Distilbert.ipynb

{
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        },
        "outputId": "1c8e1249-6a96-41d3-ef56-4f4eb3c673af"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.1 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[31m17.3 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[31m21.6 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[31m21.1 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[31m16.4 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[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m267.1/267.1 kB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\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": "7XLOL_AC0zev",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cc816b6-d578-4946-a4f8-cf3c1688b4fa"
      },
      "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",
        "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",
        "import torch\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, DistilBertForSequenceClassification\n",
        "from transformers import TrainingArguments, Trainer, set_seed\n",
        "\n",
        "import datasets\n",
        "from datasets import Dataset, DatasetDict\n",
        "\n",
        "import evaluate\n",
        "\n",
        "import torch\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "hikf0ccP0zhP"
      },
      "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": "Hzf5KxzG0zjy"
      },
      "outputs": [],
      "source": [
        "model_folder.mkdir(exist_ok=True, parents = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "i3siAe2V0zmH"
      },
      "outputs": [],
      "source": [
        "sys.path.append('/content/drive/MyDrive/NLP/custom-functions')\n",
        "import CustomPreprocessorSpacy as cp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FrVIOtzF0zq6"
      },
      "outputs": [],
      "source": [
        "# # Setting up the device for GPU usage\n",
        "\n",
        "from torch import cuda\n",
        "device = 'cuda' if cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "TTnl2rv50zoh"
      },
      "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": 8,
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        "id": "r4_HWJy_0zv_",
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          "height": 206
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        "outputId": "e293ee3e-2274-4adc-bd8f-2bbab9b14479"
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        {
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              "                                               Tweet  anger  anticipation  \\\n",
              "0  “Worry is a down payment on a problem you may ...      0             1   \n",
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              "2  @Max_Kellerman  it also helps that the majorit...      1             0   \n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    async function quickchart(key) {\n",
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              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "\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": 8
        }
      ],
      "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",
      "source": [
        "test_data.head()"
      ],
      "metadata": {
        "id": "v9KQRTH6it-w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c0a055b1-cecc-457a-f68d-9c940e5138d9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Tweet  anger  anticipation  \\\n",
              "0  @Adnan__786__ @AsYouNotWish Dont worry Indian ...      0             0   \n",
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              "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",
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              "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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              "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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ufnR4c-x0zyl"
      },
      "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": 11,
      "metadata": {
        "id": "G9VKVOp40z2B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0d8c6ef-4447-4d9d-d1e4-e93570c4f8e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "NUM_OF_LABELS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "TsfPbEDu3RD4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbe1d187-6839-4cbc-d4d0-a44c434dad5e"
      },
      "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': \"Yo Yo Yo,my name is #DarthVader \\\\nI feel like I need to puff on my inhaler (I'm no rapper but that was some sick bars)  #bars #rap\",\n",
              " 'anger': 0,\n",
              " 'anticipation': 0,\n",
              " 'disgust': 1,\n",
              " 'fear': 0,\n",
              " 'joy': 0,\n",
              " 'love': 0,\n",
              " 'optimism': 0,\n",
              " 'pessimism': 0,\n",
              " 'sadness': 1,\n",
              " 'surprise': 0,\n",
              " 'trust': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "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": 13,
      "metadata": {
        "id": "RWa4BwSK3RBm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "951307bc-ff1f-41b1-fe3a-1d1e6fc0576e"
      },
      "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 = 64\n",
        "LEARNING_RATE = 1e-4\n",
        "\n",
        "MAX_LENGTH = 128\n",
        "MODEL_CKPT = \"distilbert-base-uncased\"\n",
        "MODEL_NAME = MODEL_CKPT + \"-reviews_multilabel_clf_v2\"\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": 14,
      "metadata": {
        "id": "mrXXf-nA3Q_M",
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          "base_uri": "https://localhost:8080/",
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        "outputId": "beb3794e-707e-4677-d8ac-bfba05110444"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
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          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)\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": 15,
      "metadata": {
        "id": "mMaMzs2n3Q8v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        "outputId": "28b2a77b-fede-4217-8856-4a11a76a420f"
      },
      "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": "193b0c9a7c8a400899b0c703beec1567"
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          "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": "d821fbc6eea443759123960675437d03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3259 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "6ec6ecf54b28434baa26976fc88d5114"
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          },
          "metadata": {}
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      ],
      "source": [
        "encoded_ds = ds.map(data_preprocessor, batched=True, load_from_cache_file=False)\n",
        "encoded_ds.set_format(\"torch\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "THje9zhuq2uZ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "oJ7HYSwY3Q6F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70503d39-5fc6-4a13-8227-bc5110316448"
      },
      "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": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "c38dd3be04c346908e2dc0e99292af34",
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        "id": "Vs79U70b3Q3v",
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.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 = (DistilBertForSequenceClassification.from_pretrained(MODEL_CKPT,\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": 18,
      "metadata": {
        "id": "G8k2gQzZ3Q1T"
      },
      "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": 19,
      "metadata": {
        "id": "UYR0EroX3QzD"
      },
      "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=50,\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_DISTILLBERT',\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "TeMnrIMI3Qwr"
      },
      "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": "markdown",
      "source": [],
      "metadata": {
        "id": "m_Pc-pIQJJRV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpRo6JMO3QuJ",
        "outputId": "4130e41d-f554-4419-a442-ccad2616cb16"
      },
      "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"
          ]
        }
      ],
      "source": [
        "!wandb login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 812
        },
        "id": "JsjuK_zR5VMg",
        "outputId": "70a79a42-629d-40bd-b089-509f8269ca3f"
      },
      "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_202304-abywocvb</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/abywocvb' target=\"_blank\">Tweet_Emotion_DISTILLBERT</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/abywocvb' target=\"_blank\">https://wandb.ai/rangareddy-bhargav/huggingface/runs/abywocvb</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='970' max='970' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [970/970 13:52, 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>0.690400</td>\n",
              "      <td>0.390343</td>\n",
              "      <td>0.514936</td>\n",
              "      <td>0.244744</td>\n",
              "      <td>0.175405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.337866</td>\n",
              "      <td>0.625605</td>\n",
              "      <td>0.413177</td>\n",
              "      <td>0.242718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.329041</td>\n",
              "      <td>0.642626</td>\n",
              "      <td>0.446261</td>\n",
              "      <td>0.239482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.315598</td>\n",
              "      <td>0.659196</td>\n",
              "      <td>0.489595</td>\n",
              "      <td>0.258252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.323355</td>\n",
              "      <td>0.662012</td>\n",
              "      <td>0.497313</td>\n",
              "      <td>0.253074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.316783</td>\n",
              "      <td>0.662733</td>\n",
              "      <td>0.503276</td>\n",
              "      <td>0.260194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.334457</td>\n",
              "      <td>0.668275</td>\n",
              "      <td>0.524667</td>\n",
              "      <td>0.258900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.336059</td>\n",
              "      <td>0.665740</td>\n",
              "      <td>0.510043</td>\n",
              "      <td>0.256958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>0.690400</td>\n",
              "      <td>0.346668</td>\n",
              "      <td>0.665014</td>\n",
              "      <td>0.549949</td>\n",
              "      <td>0.238835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.359503</td>\n",
              "      <td>0.666369</td>\n",
              "      <td>0.529672</td>\n",
              "      <td>0.239482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.372179</td>\n",
              "      <td>0.662874</td>\n",
              "      <td>0.532867</td>\n",
              "      <td>0.226537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.389141</td>\n",
              "      <td>0.663197</td>\n",
              "      <td>0.532855</td>\n",
              "      <td>0.231068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.402620</td>\n",
              "      <td>0.660949</td>\n",
              "      <td>0.534665</td>\n",
              "      <td>0.223301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.410998</td>\n",
              "      <td>0.661381</td>\n",
              "      <td>0.546500</td>\n",
              "      <td>0.212298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.422428</td>\n",
              "      <td>0.661775</td>\n",
              "      <td>0.540199</td>\n",
              "      <td>0.227832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.436317</td>\n",
              "      <td>0.653875</td>\n",
              "      <td>0.526211</td>\n",
              "      <td>0.229773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.442640</td>\n",
              "      <td>0.659178</td>\n",
              "      <td>0.538251</td>\n",
              "      <td>0.227832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.447937</td>\n",
              "      <td>0.660352</td>\n",
              "      <td>0.538023</td>\n",
              "      <td>0.228479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>0.263700</td>\n",
              "      <td>0.449027</td>\n",
              "      <td>0.656163</td>\n",
              "      <td>0.538962</td>\n",
              "      <td>0.223948</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=970, training_loss=0.18184316410231835, metrics={'train_runtime': 840.4258, 'train_samples_per_second': 73.522, 'train_steps_per_second': 1.154, 'total_flos': 2046618574855680.0, 'train_loss': 0.18184316410231835, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "2aq1LurC5VJH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "307016d4-da17-42ca-f894-840b1f83b5d3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 00:05]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.3466684818267822,\n",
              " 'eval_f1_micro': 0.6650138544553013,\n",
              " 'eval_f1_macro': 0.5499492289906406,\n",
              " 'eval_accuracy': 0.23883495145631067,\n",
              " 'eval_runtime': 6.06,\n",
              " 'eval_samples_per_second': 254.949,\n",
              " 'eval_steps_per_second': 4.125,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "6r2DLJ465VGl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "76971f6a-3a7d-4e7a-f7c0-a67b7e2db25e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ],
      "source": [
        "valid_output = trainer.predict(encoded_ds[\"val\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_output._fields"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZFWBF02yu08D",
        "outputId": "b0eaaebd-838c-4cfd-c9de-70c623979afb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('predictions', 'label_ids', 'metrics')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "yhi4s9TA5VD-"
      },
      "outputs": [],
      "source": [
        "valid_preds = np.argmax(valid_output.predictions, axis=-1)\n",
        "valid_labels = np.array(valid_output.label_ids)\n"
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmU_Gb3V0aRg",
        "outputId": "f03c39dc-b79a-4ca5-fa53-9feba9d5f4fe"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best model was saved at step 450.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_folder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9YmHFlRBoTN3",
        "outputId": "95ad57b5-ed47-4224-a6b0-b252f7eca3f7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('/content/drive/MyDrive/NLP/models/nlp_spring_2024/ed/nn')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = str(model_folder) + '/checkpoint-' + best_model_checkpoint_step"
      ],
      "metadata": {
        "id": "zIG8480F-6BV"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "-7002dv35U8R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "7c5e9b62-31c4-4d00-b9aa-bf76ab019f13"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 00:05]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "valid_results = trainer.evaluate(encoded_ds[\"val\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "ld8vgFIp5U5p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfa98271-16d7-4b39-9fad-9efd09fb3bd0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.3466684818267822,\n",
              " 'eval_f1_micro': 0.6650138544553013,\n",
              " 'eval_f1_macro': 0.5499492289906406,\n",
              " 'eval_accuracy': 0.23883495145631067,\n",
              " 'eval_runtime': 6.0031,\n",
              " 'eval_samples_per_second': 257.367,\n",
              " 'eval_steps_per_second': 4.165,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "valid_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "z4Owu5rR5U3K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec606890-4a3b-4441-c4ac-5556318d303b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertForSequenceClassification(\n",
              "  (distilbert): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0-5): 6 x TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (classifier): Linear(in_features=768, out_features=11, bias=True)\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "model = DistilBertForSequenceClassification.from_pretrained(model_checkpoint)\n",
        "model.to('cuda')\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "MJuXaKMp5U0k"
      },
      "outputs": [],
      "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",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "1JU9ZTKc5Ux9"
      },
      "outputs": [],
      "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",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_ds['test']['labels']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lrHO7HVwQhB",
        "outputId": "d2838833-c807-4d8e-fdbe-c3f9f82217ae"
      },
      "execution_count": 35,
      "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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "IQN_xt4V5Uvf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "66ff412e-a664-4f98-afae-e0f4a46d9145"
      },
      "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 00:12]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_results = trainer.evaluate()"
      ]
    },
    {
      "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\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "BxkgYo171vUh",
        "outputId": "cdab5f20-0244-4203-e0b9-a1c077373a20"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4X66YUQi2GZg",
        "outputId": "e8f2c47b-7e7f-4415-b4b9-b5b0d425d050"
      },
      "execution_count": 38,
      "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             0        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    0     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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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>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>0</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>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>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",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\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>0.23883</td></tr><tr><td>eval/f1_macro</td><td>0.54995</td></tr><tr><td>eval/f1_micro</td><td>0.66501</td></tr><tr><td>eval/loss</td><td>0.34667</td></tr><tr><td>eval/runtime</td><td>6.0031</td></tr><tr><td>eval/samples_per_second</td><td>257.367</td></tr><tr><td>eval/steps_per_second</td><td>4.165</td></tr><tr><td>total_flos</td><td>2046618574855680.0</td></tr><tr><td>train/epoch</td><td>10.0</td></tr><tr><td>train/global_step</td><td>970</td></tr><tr><td>train/grad_norm</td><td>0.72455</td></tr><tr><td>train/learning_rate</td><td>5e-05</td></tr><tr><td>train/loss</td><td>0.2637</td></tr><tr><td>train_loss</td><td>0.18184</td></tr><tr><td>train_runtime</td><td>840.4258</td></tr><tr><td>train_samples_per_second</td><td>73.522</td></tr><tr><td>train_steps_per_second</td><td>1.154</td></tr></table><br/></div></div>"
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