Qwen1_5_Peft_QLora.ipynb

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        "outputId": "0a54104d-7cd5-40e2-fe60-b7b4e6750ee5"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "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",
        "\n",
        "\n",
        "!pip install transformers evaluate wandb datasets accelerate peft bitsandbytes -U -qq ## NEW LINES ##\n",
        "basepath = '/content/drive/MyDrive/NLP/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTaVA4xEkGca",
        "outputId": "0294d791-fb5c-490b-ebd8-2c9884041561"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer\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,AutoModelForSequenceClassification\n",
        "from transformers import TrainingArguments, Trainer, set_seed\n",
        "from transformers import T5ForSequenceClassification, AutoTokenizer\n",
        "from transformers import AutoConfig\n",
        "\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\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": "J49k7sZ7kGZ8"
      },
      "outputs": [],
      "source": [
        "from peft import (\n",
        "    TaskType,\n",
        "    LoraConfig,\n",
        "    prepare_model_for_kbit_training,\n",
        "    get_peft_model,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Prs3GQ2bkGXN"
      },
      "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": 5,
      "metadata": {
        "id": "-pBZBtOvkGUZ"
      },
      "outputs": [],
      "source": [
        "sys.path.append('/content/drive/MyDrive/NLP/custom-functions')\n",
        "import CustomPreprocessorSpacy as cp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
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        },
        "id": "jGFSbfUzkGRV",
        "outputId": "bcfa647d-983a-4089-82e2-ae6fe587fbdf"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "801ee88fb81e463d9eb378f8a80ef0de",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "FutCNhNIkGOo"
      },
      "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": 8,
      "metadata": {
        "id": "pPwHSkiskGLs"
      },
      "outputs": [],
      "source": [
        "train_data = pd.read_csv(data_folder/ 'train.csv')\n",
        "test= pd.read_csv(data_folder/ 'test.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlcBZ45uOJ9O"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 451
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        "id": "KUktG-07kGI4",
        "outputId": "6e5fe02f-e8eb-4d2b-e12c-9e771b5baecc"
      },
      "outputs": [
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      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",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      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",
              "      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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-49adf82c-a595-43e9-99cb-7493ad46d126 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                               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",
              "\n",
              "   disgust  fear  joy  love  optimism  pessimism  sadness  surprise  trust  \n",
              "0        0     0    0     0         1          0        0         0      1  \n",
              "1        0     0    1     1         1          0        0         0      0  \n",
              "2        1     0    1     0         1          0        0         0      0  \n",
              "3        0     0    1     0         1          0        0         0      0  \n",
              "4        1     0    0     0         0          0        0         0      0  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "RYaSfo_HkGF8",
        "outputId": "18c6a48a-c140-4d50-f31e-961a524beba2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "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}",
              "type": "dataframe",
              "variable_name": "test_data"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-3913af36-bb59-44fb-a8ee-92a2c00183de\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tweet</th>\n",
              "      <th>anger</th>\n",
              "      <th>anticipation</th>\n",
              "      <th>disgust</th>\n",
              "      <th>fear</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>@Adnan__786__ @AsYouNotWish Dont worry Indian ...</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>Academy of Sciences, eschews the normally sobe...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I blew that opportunity -__- #mad</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>This time in 2 weeks I will be 30... 😥</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3913af36-bb59-44fb-a8ee-92a2c00183de')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3913af36-bb59-44fb-a8ee-92a2c00183de button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3913af36-bb59-44fb-a8ee-92a2c00183de');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-334dc950-54b7-4042-b64e-97b20e6a2bf9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-334dc950-54b7-4042-b64e-97b20e6a2bf9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "avpk7BdOkGDV"
      },
      "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",
        "\n",
        "label2id = {label:idx for idx, label in enumerate(labels)}\n",
        "NUM_OF_LABELS = len(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tfPAuNM3kGAn",
        "outputId": "30a5b30c-bcf9-4f91-f7db-e1ed4cadc01f"
      },
      "outputs": [
        {
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              "\n",
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            ],
            "text/plain": [
              "                                                text  \\\n",
              "0  “Worry is a down payment on a problem you may ...   \n",
              "1  Whatever you decide to do make sure it makes y...   \n",
              "2  @Max_Kellerman  it also helps that the majorit...   \n",
              "3  Accept the challenges so that you can literall...   \n",
              "4  My roommate: it's okay that we can't spell bec...   \n",
              "\n",
              "                               label  \n",
              "0  [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1]  \n",
              "1  [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0]  \n",
              "2  [1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0]  \n",
              "3  [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0]  \n",
              "4  [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels = ['anger', 'anticipation', 'disgust', 'fear', 'joy', 'love', 'optimism', 'pessimism', 'sadness', 'surprise', 'trust']\n",
        "\n",
        "# Creating a new DataFrame with the 'tweet' column followed by the emotions columns as a list\n",
        "train_df = pd.DataFrame({'text': train_data['Tweet'], 'label': train_data[labels].values.tolist()})\n",
        "test_df = pd.DataFrame({'text': test_data['Tweet'], 'label': test_data[labels].values.tolist()})\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7LSLhggkF-A",
        "outputId": "16ad0746-bab1-4bf5-9d28-e11d01f83ca5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Shape: (6179, 2)\n",
            "Evaluation Dataset Shape: (1545, 2)\n",
            "Testing Dataset Shape (3259, 2)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'text': 'If you be #angry, which may be the case, &amp; which may be unavoidable, see that the sudden excitement does not become sin. #AlbertBarnes',\n",
              " 'label': [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]}"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_dataset = Dataset.from_pandas(train_df)\n",
        "test_dataset = Dataset.from_pandas(test_df)\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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r05XF-xSkF7N",
        "outputId": "de7eefa0-1991-437d-c6c0-688bea7ee5be"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "checkpoint = \"Alibaba-NLP/gte-Qwen1.5-7B-instruct\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "2TEld7SBkF4R"
      },
      "outputs": [],
      "source": [
        "def tokenize_fn(batch):\n",
        "    return tokenizer(text = batch[\"text\"], truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
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            "9f839112f1044a3f8df9b645d3ec294e",
            "cabac82cd8264c46aa195a716e3e2a49",
            "cbae94c99cf94b3bb96e46d3f2578c80",
            "43d8f24bb8df4b2396455dd0478d62f1",
            "2aaa50ced38f4f3bb7a4a3a88d9703be",
            "ea4f2080f3404c228646c5deff0a37c8",
            "5a81adb98d4a4c5aaa67049c256297da",
            "99f499a1fc8d48cd97b156932c4f19ea",
            "43d43a636b1645aabd1ef7cf7a5e12e9",
            "941e629fece4477c8931675020a14d7f",
            "5805f52dac994f69ac4021d68016a7e2",
            "d01b001aaded4e72870ea68c7e075c57",
            "acaaa8d827244607a57412ca2e11fe45"
          ]
        },
        "id": "00Op-ttNkF1V",
        "outputId": "ccae013d-ff15-44f9-a7ad-f9e94ba11a91"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "07ca36786fb64ecdb596d18567fa199d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/6179 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26196fc21e17485d97d4ae8d6b970ffb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1545 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cbae94c99cf94b3bb96e46d3f2578c80",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/3259 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenized_dataset= ds.map(tokenize_fn, batched=True)\n",
        "tokenized_dataset = tokenized_dataset.remove_columns(\n",
        "    ['text']\n",
        ")\n",
        "# tokenized_dataset.set_format(type='torch')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-tgRt9hkFzK",
        "outputId": "2e21f566-a517-4d78-eb15-2c7d2ffb01d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 6179\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 1545\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 3259\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "70KGTTXnkFwe"
      },
      "outputs": [],
      "source": [
        "accuracy_metric = evaluate.load('accuracy', 'multilabel')\n",
        "f1 = evaluate.load('f1','multilabel')\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "\n",
        "    #logits= logits[0]\n",
        "\n",
        "    # print(logits.shape)\n",
        "    preds = (logits > 0).astype(int)\n",
        "\n",
        "    accuracy = accuracy_metric.compute(predictions=preds, references=labels)\n",
        "    f1_micro = f1.compute(predictions=preds, references=labels, average='micro')\n",
        "    f1_macro = f1.compute(predictions=preds, references=labels, average='macro')\n",
        "    return {'f1_micro':f1_micro['f1'],\n",
        "            'f1_macro':f1_macro['f1'],\n",
        "            'accuracy':accuracy['accuracy'],\n",
        "            }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "zZ5jNQrukFtv"
      },
      "outputs": [],
      "source": [
        "# Define the directory where model checkpoints will be saved\n",
        "run_name = \"emotions_Qwen1.5_qlora_im\"\n",
        "base_folder = Path(basepath)\n",
        "model_folder = base_folder / \"models\"/run_name\n",
        "# Create the directory if it doesn't exist\n",
        "model_folder.mkdir(exist_ok=True, parents=True)\n",
        "\n",
        "# Configure training parameters\n",
        "training_args = TrainingArguments(\n",
        "    # Training-specific configurations\n",
        "    num_train_epochs=10,  # Total number of training epochs\n",
        "    # Number of samples per training batch for each device\n",
        "    per_device_train_batch_size=64,\n",
        "    per_device_eval_batch_size=64,\n",
        "    # gradient_accumulation_steps=8,\n",
        "\n",
        "    weight_decay=0.001,  # Apply L2 regularization to prevent overfitting\n",
        "    learning_rate=5e-6,  # Step size for the optimizer during training\n",
        "    lr_scheduler_type='linear',\n",
        "    warmup_steps=0,  # Number of warmup steps for the learning rate scheduler\n",
        "    optim='adamw_torch',  # Optimizer,\n",
        "    max_grad_norm = 1.0,\n",
        "\n",
        "    # Checkpoint saving and model evaluation settings\n",
        "    output_dir=str(model_folder),  # Directory to save model checkpoints\n",
        "    evaluation_strategy='steps',  # Evaluate model at specified step intervals\n",
        "    eval_steps=20,  # Perform evaluation every 10 training steps\n",
        "    save_strategy=\"steps\",  # Save model checkpoint at specified step intervals\n",
        "    save_steps=20,  # Save a model checkpoint every 10 training steps\n",
        "    load_best_model_at_end=True,  # Reload the best model at the end of training\n",
        "    save_total_limit=1,  # Retain only the best and the most recent model checkpoints\n",
        "    # Use 'accuracy' as the metric to determine the best model\n",
        "    metric_for_best_model=\"eval_f1_macro\",\n",
        "    greater_is_better=True,  # A model is 'better' if its accuracy is higher\n",
        "\n",
        "\n",
        "    # Experiment logging configurations (commented out in this example)\n",
        "    logging_strategy='steps',\n",
        "    logging_steps=200,\n",
        "    report_to='wandb',  # Log metrics and results to Weights & Biases platform\n",
        "    run_name=run_name,  # Experiment name for Weights & Biases\n",
        "\n",
        "    fp16=True,\n",
        "    #bf16=True\n",
        "    # tf32= False\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "3414YvOaPMoh"
      },
      "outputs": [],
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "  load_in_4bit=True,\n",
        "  llm_int8_skip_modules = ['score'],\n",
        "  bnb_4bit_quant_type=\"nf4\",\n",
        "  bnb_4bit_use_double_quant=True,\n",
        "  bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590,
          "referenced_widgets": [
            "2db816fe4eda424195e06e58d8d92c8d",
            "dd677699901d49d6b817f96dcecddbf2",
            "3baa0b420aea4a68887a43e1ade8c441",
            "9a3e2c45018b4075a2cf85367001b602",
            "92a0b839ce934f5caabb60160156fc29",
            "28fe85d121c647eeb6445bf870fd32bb",
            "8371df46aa0c4fc58429411271b66879",
            "ff986b74d2554388917a8d09cfab5608",
            "ddedb0b3d7d44bb59fc54fca13e51c93",
            "1d35073878474cfebaff216212344501",
            "d895af8be95a4934bcb47e0146029bad"
          ]
        },
        "id": "0mfGkbeJkFrB",
        "outputId": "8eaef576-7aa5-4dc8-980a-a1f9c5cde057"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2db816fe4eda424195e06e58d8d92c8d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of Qwen2ForSequenceClassification were not initialized from the model checkpoint at Alibaba-NLP/gte-Qwen1.5-7B-instruct and are newly initialized: ['score.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Qwen2ForSequenceClassification(\n",
              "  (model): Qwen2Model(\n",
              "    (embed_tokens): Embedding(151936, 4096)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x Qwen2DecoderLayer(\n",
              "        (self_attn): Qwen2SdpaAttention(\n",
              "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=True)\n",
              "          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=True)\n",
              "          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=True)\n",
              "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): Qwen2RotaryEmbedding()\n",
              "        )\n",
              "        (mlp): Qwen2MLP(\n",
              "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): Qwen2RMSNorm()\n",
              "        (post_attention_layernorm): Qwen2RMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): Qwen2RMSNorm()\n",
              "  )\n",
              "  (score): Linear(in_features=4096, out_features=11, bias=False)\n",
              ")"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint,\n",
        "                                                           num_labels=11,\n",
        "                                                           problem_type=\"multi_label_classification\",\n",
        "                                                           quantization_config=bnb_config)\n",
        "\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "\n",
        "config = AutoConfig.from_pretrained(checkpoint)\n",
        "id2label= {id_: label_ for id_, label_ in enumerate(labels)}\n",
        "label2id = {label_: id_ for id_, label_ in enumerate(labels)}\n",
        "config.id2label = id2label\n",
        "config.label2id = label2id\n",
        "model.config = config\n",
        "model.config.pad_token_id = tokenizer.pad_token_id\n",
        "model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mw5pDh13LFFr",
        "outputId": "8e8fca76-c3c7-4b9b-c51d-2790aaa84102"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Qwen2Config {\n",
              "  \"_name_or_path\": \"Alibaba-NLP/gte-Qwen1.5-7B-instruct\",\n",
              "  \"architectures\": [\n",
              "    \"Qwen2ForCausalLM\"\n",
              "  ],\n",
              "  \"attention_dropout\": 0.0,\n",
              "  \"auto_map\": {\n",
              "    \"AutoModel\": \"Alibaba-NLP/gte-Qwen1.5-7B-instruct--modeling_qwen.Qwen2Model\",\n",
              "    \"AutoModelForCausalLM\": \"Alibaba-NLP/gte-Qwen1.5-7B-instruct--modeling_qwen.Qwen2ForCausalLM\",\n",
              "    \"AutoModelForSequenceClassification\": \"Alibaba-NLP/gte-Qwen1.5-7B-instruct--modeling_qwen.Qwen2ForSequenceClassification\"\n",
              "  },\n",
              "  \"bos_token_id\": 151643,\n",
              "  \"eos_token_id\": 151643,\n",
              "  \"hidden_act\": \"silu\",\n",
              "  \"hidden_size\": 4096,\n",
              "  \"id2label\": {\n",
              "    \"0\": \"anger\",\n",
              "    \"1\": \"anticipation\",\n",
              "    \"2\": \"disgust\",\n",
              "    \"3\": \"fear\",\n",
              "    \"4\": \"joy\",\n",
              "    \"5\": \"love\",\n",
              "    \"6\": \"optimism\",\n",
              "    \"7\": \"pessimism\",\n",
              "    \"8\": \"sadness\",\n",
              "    \"9\": \"surprise\",\n",
              "    \"10\": \"trust\"\n",
              "  },\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 11008,\n",
              "  \"label2id\": {\n",
              "    \"anger\": 0,\n",
              "    \"anticipation\": 1,\n",
              "    \"disgust\": 2,\n",
              "    \"fear\": 3,\n",
              "    \"joy\": 4,\n",
              "    \"love\": 5,\n",
              "    \"optimism\": 6,\n",
              "    \"pessimism\": 7,\n",
              "    \"sadness\": 8,\n",
              "    \"surprise\": 9,\n",
              "    \"trust\": 10\n",
              "  },\n",
              "  \"max_position_embeddings\": 32768,\n",
              "  \"max_window_layers\": 28,\n",
              "  \"model_type\": \"qwen2\",\n",
              "  \"num_attention_heads\": 32,\n",
              "  \"num_hidden_layers\": 32,\n",
              "  \"num_key_value_heads\": 32,\n",
              "  \"pad_token_id\": 151643,\n",
              "  \"rms_norm_eps\": 1e-06,\n",
              "  \"rope_theta\": 1000000.0,\n",
              "  \"sliding_window\": 32768,\n",
              "  \"tie_word_embeddings\": false,\n",
              "  \"torch_dtype\": \"float32\",\n",
              "  \"transformers_version\": \"4.40.1\",\n",
              "  \"use_cache\": true,\n",
              "  \"use_sliding_window\": false,\n",
              "  \"vocab_size\": 151936\n",
              "}"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95o_JBLhkFl0",
        "outputId": "e0905404-027e-44df-e597-d935811c12ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['up_proj',\n",
              " 'o_proj',\n",
              " 'k_proj',\n",
              " 'gate_proj',\n",
              " 'down_proj',\n",
              " 'v_proj',\n",
              " 'q_proj',\n",
              " 'score']"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import re\n",
        "model_modules = str(model.modules)\n",
        "pattern = r'\\((\\w+)\\): Linear'\n",
        "linear_layer_names = re.findall(pattern, model_modules)\n",
        "\n",
        "names = []\n",
        "# Print the names of the Linear layers\n",
        "for name in linear_layer_names:\n",
        "    names.append(name)\n",
        "target_modules = list(set(names))\n",
        "target_modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NE23TS_5kFjU",
        "outputId": "2cbd5d7c-3e3e-4f0f-a50b-2b389b431063"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 250,294,272 || all params: 7,348,940,800 || trainable%: 3.405855058731729\n"
          ]
        }
      ],
      "source": [
        "gemma_peft_config = LoraConfig(\n",
        "    task_type=TaskType.SEQ_CLS,\n",
        "    r=100,\n",
        "    lora_alpha=200,\n",
        "    lora_dropout=0.01,\n",
        "    bias=\"lora_only\",\n",
        "    modules_to_save = ['score'],\n",
        "    target_modules = ['v_proj','up_proj','k_proj','o_proj','q_proj','score','down_proj','gate_proj']\n",
        ")\n",
        "gemma_peft_model = get_peft_model(model, gemma_peft_config )\n",
        "gemma_peft_model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "kV9KrLLAcXtz"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZ1KtFlzkFhC",
        "outputId": "3ddc4bc5-9548-474f-ca19-38831f02143b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'down_proj',\n",
              " 'gate_proj',\n",
              " 'k_proj',\n",
              " 'o_proj',\n",
              " 'q_proj',\n",
              " 'score',\n",
              " 'up_proj',\n",
              " 'v_proj'}"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gemma_peft_config.target_modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhLnfwKCkFec",
        "outputId": "63b204c3-8c94-4e6c-f50c-8259d9b90b39"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeftModelForSequenceClassification(\n",
              "  (base_model): LoraModel(\n",
              "    (model): Qwen2ForSequenceClassification(\n",
              "      (model): Qwen2Model(\n",
              "        (embed_tokens): Embedding(151936, 4096)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x Qwen2DecoderLayer(\n",
              "            (self_attn): Qwen2SdpaAttention(\n",
              "              (q_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=True)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=True)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=True)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (rotary_emb): Qwen2RotaryEmbedding()\n",
              "            )\n",
              "            (mlp): Qwen2MLP(\n",
              "              (gate_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=11008, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (up_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=11008, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (down_proj): lora.Linear4bit(\n",
              "                (base_layer): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=11008, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (act_fn): SiLU()\n",
              "            )\n",
              "            (input_layernorm): Qwen2RMSNorm()\n",
              "            (post_attention_layernorm): Qwen2RMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): Qwen2RMSNorm()\n",
              "      )\n",
              "      (score): ModulesToSaveWrapper(\n",
              "        (original_module): Linear(in_features=4096, out_features=11, bias=False)\n",
              "        (modules_to_save): ModuleDict(\n",
              "          (default): Linear(in_features=4096, out_features=11, bias=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gemma_peft_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "XkByqHnCkFb1"
      },
      "outputs": [],
      "source": [
        "def calculate_pos_weights(dataset):\n",
        "    # Initialize counters for all labels\n",
        "    num_labels = len(dataset['train']['label'][0])\n",
        "    total_positives = [0] * num_labels\n",
        "    total_negatives = [0] * num_labels\n",
        "\n",
        "    # Count positives and negatives for each label\n",
        "    for label_array in dataset['train']['label']:\n",
        "        for i, label in enumerate(label_array):\n",
        "            if label == 1:\n",
        "                total_positives[i] += 1\n",
        "            else:\n",
        "                total_negatives[i] += 1\n",
        "\n",
        "    # Calculate pos_weight for each label\n",
        "    pos_weight = [total_negatives[i] / max(total_positives[i], 1) for i in range(num_labels)]\n",
        "    return torch.tensor(pos_weight)\n",
        "\n",
        "# Calculate the pos_weight using the training set\n",
        "pos_weights = calculate_pos_weights(ds)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rz4LLf33nmqf",
        "outputId": "8aed669c-009a-45cd-d557-5b2ee92028bd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([ 1.7077,  6.0216,  1.6338,  4.7372,  1.6588,  8.3621,  2.3636,  7.5228,\n",
              "         2.3876, 18.4921, 18.4308])"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pos_weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "FQoyjKbHkFZO"
      },
      "outputs": [],
      "source": [
        "pos_weights= torch.tensor([2., 3., 2., 2., 2., 3., 2., 3., 2., 4., 4.])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "IHzmDip_kFWw"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "class CustomTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.pop(\"labels\").float()  # Ensure labels are float for BCE loss\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.get(\"logits\")\n",
        "\n",
        "        device = next(model.parameters()).device\n",
        "\n",
        "        loss_fct = nn.BCEWithLogitsLoss(pos_weight=pos_weights.to(device))\n",
        "        loss = loss_fct(logits, labels)\n",
        "\n",
        "        return (loss, outputs) if return_outputs else loss\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "GvBxgYMokFUI"
      },
      "outputs": [],
      "source": [
        "trainer = CustomTrainer(\n",
        "    model=gemma_peft_model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"val\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ma4FQIN2kFRa",
        "outputId": "57ea3364-7782-481e-de95-896b445ba053"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "wandb.login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nYKKQ4PPkFOz",
        "outputId": "b2030304-de95-48c7-efc0-3afa4ce452bd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.16.6"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240504_010300-7l5bjut0</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/rangareddy-bhargav/huggingface/runs/7l5bjut0' target=\"_blank\">emotions_Qwen1.5_qlora_im</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/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/rangareddy-bhargav/huggingface' target=\"_blank\">https://wandb.ai/rangareddy-bhargav/huggingface</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/rangareddy-bhargav/huggingface/runs/7l5bjut0' target=\"_blank\">https://wandb.ai/rangareddy-bhargav/huggingface/runs/7l5bjut0</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='970' max='970' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [970/970 2:43:54, 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>20</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.773256</td>\n",
              "      <td>0.341897</td>\n",
              "      <td>0.270600</td>\n",
              "      <td>0.005178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.186590</td>\n",
              "      <td>0.380881</td>\n",
              "      <td>0.293129</td>\n",
              "      <td>0.009709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.860986</td>\n",
              "      <td>0.430044</td>\n",
              "      <td>0.317386</td>\n",
              "      <td>0.029773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.722414</td>\n",
              "      <td>0.515557</td>\n",
              "      <td>0.358865</td>\n",
              "      <td>0.067314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.639634</td>\n",
              "      <td>0.578762</td>\n",
              "      <td>0.442441</td>\n",
              "      <td>0.099676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.602684</td>\n",
              "      <td>0.611167</td>\n",
              "      <td>0.487430</td>\n",
              "      <td>0.109385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.580673</td>\n",
              "      <td>0.626300</td>\n",
              "      <td>0.496714</td>\n",
              "      <td>0.115858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.556731</td>\n",
              "      <td>0.650192</td>\n",
              "      <td>0.518934</td>\n",
              "      <td>0.177994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.547822</td>\n",
              "      <td>0.651991</td>\n",
              "      <td>0.532823</td>\n",
              "      <td>0.175405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.531694</td>\n",
              "      <td>0.665898</td>\n",
              "      <td>0.535702</td>\n",
              "      <td>0.182524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.522448</td>\n",
              "      <td>0.673079</td>\n",
              "      <td>0.562802</td>\n",
              "      <td>0.201942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.512553</td>\n",
              "      <td>0.674828</td>\n",
              "      <td>0.570083</td>\n",
              "      <td>0.190291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.508601</td>\n",
              "      <td>0.673526</td>\n",
              "      <td>0.568639</td>\n",
              "      <td>0.183172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.506306</td>\n",
              "      <td>0.675606</td>\n",
              "      <td>0.584258</td>\n",
              "      <td>0.162460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.496201</td>\n",
              "      <td>0.687268</td>\n",
              "      <td>0.588240</td>\n",
              "      <td>0.213592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.501054</td>\n",
              "      <td>0.686738</td>\n",
              "      <td>0.591479</td>\n",
              "      <td>0.223948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.498371</td>\n",
              "      <td>0.674345</td>\n",
              "      <td>0.585257</td>\n",
              "      <td>0.172816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.495883</td>\n",
              "      <td>0.688788</td>\n",
              "      <td>0.598210</td>\n",
              "      <td>0.209709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>380</td>\n",
              "      <td>0.962600</td>\n",
              "      <td>0.500722</td>\n",
              "      <td>0.684437</td>\n",
              "      <td>0.595787</td>\n",
              "      <td>0.215534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.496466</td>\n",
              "      <td>0.698417</td>\n",
              "      <td>0.599640</td>\n",
              "      <td>0.239482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>420</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.500189</td>\n",
              "      <td>0.680545</td>\n",
              "      <td>0.603100</td>\n",
              "      <td>0.184466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>440</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.502149</td>\n",
              "      <td>0.685968</td>\n",
              "      <td>0.596945</td>\n",
              "      <td>0.226537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>460</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.499695</td>\n",
              "      <td>0.693501</td>\n",
              "      <td>0.604344</td>\n",
              "      <td>0.229773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>480</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.500598</td>\n",
              "      <td>0.690716</td>\n",
              "      <td>0.597827</td>\n",
              "      <td>0.222006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.519195</td>\n",
              "      <td>0.683987</td>\n",
              "      <td>0.595462</td>\n",
              "      <td>0.235599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>520</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.521341</td>\n",
              "      <td>0.683967</td>\n",
              "      <td>0.594324</td>\n",
              "      <td>0.230421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>540</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.528244</td>\n",
              "      <td>0.675967</td>\n",
              "      <td>0.586049</td>\n",
              "      <td>0.212945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>560</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.527720</td>\n",
              "      <td>0.685065</td>\n",
              "      <td>0.593212</td>\n",
              "      <td>0.234304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>580</td>\n",
              "      <td>0.453200</td>\n",
              "      <td>0.522937</td>\n",
              "      <td>0.681487</td>\n",
              "      <td>0.591400</td>\n",
              "      <td>0.207767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.549571</td>\n",
              "      <td>0.675054</td>\n",
              "      <td>0.589529</td>\n",
              "      <td>0.207120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>620</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.562904</td>\n",
              "      <td>0.677644</td>\n",
              "      <td>0.592379</td>\n",
              "      <td>0.218123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.565449</td>\n",
              "      <td>0.676595</td>\n",
              "      <td>0.587645</td>\n",
              "      <td>0.220065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>660</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.579202</td>\n",
              "      <td>0.679396</td>\n",
              "      <td>0.585199</td>\n",
              "      <td>0.223301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>680</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.569983</td>\n",
              "      <td>0.676183</td>\n",
              "      <td>0.583126</td>\n",
              "      <td>0.207120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.623119</td>\n",
              "      <td>0.669359</td>\n",
              "      <td>0.576957</td>\n",
              "      <td>0.211003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>720</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.622258</td>\n",
              "      <td>0.675174</td>\n",
              "      <td>0.583240</td>\n",
              "      <td>0.218770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>740</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.631566</td>\n",
              "      <td>0.670051</td>\n",
              "      <td>0.578568</td>\n",
              "      <td>0.212298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>760</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.642529</td>\n",
              "      <td>0.674915</td>\n",
              "      <td>0.578604</td>\n",
              "      <td>0.221359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>780</td>\n",
              "      <td>0.358500</td>\n",
              "      <td>0.664906</td>\n",
              "      <td>0.675163</td>\n",
              "      <td>0.575918</td>\n",
              "      <td>0.226537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.699830</td>\n",
              "      <td>0.673296</td>\n",
              "      <td>0.573724</td>\n",
              "      <td>0.229126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>820</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.702914</td>\n",
              "      <td>0.669829</td>\n",
              "      <td>0.573360</td>\n",
              "      <td>0.217476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>840</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.717222</td>\n",
              "      <td>0.670826</td>\n",
              "      <td>0.570882</td>\n",
              "      <td>0.219417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>860</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.722703</td>\n",
              "      <td>0.668172</td>\n",
              "      <td>0.570557</td>\n",
              "      <td>0.223301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>880</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.734700</td>\n",
              "      <td>0.670114</td>\n",
              "      <td>0.571622</td>\n",
              "      <td>0.231068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.757795</td>\n",
              "      <td>0.666755</td>\n",
              "      <td>0.567234</td>\n",
              "      <td>0.227832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>920</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.771229</td>\n",
              "      <td>0.665769</td>\n",
              "      <td>0.562603</td>\n",
              "      <td>0.224595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>940</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.778079</td>\n",
              "      <td>0.666488</td>\n",
              "      <td>0.563165</td>\n",
              "      <td>0.225890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>960</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.781401</td>\n",
              "      <td>0.668539</td>\n",
              "      <td>0.567301</td>\n",
              "      <td>0.229126</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=970, training_loss=0.44429533457018666, metrics={'train_runtime': 9843.922, 'train_samples_per_second': 6.277, 'train_steps_per_second': 0.099, 'total_flos': 1.2104404552153498e+17, 'train_loss': 0.44429533457018666, 'epoch': 10.0})"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()  # start training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "id": "zCCOBMAAkFMM",
        "outputId": "a0860b8a-5e66-4ef1-abe6-410d707a0c82"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 00:50]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.5000859498977661,\n",
              " 'eval_f1_micro': 0.6933333333333334,\n",
              " 'eval_f1_macro': 0.6042017181890457,\n",
              " 'eval_accuracy': 0.2297734627831715,\n",
              " 'eval_runtime': 53.0259,\n",
              " 'eval_samples_per_second': 29.137,\n",
              " 'eval_steps_per_second': 0.471,\n",
              " 'epoch': 10.0}"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-CHMFsNkFEL",
        "outputId": "2453576c-cd39-4ef6-9b40-b8f4157fbbb9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The best model was saved at step 460.\n"
          ]
        }
      ],
      "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}.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tGZwDZUkFBq",
        "outputId": "ea882439-c81b-4863-d711-3d70f4cdbd43"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PosixPath('/content/drive/MyDrive/NLP/models/emotions_Qwen1.5_qlora_im')"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_folder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "m8ia9UdxkE_E"
      },
      "outputs": [],
      "source": [
        "model_checkpoint = str(model_folder) + '/checkpoint-' + best_model_checkpoint_step"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "pyV1oQCrkE8B",
        "outputId": "30008027-6608-4c46-8570-80d5460e5572"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='50' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 01:43]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "valid_results = trainer.evaluate(tokenized_dataset[\"val\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWlYdYSnkE5h",
        "outputId": "c272f479-048e-47ae-b8c5-b6f8f757bbe4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.5000859498977661,\n",
              " 'eval_f1_micro': 0.6933333333333334,\n",
              " 'eval_f1_macro': 0.6042017181890457,\n",
              " 'eval_accuracy': 0.2297734627831715,\n",
              " 'eval_runtime': 53.2391,\n",
              " 'eval_samples_per_second': 29.02,\n",
              " 'eval_steps_per_second': 0.47,\n",
              " 'epoch': 10.0}"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valid_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "IdVTwbm8we96"
      },
      "outputs": [],
      "source": [
        "gemma_peft_model.save_pretrained('/content/drive/MyDrive/NLP/testing')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "be3d043e418f4ecd835b65b74ae2c264",
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            "d3af90a141fc4561b130a5549590853d",
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            "d3076146f77b4cf798bb96c092126677",
            "1635c4255e0a44ed82fdacda93c40b62",
            "a453b4c82a4b46a3b581bfb24140bab4",
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            "414a68d1309d4cbf8b58dc196baed386",
            "e1d143cffcee48fdaafddadfb8ca2767",
            "5c1f84dd1ae04199ba0b6054a7f86154",
            "628d12b9e3e54812b332d93105c2e912",
            "2e90cec1469b4718a24d48d62449558c",
            "7f6ea2c57da9435a87339443918b9e4f",
            "785c24f536f44c36a9336ffaaad52673",
            "5ccd377c480d40b297c01b39d667ecaf",
            "b1dbc9c7538f472ca79ec66ad880bb51",
            "48436b2f3de34592ae48c2babadda16e",
            "ae7288ff80334a3fa46f82985a0927a9",
            "82722e0d9cad46069fa25ed0c79e1a22",
            "10679199697b49f6a92eccfaaa4c8b25",
            "5253a55fbe5b43fc9eaf4aab9c90a0a7",
            "9ea86ba018c04d43862a584e9bb73e30",
            "97ebe952c8e94d8db9382d6606ec94d9",
            "b4b0a9de3ab347d787fb4fd21aadd6ad",
            "0459a16d40df4fbea4e5dd8d5f98c99e",
            "59be81aa0c4d4ce3a1fec000c8758ecc",
            "e62c646e5d0646a389c14e0806770592",
            "20a8df7e41354b739546be8f407994a8",
            "1666094aeba4467c8b20230b8a83bb5c",
            "8cdbbe66f25e4addb6665ffe2dd5dad4",
            "0e158f8698f548e98bfa530907514f48",
            "26d566bba9c543b7bd06482ef082a134",
            "f9aec64e63a44759a7161a79ccf562aa",
            "68fdb7717972429da371b0b5e817410e",
            "65a34a8c7d834406ab309870de131b98",
            "e682b70fc5b4432ba4ca07b7cfe09aac",
            "dc76d4b033c74a6ebd8bc587676c0b9f",
            "c1d4384b2adf4bfd9ca2e534eb10b339",
            "6e60ea7a20bb464088771d661e47732a",
            "837513d8a8974ca5be67293d81c59cf3",
            "75e50b9883b6443bbd3923eec12cdf20",
            "8ccd7c75fc3d41b9b3b9bf9f0d1cfbb7",
            "4bbeeecce43146dc9f0e0368bf3f09ad"
          ]
        },
        "id": "yF7Lywd8kE20",
        "outputId": "1f6f5ced-1877-452b-b52f-2c0175122f45"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be3d043e418f4ecd835b65b74ae2c264",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155,
          "referenced_widgets": [
            "c2a7c7afc83f44599904ba6c10b4c188",
            "865036d69210436e811e502f920edd3d",
            "98e6975f13504ea0be2f54ed63661ba6",
            "e6a6cf58f44949c7a5e77447ad3e4cb8",
            "77f8a5d203074decb56bfa746f6b8d28",
            "8b69432871374912963130e76c241dbd",
            "f25ea02580f6424991bbc4d411b33b3b",
            "e95eff5476334262a91aa258f366edb6",
            "4ad85b63fe794f47bf70a2d3409d8705",
            "2ae5835ba2e94a9caa60ba269b7a0fe3",
            "c703e43ec7584729a4a3e99a239a5674",
            "6ad03379a01045209900d9adf237af6f",
            "a4a267ec32a14f2cb22cff424e2d6dc2",
            "0ce0047f358a420581593ab0f47fc774",
            "d7ae8704c88e420e8d01a324dbd59ad7",
            "d09cd6cb98854303811b9e6eb8fb8ae9",
            "3fea53eaa36541bb8b9d78b3e5872d3f",
            "dc83938b63e949cbb378572c3d265d03",
            "00aa65bae9bc4966acfdd84bd6b774c5",
            "f349224906e7415db0915202046df49a",
            "de293e2ad5dc4d3a96057d7f1388ab02",
            "f10f1d86e490473b817acb525e126aea"
          ]
        },
        "id": "Uy-cN0xuwbno",
        "outputId": "75d58ff0-9845-40c9-c569-8094ab651a76"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2a7c7afc83f44599904ba6c10b4c188",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ad03379a01045209900d9adf237af6f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/1.00G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/zura1101/Qwen_peft_model_emotion_detection_qlora/commit/e8c51b5603573c1e418ab770a6363ba49fa44ae7', commit_message='Upload model', commit_description='', oid='e8c51b5603573c1e418ab770a6363ba49fa44ae7', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gemma_peft_model.push_to_hub(\"zura1101/Qwen_peft_model_emotion_detection_qlora\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "f626279d96ee4630a1bea45f28483a17",
            "c4b7c4903bb845e7a266c1e99ba74531",
            "f62abf361ce246579f483bb039526ca6",
            "47caff7d2eca48f0a0436235a45132b6",
            "a4de4a3f1a2a404e939a35fe4ff028d9",
            "8f46b0f61cd343c8ae3aad6d75b1e620",
            "56f7aa3f80144780af067810eeb67912",
            "c5a5bdebb4544dfe8081f4108119ef5d",
            "5c672f514c70437991ed8fbabda864fd",
            "ce7913e4dd0a4914ab1d53842ce8811b",
            "daadeaa0beaf4d8d9ba7b0177fc30983"
          ]
        },
        "id": "QGjyIxypwbkQ",
        "outputId": "3ca37f0f-8e99-459a-f602-f954656586c7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f626279d96ee4630a1bea45f28483a17",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of GemmaForSequenceClassification were not initialized from the model checkpoint at google/gemma-1.1-2b-it and are newly initialized: ['score.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": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "\n",
        "peft_model_id = \"zura1101/Qwen_peft_model_emotion_detection_qlora\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "config.base_model_name_or_path\n",
        "base_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path,\n",
        "                                                                num_labels=11,\n",
        "                                                                problem_type=\"multi_label_classification\",\n",
        "                                                                quantization_config=bnb_config)\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "fSHgAaKIwbiN"
      },
      "outputs": [],
      "source": [
        "# Load the Lora model\n",
        "inference_model = PeftModel.from_pretrained(base_model, peft_model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdSDt2EDRmIZ",
        "outputId": "39285ca7-bdaf-4893-a0a1-ea4342003d10"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeftModelForSequenceClassification(\n",
              "  (base_model): LoraModel(\n",
              "    (model): GemmaForSequenceClassification(\n",
              "      (model): GemmaModel(\n",
              "        (embed_tokens): Embedding(256000, 2048, padding_idx=0)\n",
              "        (layers): ModuleList(\n",
              "          (0-17): 18 x GemmaDecoderLayer(\n",
              "            (self_attn): GemmaSdpaAttention(\n",
              "              (q_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=2048, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=2048, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=2048, out_features=256, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=2048, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=256, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=2048, out_features=256, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=2048, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=256, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=2048, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=2048, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (rotary_emb): GemmaRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): GemmaMLP(\n",
              "              (gate_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=2048, out_features=16384, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=2048, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=16384, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (up_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=2048, out_features=16384, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=2048, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=16384, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (down_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=16384, out_features=2048, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.01, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=16384, out_features=100, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=100, out_features=2048, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (act_fn): PytorchGELUTanh()\n",
              "            )\n",
              "            (input_layernorm): GemmaRMSNorm()\n",
              "            (post_attention_layernorm): GemmaRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): GemmaRMSNorm()\n",
              "      )\n",
              "      (score): ModulesToSaveWrapper(\n",
              "        (original_module): Linear(in_features=2048, out_features=11, bias=False)\n",
              "        (modules_to_save): ModuleDict(\n",
              "          (default): Linear(in_features=2048, out_features=11, bias=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inference_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "Cf_OmlZ-wbf2"
      },
      "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",
        "    fp16 = True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "bBWkdakRwbdT"
      },
      "outputs": [],
      "source": [
        "trainer = CustomTrainer(\n",
        "    model=inference_model,\n",
        "    args=training_args,\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "nZc7v-rZkE0U",
        "outputId": "c1c0dd78-15fa-4490-d7dc-81688ac398ae"
      },
      "outputs": [
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_results = trainer.predict(tokenized_dataset[\"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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nm--UfoETd49",
        "outputId": "73dac22b-18c7-4fa1-b0db-560009a49d54"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0, 1, 0, ..., 0, 1, 1],\n",
              "       [0, 1, 1, ..., 1, 1, 0],\n",
              "       [0, 1, 0, ..., 0, 0, 1],\n",
              "       ...,\n",
              "       [0, 1, 0, ..., 0, 0, 0],\n",
              "       [0, 1, 0, ..., 0, 0, 1],\n",
              "       [0, 1, 0, ..., 0, 0, 0]], dtype=int32)"
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          "metadata": {},
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      "source": [
        "y_pred"
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    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
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        },
        "id": "6zhSCo0okExn",
        "outputId": "d4a6e92e-5bcf-4be3-8f60-9a0371e150f9"
      },
      "outputs": [
        {
          "data": {
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              "summary": "{\n  \"name\": \"output_submission\",\n  \"rows\": 3259,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3259,\n        \"samples\": [\n          \"2018-00951\",\n          \"2018-04124\",\n          \"2018-04333\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anger\",\n      \"properties\": {\n        \"dtype\": \"int32\",\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\": \"int32\",\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\": \"int32\",\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\": \"int32\",\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\": \"int32\",\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\": \"int32\",\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\": \"int32\",\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\": \"int32\",\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\": \"int32\",\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\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trust\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "  <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",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-00385</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-03001</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01988</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>2018-03848</td>\n",
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              "      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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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "              ID  anger  anticipation  disgust  fear  joy  love  optimism  \\\n",
              "0     2018-01559      0             1        0     0    0     0         1   \n",
              "1     2018-03739      0             1        1     0    1     0         1   \n",
              "2     2018-00385      0             1        0     1    0     0         1   \n",
              "3     2018-03001      0             1        0     0    1     0         1   \n",
              "4     2018-01988      0             1        0     0    0     0         1   \n",
              "...          ...    ...           ...      ...   ...  ...   ...       ...   \n",
              "3254  2018-03848      1             1        0     0    0     0         0   \n",
              "3255  2018-00416      0             1        0     0    1     0         0   \n",
              "3256  2018-03717      0             1        0     0    0     0         1   \n",
              "3257  2018-03504      0             1        0     0    0     0         1   \n",
              "3258  2018-00115      0             1        0     0    1     0         0   \n",
              "\n",
              "      pessimism  sadness  surprise  trust  \n",
              "0             0        0         1      1  \n",
              "1             0        1         1      0  \n",
              "2             0        0         0      1  \n",
              "3             0        0         0      0  \n",
              "4             0        0         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      1  \n",
              "3258          0        0         0      0  \n",
              "\n",
              "[3259 rows x 12 columns]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "n1RMW-ve5-PG"
      },
      "outputs": [],
      "source": [
        "output_submission.to_csv('/content/drive/MyDrive/NLP/output_submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
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        {
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              "<style>\n",
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              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<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>0.22977</td></tr><tr><td>eval/f1_macro</td><td>0.6042</td></tr><tr><td>eval/f1_micro</td><td>0.69333</td></tr><tr><td>eval/loss</td><td>0.50009</td></tr><tr><td>eval/runtime</td><td>53.2391</td></tr><tr><td>eval/samples_per_second</td><td>29.02</td></tr><tr><td>eval/steps_per_second</td><td>0.47</td></tr><tr><td>total_flos</td><td>1.2104404552153498e+17</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>6.16474</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.2455</td></tr><tr><td>train_loss</td><td>0.4443</td></tr><tr><td>train_runtime</td><td>9843.922</td></tr><tr><td>train_samples_per_second</td><td>6.277</td></tr><tr><td>train_steps_per_second</td><td>0.099</td></tr></table><br/></div></div>"
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