
              "      <td>0.938000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.121100</td>\n",
              "      <td>0.175350</td>\n",
              "      <td>0.939000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train Combined Dataset"
      ],
      "metadata": {
        "id": "WzW4_E4IBB-U"
      },
      "id": "WzW4_E4IBB-U"
    },
    {
      "cell_type": "code",
      "source": [
        "combined_train_ds = get_ds(\"./final_train.txt\")\n",
        "combined_val_ds = get_ds(\"./final_val.txt\")\n",
        "\n",
        "model2 = RobertaForSequenceClassification.from_pretrained(\"roberta-base\", num_labels=6)\n",
        "\n",
        "trainer2 = Trainer(\n",
        "    model=model2,\n",
        "    args=training_args,\n",
        "    train_dataset=combined_train_ds,\n",
        "    eval_dataset=combined_val_ds,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer2.train()\n",
        "\n",
        "model2.save_pretrained('/content/drive/MyDrive/Models/roberta_model2')"
      ],
      "metadata": {
        "id": "TOyZVa4nA4wG"
      },
      "id": "TOyZVa4nA4wG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCAbpoC7wCzQ"
      },
      "source": [
        "## Results"
      ],
      "id": "dCAbpoC7wCzQ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### When Trained only on Twitter Message Data"
      ],
      "metadata": {
        "id": "G9-QvyWWIAWg"
      },
      "id": "G9-QvyWWIAWg"
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fOeNOwDeJfTI",
        "outputId": "0b73976a-a91a-43df-bf65-8993b753f6f8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy: 0.93\n",
            "['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\n",
            "Label-wise true positive rate:\n",
            "anger: 92.14%\n",
            "fear: 88.41%\n",
            "joy: 97.70%\n",
            "love: 74.53%\n",
            "sadness: 96.42%\n",
            "surprise: 97.22%\n",
            "\n",
            "Label-wise accuracy:\n",
            "anger: 93.82%\n",
            "fear: 91.96%\n",
            "joy: 91.80%\n",
            "love: 99.37%\n",
            "sadness: 97.25%\n",
            "surprise: 53.03%\n",
            "\n",
            "Confusion matrix:\n",
            "[[258   5   3   0   9   0]\n",
            " [  9 206   0   0   9   0]\n",
            " [  3   0 638  52   1   1]\n",
            " [  0   0   1 158   0   0]\n",
            " [ 10   4   1   1 565   0]\n",
            " [  0  18  10   1   2  35]]\n",
            " -----------------------------POEM DATASET--------------------------------------- \n"
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            "Test accuracy: 0.27800829875518673\n",
            "['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\n",
            "Label-wise true positive rate:\n",
            "anger: 15.38%\n",
            "fear: 4.76%\n",
            "joy: 29.31%\n",
            "love: 39.34%\n",
            "sadness: 36.73%\n",
            "surprise: 40.00%\n",
            "\n",
            "Label-wise accuracy:\n",
            "anger: 16.67%\n",
            "fear: 20.00%\n",
            "joy: 32.08%\n",
            "love: 36.92%\n",
            "sadness: 22.50%\n",
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            "\n",
            "Confusion matrix:\n",
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            " -----------------------------REDDIT DATASET--------------------------------------- \n"
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            "Test accuracy: 0.4644538322269161\n",
            "['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\n",
            "Label-wise true positive rate:\n",
            "anger: 41.15%\n",
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            "joy: 43.91%\n",
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            "sadness: 54.54%\n",
            "surprise: 60.56%\n",
            "\n",
            "Label-wise accuracy:\n",
            "anger: 66.45%\n",
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            "joy: 62.09%\n",
            "love: 32.05%\n",
            "sadness: 41.43%\n",
            "surprise: 18.54%\n",
            "\n",
            "Confusion matrix:\n",
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            "Test accuracy: 0.93\n",
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            "Label-wise true positive rate:\n",
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            "joy: 94.98%\n",
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            "sadness: 95.39%\n",
            "surprise: 94.87%\n",
            "\n",
            "Label-wise accuracy:\n",
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            "fear: 95.54%\n",
            "joy: 95.25%\n",
            "love: 84.28%\n",
            "sadness: 96.21%\n",
            "surprise: 56.06%\n",
            "\n",
            "Confusion matrix:\n",
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            " -----------------------------POEM DATASET--------------------------------------- \n"
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            "Label-wise true positive rate:\n",
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            "joy: 48.57%\n",
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            "surprise: 50.00%\n",
            "\n",
            "Label-wise accuracy:\n",
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            "surprise: 44.44%\n",
            "\n",
            "Confusion matrix:\n",
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            " -----------------------------REDDIT DATASET--------------------------------------- \n"
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            "Label-wise true positive rate:\n",
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            "joy: 75.20%\n",
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            "\n",
            "Label-wise accuracy:\n",
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            "surprise: 65.58%\n",
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