FLanT5.ipynb

{
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        },
        "id": "aO1UiRu1HpOn",
        "outputId": "78f521a7-cab5-41d9-9288-2f32a6c5706d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m48.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m52.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.6/297.6 kB\u001b[0m \u001b[31m35.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m27.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m267.1/267.1 kB\u001b[0m \u001b[31m30.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
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        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)\n",
        "\n",
        "  # Install the latest version of torchtext library quietly without showing output\n",
        "!pip install torchtext -qq\n",
        "!pip install transformers evaluate wandb datasets accelerate -U -qq ## NEW LINES ##\n",
        "basepath = '/content/drive/MyDrive/NLP/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "XBmxUMlDHs52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab3cd62b-900f-49e9-c4bc-fd51c287cf5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "# wandb library\n",
        "import wandb\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "from tqdm import tqdm\n",
        "from sklearn import metrics\n",
        "import torch\n",
        "torch.cuda.empty_cache()\n",
        "from transformers import DistilBertTokenizer, DistilBertModel\n",
        "import logging\n",
        "logging.basicConfig(level=logging.ERROR)\n",
        "# Importing stock ml libraries\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from sklearn import metrics\n",
        "import transformers\n",
        "\n",
        "from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from transformers import DistilBertTokenizer, DistilBertModel\n",
        "import logging\n",
        "logging.basicConfig(level=logging.ERROR)\n",
        "import os, sys\n",
        "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score\n",
        "\n",
        "import transformers\n",
        "from transformers import DataCollatorForSeq2Seq, AutoTokenizer, RobertaForSequenceClassification\n",
        "from transformers import TrainingArguments, Trainer, set_seed\n",
        "from transformers import T5ForSequenceClassification, AutoTokenizer\n",
        "\n",
        "\n",
        "import datasets\n",
        "from datasets import Dataset, DatasetDict\n",
        "\n",
        "import evaluate\n",
        "\n",
        "\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "SO_979RuHtEK"
      },
      "outputs": [],
      "source": [
        "base_folder = Path(basepath)\n",
        "data_folder = base_folder/'Homework/Homework7'\n",
        "model_folder = base_folder/'models/nlp_spring_2024/ed/nn'\n",
        "custom_functions = base_folder/'custom-functions'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "nzhVxUqKHtG2"
      },
      "outputs": [],
      "source": [
        "sys.path.append('/content/drive/MyDrive/NLP/custom-functions')\n",
        "import CustomPreprocessorSpacy as cp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "nu_iASoKHtJQ"
      },
      "outputs": [],
      "source": [
        "# # Setting up the device for GPU usage\n",
        "\n",
        "from torch import cuda\n",
        "device = 'cuda' if cuda.is_available() else 'cpu'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Nb3ggW5PHtL2"
      },
      "outputs": [],
      "source": [
        "train_data = pd.read_csv(data_folder/ 'train.csv')\n",
        "test= pd.read_csv(data_folder/ 'test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        "id": "ZVC2PgOcHtOl",
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          "height": 206
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        "outputId": "671d5e97-7d19-4cf7-e0e6-000ddef60e95"
      },
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        {
          "output_type": "execute_result",
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              "                                               Tweet  anger  anticipation  \\\n",
              "0  “Worry is a down payment on a problem you may ...      0             1   \n",
              "1  Whatever you decide to do make sure it makes y...      0             0   \n",
              "2  @Max_Kellerman  it also helps that the majorit...      1             0   \n",
              "3  Accept the challenges so that you can literall...      0             0   \n",
              "4  My roommate: it's okay that we can't spell bec...      1             0   \n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_data",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 7724,\n  \"fields\": [\n    {\n      \"column\": \"Tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7724,\n        \"samples\": [\n          \"I'm due for a big change! I've prayed on it, I think I deserve it #positivity \",\n          \"incetown, age 23, joyful, elevated in hope with the\",\n          \"Never heard of #CaraCaraOranges until today. Very #tasty! I highly recommend them! #delicious #GoodForYou #fruits  #oranges\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anger\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anticipation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disgust\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"joy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"love\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"optimism\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pessimism\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sadness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surprise\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trust\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train_data.drop(columns=[\"ID\"],inplace=True)\n",
        "y_test = test.drop(['ID','Tweet'],axis=1)\n",
        "for column in y_test:\n",
        "    y_test[column] = y_test[column].apply(lambda x: 0 if x == \"NONE\" else 1)\n",
        "y_test = y_test.apply(lambda x: list(map(int, x)))\n",
        "test_data = pd.concat([test['Tweet'],y_test],axis=1)\n",
        "\n",
        "train_data.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "njeJaiYOHtQ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "59a3a4fd-f242-44c6-a96c-b3d35fd55d9d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Tweet  anger  anticipation  \\\n",
              "0  @Adnan__786__ @AsYouNotWish Dont worry Indian ...      0             0   \n",
              "1  Academy of Sciences, eschews the normally sobe...      0             0   \n",
              "2                  I blew that opportunity -__- #mad      0             0   \n",
              "3             This time in 2 weeks I will be 30... 😥      0             0   \n",
              "4  #Deppression is real. Partners w/ #depressed p...      0             0   \n",
              "\n",
              "   disgust  fear  joy  love  optimism  pessimism  sadness  surprise  trust  \n",
              "0        0     0    0     0         0          0        0         0      0  \n",
              "1        0     0    0     0         0          0        0         0      0  \n",
              "2        0     0    0     0         0          0        0         0      0  \n",
              "3        0     0    0     0         0          0        0         0      0  \n",
              "4        0     0    0     0         0          0        0         0      0  "
            ],
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              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "type": "dataframe",
              "variable_name": "test_data",
              "summary": "{\n  \"name\": \"test_data\",\n  \"rows\": 3259,\n  \"fields\": [\n    {\n      \"column\": \"Tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3259,\n        \"samples\": [\n          \"When you're on a diet and your whole family orders pizza. \",\n          \"@lukeWHC09 Hahaaa! Was fuming with that \\ud83d\\ude1e\\ud83d\\ude02\",\n          \"@CaxtonSupport I'm sorry but I don't know what DM stands for. I know a PM \\ud83d\\ude02. But sadly not a DM. It won't create an account to find.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anger\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anticipation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disgust\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"joy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"love\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"optimism\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pessimism\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sadness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surprise\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trust\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "yuOHltp4HtUh"
      },
      "outputs": [],
      "source": [
        "labels = [x for x in train_data.columns if x not in ['Tweet']]\n",
        "id2label = {idx:label for idx, label in enumerate(labels)}\n",
        "label2id = {label:idx for idx, label in enumerate(labels)}\n",
        "NUM_OF_LABELS = len(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "EeK1gvJnH5S2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "000f5d0c-ae9c-46a6-a74b-0d86aa59ebbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Dataset Shape: (6179, 12)\n",
            "Evaluation Dataset Shape: (1545, 12)\n",
            "Testing Dataset Shape (3259, 12)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Tweet': 'Food that gets delivered 😍🙌🏻 #cheering #happy',\n",
              " 'anger': 0,\n",
              " 'anticipation': 0,\n",
              " 'disgust': 0,\n",
              " 'fear': 0,\n",
              " 'joy': 1,\n",
              " 'love': 0,\n",
              " 'optimism': 1,\n",
              " 'pessimism': 0,\n",
              " 'sadness': 0,\n",
              " 'surprise': 1,\n",
              " 'trust': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "train_dataset = Dataset.from_pandas(train_data)\n",
        "test_dataset = Dataset.from_pandas(test_data)\n",
        "train_testEval = train_dataset.train_test_split(train_size=0.80)\n",
        "val_eval = train_testEval['test']\n",
        "\n",
        "ds = DatasetDict({\n",
        "    'train' : train_testEval['train'],\n",
        "    'val' : val_eval,\n",
        "    'test':test_dataset\n",
        "\n",
        "})\n",
        "\n",
        "print(\"Training Dataset Shape:\", ds['train'].shape)\n",
        "print(\"Evaluation Dataset Shape:\", ds['val'].shape)\n",
        "print(\"Testing Dataset Shape\",ds['test'].shape)\n",
        "ds['train'][25]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "We-Rz6rAH5PZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2822442c-ef7e-492b-844b-c26580508398"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This project will utilize the cpu to train and evaluate.\n",
            "This project will have a maximum length for the input text of 128 tokens.\n"
          ]
        }
      ],
      "source": [
        "set_seed = 42\n",
        "NUM_OF_EPOCHS = 10\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "LEARNING_RATE = 1e-4\n",
        "\n",
        "MAX_LENGTH = 128\n",
        "MODEL_NAME = \"t5-base\"\n",
        "\n",
        "METRIC_NAME = \"f1_macro\"\n",
        "DEVICE = torch.device(\"cpu\")\n",
        "print(f\"This project will utilize the {DEVICE} to train and evaluate.\")\n",
        "print(f\"This project will have a maximum length for the input text of {MAX_LENGTH} tokens.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "SXWlLaCpH5Mz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02c2bc6926ea4fed8c5a88e302d03c48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15536846a5214da1a56e538eb2013e9e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "\n",
        "def data_preprocessor(examples):\n",
        "\t# take a batch of text\n",
        "\ttext = examples[\"Tweet\"]\n",
        "\t# encode them\n",
        "\tencoded_data = tokenizer(text, padding=\"max_length\", truncation=True, max_length=MAX_LENGTH)\n",
        "\t# add labels\n",
        "\tlabels_batch = {k: examples[k] for k in examples.keys() if k in labels}\n",
        "\t# create numpy array of shape (batch_size, num_labels)\n",
        "\tlabels_matrix = np.zeros((len(text), len(labels)))\n",
        "\t# fill numpy array\n",
        "\tfor idx, label in enumerate(labels):\n",
        "\t\tlabels_matrix[:, idx] = labels_batch[label]\n",
        "\n",
        "\tencoded_data[\"labels\"] = labels_matrix.tolist()\n",
        "\n",
        "\treturn encoded_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "XATeETmvH5KL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        },
        "outputId": "39168190-2f48-49bd-964b-acfce8098cb6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6179 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21aa5b3328e84df983949165833c5b20"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1545 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da0e0f7565b74e31afd68e344f3d51c8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3259 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e775e3ccce04d2195d3205a58cf0773"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "encoded_ds = ds.map(data_preprocessor, batched=True, load_from_cache_file=False)\n",
        "encoded_ds.set_format(\"torch\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "A9MdcIb5H5H7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f27258d-b02d-4e3c-baef-74ff59155bd4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of the training dataset is: (6179, 15)\n",
            "The shape of the evaluation dataset is: (1545, 15)\n",
            "The shape of the test dataset is: (3259, 15)\n"
          ]
        }
      ],
      "source": [
        "print(f\"The shape of the training dataset is: {encoded_ds['train'].shape}\")\n",
        "\n",
        "print(f\"The shape of the evaluation dataset is: {encoded_ds['val'].shape}\")\n",
        "print(f\"The shape of the test dataset is: {encoded_ds['test'].shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "IiBngZKuH5FU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "5fee7ddd325c412a8c78c68237a5e255",
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        },
        "outputId": "d85db2e5-32ae-4f12-a27e-b22f1eb819a3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5fee7ddd325c412a8c78c68237a5e255"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of T5ForSequenceClassification were not initialized from the model checkpoint at t5-base and are newly initialized: ['classification_head.dense.bias', 'classification_head.dense.weight', 'classification_head.out_proj.bias', 'classification_head.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"
          ]
        }
      ],
      "source": [
        "model = (T5ForSequenceClassification.from_pretrained(MODEL_NAME,\n",
        "                                                             num_labels=NUM_OF_LABELS,\n",
        "                                                             problem_type=\"multi_label_classification\",\n",
        "                                                             id2label=id2label,\n",
        "                                                             label2id=label2id)\n",
        "         ).to(DEVICE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "I4GxELiWH5Cz"
      },
      "outputs": [],
      "source": [
        "def multi_label_metrics(predictions,\n",
        "                        labels,\n",
        "                        threshold=0.5):\n",
        "    '''\n",
        "    This function calculates & returns metrics\n",
        "    for a multilabel classification analysis.\n",
        "    '''\n",
        "\n",
        "    # first, apply sigmoid on predictions which are of shape (batch_size, num_labels)\n",
        "    sigmoid = torch.nn.Sigmoid()\n",
        "    probs = sigmoid(torch.Tensor(predictions))\n",
        "    # use threshold to turn them into integer predictions\n",
        "    y_pred = np.zeros(probs.shape)\n",
        "    y_pred[np.where(probs >= threshold)] = 1\n",
        "    # compute metrics\n",
        "    y_true = labels\n",
        "    f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro')\n",
        "    f1_macro_average = f1_score(y_true=y_true,y_pred=y_pred,average ='macro')\n",
        "\n",
        "    #roc_auc = roc_auc_score(y_true, y_pred, average = 'micro')\n",
        "    accuracy = accuracy_score(y_true, y_pred)\n",
        "    # return metrics as dictionary\n",
        "    metrics = {'f1_micro': f1_micro_average,\n",
        "               'f1_macro':f1_macro_average,\n",
        "               #'roc_auc': roc_auc,\n",
        "               'accuracy': accuracy}\n",
        "    return metrics\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "\n",
        "    preds = preds[0] if isinstance(preds,\n",
        "                    tuple) else preds\n",
        "    results = multi_label_metrics(\n",
        "                    predictions=preds,\n",
        "                    labels=labels)\n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "qXGnbv3BH5Ac"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    output_dir=model_folder,\n",
        "    evaluation_strategy='steps',  # Evaluate model at specified step intervals\n",
        "    eval_steps=50,  # Perform evaluation every 10 training steps\n",
        "    save_strategy=\"steps\",  # Save model checkpoint at specified step intervals\n",
        "    save_steps=100,\n",
        "    save_total_limit=1,# Save a model checkpoint every 10 training steps\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    lr_scheduler_type='linear',\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    optim='adamw_torch',\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=BATCH_SIZE,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=METRIC_NAME,\n",
        "    warmup_steps = 0,\n",
        "    greater_is_better=True,\n",
        "    weight_decay=0.01,\n",
        "    disable_tqdm=False,\n",
        "    logging_first_step=True,\n",
        "    report_to='wandb',  # Log metrics and results to Weights & Biases platform\n",
        "    run_name='Tweet_Emotion_FLANT5',\n",
        "    fp16=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "4qwITBI9H490"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "\n",
        "    train_dataset=encoded_ds[\"train\"],\n",
        "    eval_dataset=encoded_ds[\"val\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wandb login"
      ],
      "metadata": {
        "id": "q9EZpnInRHg8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fafdeedc-4330-4fbc-e9e4-e7565f75c5bc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "WFLkYh8WRHnv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e4a5c6d3-a10d-4837-ab6f-79d7f711d0d6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mrangareddy-bhargav\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.6"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240417_193415-szmujvud</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/rangareddy-bhargav/huggingface/runs/szmujvud' target=\"_blank\">Tweet_Emotion_FLANT5</a></strong> to <a href='https://wandb.ai/rangareddy-bhargav/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/rangareddy-bhargav/huggingface' target=\"_blank\">https://wandb.ai/rangareddy-bhargav/huggingface</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/rangareddy-bhargav/huggingface/runs/szmujvud' target=\"_blank\">https://wandb.ai/rangareddy-bhargav/huggingface/runs/szmujvud</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1940' max='1940' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1940/1940 33:35, Epoch 10/10]\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>F1 Micro</th>\n",
              "      <th>F1 Macro</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.474177</td>\n",
              "      <td>0.003249</td>\n",
              "      <td>0.001904</td>\n",
              "      <td>0.025243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.402735</td>\n",
              "      <td>0.449932</td>\n",
              "      <td>0.221011</td>\n",
              "      <td>0.148220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.363697</td>\n",
              "      <td>0.559313</td>\n",
              "      <td>0.301985</td>\n",
              "      <td>0.174757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.342519</td>\n",
              "      <td>0.622324</td>\n",
              "      <td>0.373472</td>\n",
              "      <td>0.208414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.324235</td>\n",
              "      <td>0.640100</td>\n",
              "      <td>0.443611</td>\n",
              "      <td>0.233657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.313199</td>\n",
              "      <td>0.663503</td>\n",
              "      <td>0.464431</td>\n",
              "      <td>0.260194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.304569</td>\n",
              "      <td>0.661712</td>\n",
              "      <td>0.470971</td>\n",
              "      <td>0.258900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.303732</td>\n",
              "      <td>0.675516</td>\n",
              "      <td>0.482677</td>\n",
              "      <td>0.248544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>0.686200</td>\n",
              "      <td>0.302731</td>\n",
              "      <td>0.675180</td>\n",
              "      <td>0.490028</td>\n",
              "      <td>0.258900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.300320</td>\n",
              "      <td>0.682687</td>\n",
              "      <td>0.503053</td>\n",
              "      <td>0.270550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.300648</td>\n",
              "      <td>0.674924</td>\n",
              "      <td>0.514208</td>\n",
              "      <td>0.261489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.296223</td>\n",
              "      <td>0.685504</td>\n",
              "      <td>0.528432</td>\n",
              "      <td>0.266019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.295148</td>\n",
              "      <td>0.685321</td>\n",
              "      <td>0.532905</td>\n",
              "      <td>0.271197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.301602</td>\n",
              "      <td>0.687230</td>\n",
              "      <td>0.519706</td>\n",
              "      <td>0.267961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.297812</td>\n",
              "      <td>0.686767</td>\n",
              "      <td>0.532765</td>\n",
              "      <td>0.270550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.299919</td>\n",
              "      <td>0.691427</td>\n",
              "      <td>0.539122</td>\n",
              "      <td>0.271197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.299548</td>\n",
              "      <td>0.694793</td>\n",
              "      <td>0.546230</td>\n",
              "      <td>0.276375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.299023</td>\n",
              "      <td>0.693063</td>\n",
              "      <td>0.544024</td>\n",
              "      <td>0.272492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>0.348700</td>\n",
              "      <td>0.300104</td>\n",
              "      <td>0.690786</td>\n",
              "      <td>0.545658</td>\n",
              "      <td>0.275728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.303763</td>\n",
              "      <td>0.696798</td>\n",
              "      <td>0.559257</td>\n",
              "      <td>0.273786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.306930</td>\n",
              "      <td>0.691619</td>\n",
              "      <td>0.541491</td>\n",
              "      <td>0.274434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.302612</td>\n",
              "      <td>0.693889</td>\n",
              "      <td>0.554722</td>\n",
              "      <td>0.271197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1150</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.307235</td>\n",
              "      <td>0.692128</td>\n",
              "      <td>0.543096</td>\n",
              "      <td>0.275081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.306032</td>\n",
              "      <td>0.695400</td>\n",
              "      <td>0.554438</td>\n",
              "      <td>0.269903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.305162</td>\n",
              "      <td>0.698851</td>\n",
              "      <td>0.561737</td>\n",
              "      <td>0.274434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.306882</td>\n",
              "      <td>0.697762</td>\n",
              "      <td>0.563502</td>\n",
              "      <td>0.269256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1350</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.305908</td>\n",
              "      <td>0.698376</td>\n",
              "      <td>0.570843</td>\n",
              "      <td>0.268608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.306601</td>\n",
              "      <td>0.695981</td>\n",
              "      <td>0.570350</td>\n",
              "      <td>0.275081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1450</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.308497</td>\n",
              "      <td>0.698335</td>\n",
              "      <td>0.566482</td>\n",
              "      <td>0.271197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.311190</td>\n",
              "      <td>0.702656</td>\n",
              "      <td>0.574505</td>\n",
              "      <td>0.271197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1550</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.308895</td>\n",
              "      <td>0.697040</td>\n",
              "      <td>0.565175</td>\n",
              "      <td>0.267314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.311890</td>\n",
              "      <td>0.699149</td>\n",
              "      <td>0.566748</td>\n",
              "      <td>0.269903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1650</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.312982</td>\n",
              "      <td>0.700588</td>\n",
              "      <td>0.569243</td>\n",
              "      <td>0.269256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.311310</td>\n",
              "      <td>0.699712</td>\n",
              "      <td>0.568341</td>\n",
              "      <td>0.273786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1750</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.312209</td>\n",
              "      <td>0.697332</td>\n",
              "      <td>0.567085</td>\n",
              "      <td>0.266019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.312261</td>\n",
              "      <td>0.700029</td>\n",
              "      <td>0.570426</td>\n",
              "      <td>0.272492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1850</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.312519</td>\n",
              "      <td>0.697789</td>\n",
              "      <td>0.568157</td>\n",
              "      <td>0.269903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.208100</td>\n",
              "      <td>0.313805</td>\n",
              "      <td>0.697272</td>\n",
              "      <td>0.565434</td>\n",
              "      <td>0.269256</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "There were missing keys in the checkpoint model loaded: ['transformer.encoder.embed_tokens.weight', 'transformer.decoder.embed_tokens.weight'].\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1940, training_loss=0.25013460129806675, metrics={'train_runtime': 2020.4157, 'train_samples_per_second': 30.583, 'train_steps_per_second': 0.96, 'total_flos': 9435304811896320.0, 'train_loss': 0.25013460129806675, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "id": "axAPqGpQRHp8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "f1b0565f-8ff3-4162-e3a3-a149a90b5ebd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='49' max='49' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [49/49 00:09]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.3111904263496399,\n",
              " 'eval_f1_micro': 0.702656383890317,\n",
              " 'eval_f1_macro': 0.5745047009285973,\n",
              " 'eval_accuracy': 0.27119741100323624,\n",
              " 'eval_runtime': 10.6147,\n",
              " 'eval_samples_per_second': 145.553,\n",
              " 'eval_steps_per_second': 4.616,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_output = trainer.predict(encoded_ds[\"val\"])"
      ],
      "metadata": {
        "id": "refWGzcrRHso",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "69f6802e-5a44-4605-b24b-dcbc349d6faa"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_output._fields"
      ],
      "metadata": {
        "id": "H1bE99pIRHvC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "676c2d8b-c1b3-43b8-de16-322542fd988b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('predictions', 'label_ids', 'metrics')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model_checkpoint_step = trainer.state.best_model_checkpoint.split('-')[-1]\n",
        "print(f\"The best model was saved at step {best_model_checkpoint_step}.\")"
      ],
      "metadata": {
        "id": "Avwnj4BTRHzt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a2cd982-c112-45dc-c8d5-65db4cf639a2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best model was saved at step 1500.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_folder"
      ],
      "metadata": {
        "id": "B0q4thkKRH2E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c038dd1a-ad39-4f1a-d0ee-a3d8a62a26f2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('/content/drive/MyDrive/NLP/models/nlp_spring_2024/ed/nn')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eVWwOt6qTTPp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = str(model_folder) + '/checkpoint-' + best_model_checkpoint_step"
      ],
      "metadata": {
        "id": "8C9-CasTRH5i"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_results = trainer.evaluate(encoded_ds[\"val\"])"
      ],
      "metadata": {
        "id": "QmHchOiYRWMt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "d93281fe-9575-484c-bc20-0d08a30991dd"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
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              "      [49/49 00:09]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_results"
      ],
      "metadata": {
        "id": "7NsUryDPRWJR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbb3354e-8217-4a54-891f-c79de28c6caa"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.3111904263496399,\n",
              " 'eval_f1_micro': 0.702656383890317,\n",
              " 'eval_f1_macro': 0.5745047009285973,\n",
              " 'eval_accuracy': 0.27119741100323624,\n",
              " 'eval_runtime': 10.3588,\n",
              " 'eval_samples_per_second': 149.149,\n",
              " 'eval_steps_per_second': 4.73,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = T5ForSequenceClassification.from_pretrained(model_checkpoint)\n",
        "model.to('cuda')\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "p8ddCM2RRWGz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42d01c1e-76d3-4b59-e9ae-edf94e795e63"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T5ForSequenceClassification(\n",
              "  (transformer): T5Model(\n",
              "    (shared): Embedding(32128, 768)\n",
              "    (encoder): T5Stack(\n",
              "      (embed_tokens): Embedding(32128, 768)\n",
              "      (block): ModuleList(\n",
              "        (0): T5Block(\n",
              "          (layer): ModuleList(\n",
              "            (0): T5LayerSelfAttention(\n",
              "              (SelfAttention): T5Attention(\n",
              "                (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (relative_attention_bias): Embedding(32, 12)\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (1): T5LayerFF(\n",
              "              (DenseReluDense): T5DenseActDense(\n",
              "                (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "                (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "                (act): ReLU()\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (1-11): 11 x T5Block(\n",
              "          (layer): ModuleList(\n",
              "            (0): T5LayerSelfAttention(\n",
              "              (SelfAttention): T5Attention(\n",
              "                (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (1): T5LayerFF(\n",
              "              (DenseReluDense): T5DenseActDense(\n",
              "                (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "                (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "                (act): ReLU()\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (final_layer_norm): T5LayerNorm()\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (decoder): T5Stack(\n",
              "      (embed_tokens): Embedding(32128, 768)\n",
              "      (block): ModuleList(\n",
              "        (0): T5Block(\n",
              "          (layer): ModuleList(\n",
              "            (0): T5LayerSelfAttention(\n",
              "              (SelfAttention): T5Attention(\n",
              "                (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (relative_attention_bias): Embedding(32, 12)\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (1): T5LayerCrossAttention(\n",
              "              (EncDecAttention): T5Attention(\n",
              "                (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (2): T5LayerFF(\n",
              "              (DenseReluDense): T5DenseActDense(\n",
              "                (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "                (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "                (act): ReLU()\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (1-11): 11 x T5Block(\n",
              "          (layer): ModuleList(\n",
              "            (0): T5LayerSelfAttention(\n",
              "              (SelfAttention): T5Attention(\n",
              "                (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (1): T5LayerCrossAttention(\n",
              "              (EncDecAttention): T5Attention(\n",
              "                (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "                (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (2): T5LayerFF(\n",
              "              (DenseReluDense): T5DenseActDense(\n",
              "                (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "                (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "                (act): ReLU()\n",
              "              )\n",
              "              (layer_norm): T5LayerNorm()\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (final_layer_norm): T5LayerNorm()\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "  )\n",
              "  (classification_head): T5ClassificationHead(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (dropout): Dropout(p=0.0, inplace=False)\n",
              "    (out_proj): Linear(in_features=768, out_features=11, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    per_device_eval_batch_size=8,\n",
        "    do_train=False,\n",
        "    do_eval=True,\n",
        "    report_to=[]\n",
        ")"
      ],
      "metadata": {
        "id": "juodHs0xRWEa"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    eval_dataset=encoded_ds['test'],\n",
        "    compute_metrics=compute_metrics,\n",
        "    tokenizer=tokenizer,\n",
        ")"
      ],
      "metadata": {
        "id": "X6rEfL25RWBm"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_ds['test']['labels']"
      ],
      "metadata": {
        "id": "Po5hC1bzRcp0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39ae4ef7-a52c-4470-f46f-947ae496cd81"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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.]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_results = trainer.evaluate()"
      ],
      "metadata": {
        "id": "pxdaz2LMRcmg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "f032b2f0-26a3-4545-aae2-ff1d9c76b526"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='408' max='408' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [408/408 00:29]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_results = trainer.predict(encoded_ds['test'])\n",
        "\n",
        "# The predictions object contains various fields including predictions\n",
        "logits = test_results.predictions[0]\n",
        "\n",
        "# Apply sigmoid to get the probabilities\n",
        "probs = torch.sigmoid(torch.Tensor(logits))\n",
        "\n",
        "# Apply a threshold to get the binary predictions\n",
        "threshold = 0.5\n",
        "y_pred = (probs >= threshold).int().numpy()"
      ],
      "metadata": {
        "id": "AmcClkETRckO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f91047f5-31dd-49af-df25-cdb5a2b1256a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_results.predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kd1vMpWgf_zj",
        "outputId": "91ae3523-658c-4c2e-9d3f-1c65915460f5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 1.0439453 , -3.0683594 ,  0.43774414, ..., -1.8896484 ,\n",
              "         -5.5664062 , -5.1015625 ],\n",
              "        [ 1.1445312 , -3.7539062 ,  1.53125   , ...,  0.38305664,\n",
              "         -5.1445312 , -6.6328125 ],\n",
              "        [ 5.2734375 , -4.9375    ,  3.6992188 , ...,  0.3803711 ,\n",
              "         -4.4921875 , -7.4414062 ],\n",
              "        ...,\n",
              "        [ 6.9023438 , -4.234375  ,  4.2539062 , ..., -2.0917969 ,\n",
              "         -5.2304688 , -6.1132812 ],\n",
              "        [-4.0976562 , -2.4511719 , -4.2265625 , ..., -4.859375  ,\n",
              "         -1.5996094 , -1.3115234 ],\n",
              "        [-1.3867188 , -2.5371094 , -1.1494141 , ..., -3.6328125 ,\n",
              "         -3.4960938 , -4.0429688 ]], dtype=float32),\n",
              " array([[[ 1.03530865e-02,  3.93101610e-02,  2.33311012e-01, ...,\n",
              "           5.25893629e-01, -1.01575479e-01,  2.71867514e-02],\n",
              "         [-5.22031963e-01,  1.27154544e-01,  5.30952632e-01, ...,\n",
              "           3.16550165e-01,  4.15139310e-02, -6.83843717e-02],\n",
              "         [-7.54108667e-01,  1.22005403e-01,  4.68024433e-01, ...,\n",
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              "         ...,\n",
              "         [-2.10125685e-01, -3.24707389e-01,  9.45712328e-02, ...,\n",
              "           4.52195197e-01,  4.38790649e-01, -4.10414487e-01],\n",
              "         [-1.31402284e-01, -2.26970270e-01,  3.17171998e-02, ...,\n",
              "           3.92749399e-01,  4.34595555e-01, -5.58561146e-01],\n",
              "         [-1.82688534e-01, -2.17151389e-01, -5.45662753e-02, ...,\n",
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              " \n",
              "        [[-1.23362787e-01, -2.73129321e-03,  6.63017854e-02, ...,\n",
              "          -5.90688400e-02,  5.98536730e-01,  4.68825161e-01],\n",
              "         [-3.07496548e-01, -8.14857036e-02,  1.74246579e-01, ...,\n",
              "           2.38174461e-02, -3.45096886e-01,  1.82761565e-01],\n",
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              "         [-3.99494022e-01,  1.21327251e-01, -4.09226939e-02, ...,\n",
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              " \n",
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              "           1.99750308e-02, -4.38777328e-01, -2.89042413e-01],\n",
              "         [-4.72515561e-02, -2.01306820e-01, -3.62557657e-02, ...,\n",
              "           2.93961931e-02, -5.13395607e-01, -1.01770781e-01],\n",
              "         [-1.40274316e-01,  2.02949941e-01, -3.43279005e-03, ...,\n",
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              "         ...,\n",
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              "           5.35213172e-01,  6.63137808e-02, -8.91594216e-02],\n",
              "         [-4.40146744e-01, -6.62109032e-02,  5.51094897e-02, ...,\n",
              "           5.35213172e-01,  6.63137808e-02, -8.91594216e-02],\n",
              "         [-4.40146744e-01, -6.62109032e-02,  5.51094897e-02, ...,\n",
              "           5.35213172e-01,  6.63137808e-02, -8.91594216e-02]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[-5.05175531e-01, -3.11781019e-02,  5.47318868e-02, ...,\n",
              "           1.71105146e-01, -3.27235043e-01, -8.25034976e-02],\n",
              "         [-1.93160638e-01, -3.39623153e-01, -3.03540498e-01, ...,\n",
              "          -8.60871468e-03, -2.04659104e-01, -2.64878809e-01],\n",
              "         [-2.52941370e-01, -4.57105115e-02,  1.21187381e-01, ...,\n",
              "           1.65577203e-01,  1.94382086e-01, -3.18288691e-02],\n",
              "         ...,\n",
              "         [-3.48933578e-01, -3.79934236e-02,  1.18431292e-01, ...,\n",
              "           2.61091143e-01, -6.50524944e-02, -1.25182375e-01],\n",
              "         [-3.48933578e-01, -3.79934236e-02,  1.18431292e-01, ...,\n",
              "           2.61091143e-01, -6.50524944e-02, -1.25182375e-01],\n",
              "         [-3.48933578e-01, -3.79934236e-02,  1.18431292e-01, ...,\n",
              "           2.61091143e-01, -6.50524944e-02, -1.25182375e-01]],\n",
              " \n",
              "        [[-9.05176327e-02, -1.49584785e-01, -6.96778819e-02, ...,\n",
              "           9.11602229e-02,  9.95238796e-02,  8.90100822e-02],\n",
              "         [ 1.09394632e-01, -9.30236280e-03,  1.60130426e-01, ...,\n",
              "           6.92477857e-04, -1.54564269e-02, -6.64677843e-02],\n",
              "         [-3.75999749e-01,  1.07352942e-01, -4.15586144e-01, ...,\n",
              "           2.66252697e-01, -2.29183555e-01, -2.38138229e-01],\n",
              "         ...,\n",
              "         [-8.06939125e-01, -7.47828335e-02, -1.37568951e-01, ...,\n",
              "          -1.76948994e-01,  4.07094240e-01,  2.38750167e-02],\n",
              "         [-8.06939125e-01, -7.47828335e-02, -1.37568951e-01, ...,\n",
              "          -1.76948994e-01,  4.07094240e-01,  2.38750167e-02],\n",
              "         [-8.06939125e-01, -7.47828335e-02, -1.37568951e-01, ...,\n",
              "          -1.76948994e-01,  4.07094240e-01,  2.38750167e-02]],\n",
              " \n",
              "        [[-5.92646264e-02,  9.31583717e-02,  6.42977059e-02, ...,\n",
              "          -1.57108337e-01, -4.28003035e-02, -1.18805524e-02],\n",
              "         [-1.03279039e-01, -1.98893681e-01,  2.56035596e-01, ...,\n",
              "           1.15888454e-01, -6.00893199e-02, -1.37706459e-01],\n",
              "         [-8.49112421e-02, -2.63702959e-01, -1.21862158e-01, ...,\n",
              "          -3.40883434e-02, -3.84667188e-01, -7.14968070e-02],\n",
              "         ...,\n",
              "         [-1.81760475e-01, -8.90166610e-02, -1.58955753e-01, ...,\n",
              "           1.41033918e-01, -4.49811183e-02, -2.32853368e-01],\n",
              "         [-1.81760475e-01, -8.90166610e-02, -1.58955753e-01, ...,\n",
              "           1.41033918e-01, -4.49811183e-02, -2.32853368e-01],\n",
              "         [-1.81760475e-01, -8.90166610e-02, -1.58955753e-01, ...,\n",
              "           1.41033918e-01, -4.49811183e-02, -2.32853368e-01]]],\n",
              "       dtype=float32))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_df = pd.DataFrame(y_pred,columns=labels)\n",
        "output_submission = pd.concat([test['ID'],y_pred_df],axis=  1)\n",
        "output_submission"
      ],
      "metadata": {
        "id": "_WL4i2otRchh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "bd6db78e-3e18-4077-f219-680b341283f8"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              ID  anger  anticipation  disgust  fear  joy  love  optimism  \\\n",
              "0     2018-01559      1             0        1     1    0     0         0   \n",
              "1     2018-03739      1             0        1     0    0     0         0   \n",
              "2     2018-00385      1             0        1     0    0     0         0   \n",
              "3     2018-03001      0             1        0     0    1     0         1   \n",
              "4     2018-01988      0             0        0     1    0     0         0   \n",
              "...          ...    ...           ...      ...   ...  ...   ...       ...   \n",
              "3254  2018-03848      1             0        1     0    0     0         0   \n",
              "3255  2018-00416      0             1        0     1    1     0         0   \n",
              "3256  2018-03717      1             0        1     0    0     0         0   \n",
              "3257  2018-03504      0             0        0     0    1     1         1   \n",
              "3258  2018-00115      0             0        0     0    1     0         0   \n",
              "\n",
              "      pessimism  sadness  surprise  trust  \n",
              "0             0        0         0      0  \n",
              "1             0        1         0      0  \n",
              "2             0        1         0      0  \n",
              "3             0        0         0      0  \n",
              "4             1        1         0      0  \n",
              "...         ...      ...       ...    ...  \n",
              "3254          0        0         0      0  \n",
              "3255          0        0         0      0  \n",
              "3256          0        0         0      0  \n",
              "3257          0        0         0      0  \n",
              "3258          0        0         0      0  \n",
              "\n",
              "[3259 rows x 12 columns]"
            ],
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>anger</th>\n",
              "      <th>anticipation</th>\n",
              "      <th>disgust</th>\n",
              "      <th>fear</th>\n",
              "      <th>joy</th>\n",
              "      <th>love</th>\n",
              "      <th>optimism</th>\n",
              "      <th>pessimism</th>\n",
              "      <th>sadness</th>\n",
              "      <th>surprise</th>\n",
              "      <th>trust</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01559</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-03739</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-00385</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-03001</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01988</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3254</th>\n",
              "      <td>2018-03848</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3255</th>\n",
              "      <td>2018-00416</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3256</th>\n",
              "      <td>2018-03717</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3257</th>\n",
              "      <td>2018-03504</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3258</th>\n",
              "      <td>2018-00115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3259 rows × 12 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  }\n",
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              "\n",
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              "    border-color: transparent;\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▄▅▆▇██▇████████████████████████████████</td></tr><tr><td>eval/f1_macro</td><td>▁▄▅▆▆▇▇▇▇▇▇▇▇▇▇█████████████████████████</td></tr><tr><td>eval/f1_micro</td><td>▁▅▇▇▇███████████████████████████████████</td></tr><tr><td>eval/loss</td><td>█▅▄▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▂▂▂▂▂▂▂▂▂▂</td></tr><tr><td>eval/runtime</td><td>█▄▄▂▆▁▅▂▄▁▄▁▅▂▅▁▄▂▅▁▆▂▅▂▅▂▅▂▇▂▇▃▅▂▅▃▅▂▅▃</td></tr><tr><td>eval/samples_per_second</td><td>▁▅▅▇▃█▄▇▄█▄█▄▇▄█▄▇▄█▃▇▃▇▄▇▄▇▂▇▂▆▄▇▄▆▄▇▃▅</td></tr><tr><td>eval/steps_per_second</td><td>▁▅▅▇▃█▄▇▄█▄█▄▇▄█▄▇▄█▃▇▃▇▄▇▄▇▂▇▂▆▄▇▄▆▄▇▃▅</td></tr><tr><td>train/epoch</td><td>▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇████</td></tr><tr><td>train/global_step</td><td>▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇████</td></tr><tr><td>train/grad_norm</td><td>█▂▁▃</td></tr><tr><td>train/learning_rate</td><td>█▆▃▁</td></tr><tr><td>train/loss</td><td>█▃▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.2712</td></tr><tr><td>eval/f1_macro</td><td>0.5745</td></tr><tr><td>eval/f1_micro</td><td>0.70266</td></tr><tr><td>eval/loss</td><td>0.31119</td></tr><tr><td>eval/runtime</td><td>10.3588</td></tr><tr><td>eval/samples_per_second</td><td>149.149</td></tr><tr><td>eval/steps_per_second</td><td>4.73</td></tr><tr><td>total_flos</td><td>9435304811896320.0</td></tr><tr><td>train/epoch</td><td>10.0</td></tr><tr><td>train/global_step</td><td>1940</td></tr><tr><td>train/grad_norm</td><td>0.61907</td></tr><tr><td>train/learning_rate</td><td>2e-05</td></tr><tr><td>train/loss</td><td>0.2081</td></tr><tr><td>train_loss</td><td>0.25013</td></tr><tr><td>train_runtime</td><td>2020.4157</td></tr><tr><td>train_samples_per_second</td><td>30.583</td></tr><tr><td>train_steps_per_second</td><td>0.96</td></tr></table><br/></div></div>"
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