NLP-DeBERTa.ipynb

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      "execution_count": 2,
      "metadata": {
        "id": "d5u1luR1T1JT"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import os\n",
        "import os.path as osp\n",
        "import numpy as np\n",
        "from google.colab import drive\n",
        "import torch\n",
        "from torchvision.datasets import Flowers102\n",
        "%matplotlib inline\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install evaluate"
      ],
      "metadata": {
        "id": "-euiEvHSghjd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from Huggingface\n",
        "! pip install -q datasets \n",
        "! pip install -q evaluate "
      ],
      "metadata": {
        "id": "_2Yp5ZXjg5N_"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "pip install --no-cache-dir transformers sentencepiece"
      ],
      "metadata": {
        "id": "lnu4dTHrhBt5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "import pandas as pd\n",
        "import re\n",
        "import unicodedata\n",
        "import transformers\n",
        "import tensorflow as tf\n",
        "from datasets import Dataset, DatasetDict\n",
        "from transformers import TrainingArguments,Trainer\n",
        "from transformers import TFAutoModel, AutoTokenizer\n",
        "from transformers import DistilBertTokenizer, TFDistilBertModel\n",
        "from sklearn.model_selection import train_test_split, KFold\n",
        "from tensorflow.keras.callbacks import EarlyStopping, Callback\n",
        "from tensorflow.keras.optimizers.schedules import ExponentialDecay"
      ],
      "metadata": {
        "id": "fYR_F5pnZ1FI"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')\n",
        "datadir = \"/content/drive/My Drive/HW3/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpaftflXWh0z",
        "outputId": "a27fccd6-a26d-4567-dbbc-4b67bd7080bb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings, logging\n",
        "warnings.simplefilter('ignore')\n",
        "logging.disable(logging.WARNING)"
      ],
      "metadata": {
        "id": "GuFZOFW0YU1o"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. distilbert-base-uncased model**\n",
        " "
      ],
      "metadata": {
        "id": "lTa7V-q5mHNf"
      }
    },
    {
      "cell_type": "code",
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        "os.chdir(datadir)\n",
        "train = pd.read_csv(\"train.csv\")\n",
        "train.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "outputId": "717f3926-71ef-4f18-8f81-6c73d5d509a0"
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      "execution_count": 9,
      "outputs": [
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              "   id keyword location                                               text  \\\n",
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        "X_train = train.drop(columns=[\"keyword\", \"location\", \"target\"])\n",
        "y_train = train[\"target\"]"
      ],
      "metadata": {
        "id": "TBrfRpIBqI_D"
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        "train_data.head()"
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Dataset.from_pandas(train_data)\n",
        "val_dataset=Dataset.from_pandas(val_data)\n",
        "train_dataset = train_dataset.remove_columns(['__index_level_0__'])\n",
        "val_dataset = val_dataset.remove_columns(['__index_level_0__'])"
      ],
      "metadata": {
        "id": "Mxu4BfzUqicv"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dd = DatasetDict({\"train\": train_dataset, \"val\":val_dataset})"
      ],
      "metadata": {
        "id": "fRoK58VNqr7e"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dd.set_format(\"pandas\")"
      ],
      "metadata": {
        "id": "rTeihiLrqvph"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_batch(batch):\n",
        "    encodings = tokenizer(\n",
        "    batch[\"text\"].tolist(),\n",
        "    padding=True,\n",
        "    return_tensors=\"tf\",\n",
        "    )\n",
        "    batch_dict = {key: value.tolist() for key, value in batch.items() if key in ['id','target']}\n",
        "    encodings_dict = {key:value.numpy().tolist() for key, value in encodings.items()}\n",
        "    return {**batch_dict, **encodings_dict}"
      ],
      "metadata": {
        "id": "u3v3f_G_qznZ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_encoded = dd.map(tokenize_batch, batched=True, batch_size =None)\n",
        "data_encoded.set_format(\"tf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "5b118a0a11134156a5007061af4a6a3e",
            "a65b116193644a839a69a8965a16ca38",
            "41be5ef97c9840d4a9334b0fe5188dbd",
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            "c94958baa43842d5a6cca36b7b2af402",
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            "8a2443f19c944cb38630f975830d409b",
            "3a3c06f4190f400b94c427618662f86c",
            "b9561d5cc1484985ad5d9a49bfd5ff61",
            "bb9b516db75144c1a78464ae6ef21ae0",
            "c9a85f06743e42f29bc1eb2a8cc39db5",
            "877435bbd9784830aa20d86df74a6e68",
            "b87ed916a87a425596e5cd6b71915104",
            "dc056224a1ac41158e4349919e23ba9b",
            "72cc986a11b94c27b8f9cf07c9f7acbe",
            "05c6bea39d1d487bba70bbf78baf50e2",
            "00d193c505074a7e8e1fcdf5ed68b92e",
            "be3bcaaf5d6745aba1ab9e565efb90d9",
            "876d1daefccc4d48a17ebfe3148a27b3",
            "c9705f65203b41518840d3e58d4c5a76",
            "d861da9d6f9f4bde92819e40df2c4999",
            "7c933baad388484f8054797db01e7258"
          ]
        },
        "id": "oGj-YXy1q4TU",
        "outputId": "d239926d-3a45-496e-851f-41620478d46b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5709 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b118a0a11134156a5007061af4a6a3e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1904 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "877435bbd9784830aa20d86df74a6e68"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DistilBertForTweetClassification(tf.keras.Model):\n",
        "    def __init__(self, model_name: str , num_classes: int):\n",
        "        super().__init__()\n",
        "        self.transformer = TFDistilBertModel.from_pretrained(model_name)\n",
        "        self.dense = tf.keras.layers.Dense(512, activation=\"relu\")\n",
        "        self.dense2 = tf.keras.layers.Dense(64, activation=\"relu\")\n",
        "        self.dropout = tf.keras.layers.Dropout(0.4)\n",
        "        self.output_layer = tf.keras.layers.Dense(num_classes, activation=\"sigmoid\")\n",
        "    \n",
        "    def call(self,x):\n",
        "        outputs = self.transformer(x)\n",
        "        cls_last_hidden = outputs.last_hidden_state[:,0,:]\n",
        "        x = self.dropout(cls_last_hidden)\n",
        "        x = self.dense(x)\n",
        "        x = self.dense2(x)\n",
        "        x = self.output_layer(x)\n",
        "        \n",
        "        return x"
      ],
      "metadata": {
        "id": "nHFBugh_rIfj"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_to_tf_dataset(dataset):\n",
        "    input_ids = dataset[\"input_ids\"]\n",
        "    attention_mask = dataset[\"attention_mask\"]\n",
        "    target = dataset[\"target\"]\n",
        "    return {\"input_ids\":input_ids, \"attention_mask\":attention_mask}, target"
      ],
      "metadata": {
        "id": "XkAIZC3brSBc"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_inputs = tf.data.Dataset.from_tensor_slices(convert_to_tf_dataset(data_encoded[\"train\"]))\n",
        "val_data_inputs = tf.data.Dataset.from_tensor_slices(convert_to_tf_dataset(data_encoded[\"val\"]))"
      ],
      "metadata": {
        "id": "UAdoostprVtO"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size =32\n",
        "buffer_size=500\n",
        "train_data_inputs = train_data_inputs.shuffle(buffer_size).batch(batch_size)\n",
        "val_data_inputs = val_data_inputs.batch(batch_size)"
      ],
      "metadata": {
        "id": "j21kgngyraue"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = DistilBertForTweetClassification(model_name, 1)"
      ],
      "metadata": {
        "id": "sFh5kU6BrhRL"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = EarlyStopping(\n",
        "  monitor=\"val_loss\",\n",
        "  patience=2,\n",
        "  verbose=1,\n",
        "  restore_best_weights=True\n",
        ")\n",
        "\n",
        "class LearningRatePrinter(Callback):\n",
        "    def on_epoch_begin(self, epoch, logs=None):\n",
        "        lr = self.model.optimizer.learning_rate.numpy()\n",
        "        print(f\"Learning rate for epoch {epoch + 1}: {lr}\")\n",
        "        \n",
        "lr_printer = LearningRatePrinter()"
      ],
      "metadata": {
        "id": "ExIV2xkCrjxJ"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_schedule = ExponentialDecay(1e-5, len(train_dataset), 1e-4)"
      ],
      "metadata": {
        "id": "QQ-TZvUQrykf"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n",
        "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "    metrics=[\"accuracy\"],\n",
        ")"
      ],
      "metadata": {
        "id": "SQBXglpkr0KB"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_data_inputs,\n",
        "          epochs=10,\n",
        "          validation_data=val_data_inputs,\n",
        "          callbacks=[early_stopping, lr_printer])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vXSfR6Ir10h",
        "outputId": "1a532706-40a1-4a86-dab2-f877cb1063e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate for epoch 1: 9.999999747378752e-06\n",
            "Epoch 1/10\n",
            "179/179 [==============================] - 3191s 18s/step - loss: 0.4878 - accuracy: 0.7735 - val_loss: 0.3949 - val_accuracy: 0.8325\n",
            "Learning rate for epoch 2: 7.5038578870589845e-06\n",
            "Epoch 2/10\n",
            " 84/179 [=============>................] - ETA: 25:08 - loss: 0.3849 - accuracy: 0.8363"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(datadir)\n",
        "test = pd.read_csv(\"test.csv\")\n",
        "test_data = test[[\"id\",\"text\"]]\n",
        "encoded_texts = tokenizer(\n",
        "list(test_data[\"text\"].values),\n",
        "padding=True,\n",
        "return_tensors=\"tf\",\n",
        ")"
      ],
      "metadata": {
        "id": "EU1VIu9ruOGa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = (\n",
        "encoded_texts[\"input_ids\"],\n",
        "encoded_texts[\"attention_mask\"],\n",
        ")"
      ],
      "metadata": {
        "id": "fJiao_truSO_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydGtyPTguauC",
        "outputId": "82ace594-93e0-4a35-b29e-083bc38f18f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102/102 [==============================] - 12s 84ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data[\"target\"] = np.round(pred).astype(int)\n",
        "test_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5-TBbE6YucCM",
        "outputId": "64e039f8-3316-4456-807c-74b26de02c31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                               text  target\n",
              "0   0                 Just happened a terrible car crash       1\n",
              "1   2  Heard about #earthquake is different cities, s...       1\n",
              "2   3  there is a forest fire at spot pond, geese are...       1\n",
              "3   9           Apocalypse lighting. #Spokane #wildfires       1\n",
              "4  11      Typhoon Soudelor kills 28 in China and Taiwan       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4496b71a-3653-4a97-a057-be018676aa24\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Just happened a terrible car crash</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Heard about #earthquake is different cities, s...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>there is a forest fire at spot pond, geese are...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4496b71a-3653-4a97-a057-be018676aa24')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4496b71a-3653-4a97-a057-be018676aa24 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-4496b71a-3653-4a97-a057-be018676aa24');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_output = test_data[[\"id\",\"target\"]]\n",
        "final_output.to_csv(\"submission.csv\", index= False)"
      ],
      "metadata": {
        "id": "h-qXIn26uh6A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('submission.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "frAG3J5dwgep",
        "outputId": "ad8c558b-ab4f-4e9f-a461-2e69e2f2afaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_bea42c1b-538f-422a-b1db-9e5fed435e31\", \"submission.csv\", 22746)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy: 83.29"
      ],
      "metadata": {
        "id": "LRGy8j85xsHz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Microsoft DeBERTa model**"
      ],
      "metadata": {
        "id": "vviArEd2jb1F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "model_name = \"microsoft/deberta-v3-large\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast =False)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "M4NsSMj3e1Nc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(datadir)\n",
        "train = pd.read_csv(\"train.csv\")\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-3dpZIKcjd1U",
        "outputId": "5acda551-c84d-4b19-9104-00a6b65d1171"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id keyword location                                               text  \\\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
              "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
              "\n",
              "   target  \n",
              "0       1  \n",
              "1       1  \n",
              "2       1  \n",
              "3       1  \n",
              "4       1  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "  <thead>\n",
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              "      <th>id</th>\n",
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              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
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              "      <td>6</td>\n",
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              "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b9afd499-7853-49db-b483-b85a23030da2 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-b9afd499-7853-49db-b483-b85a23030da2');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['target'] = train['target'].astype(float)\n",
        "train = train.fillna('[N]')\n",
        "train['input'] = \"[CLS]\" + train.text"
      ],
      "metadata": {
        "id": "bOlPFQabkR--"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = Dataset.from_pandas(train)\n",
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f3161b5-1559-47ad-cb54-e76f7b4e4ab6",
        "id": "CK58UMiGkewD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'keyword', 'location', 'text', 'target'],\n",
              "    num_rows: 7613\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tok_func(x): \n",
        "  return tokenizer(x[\"input\"])"
      ],
      "metadata": {
        "id": "UzpceRxIkMqj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Parallel processing\n",
        "tok_ds = ds.map(tok_func, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
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          ]
        },
        "outputId": "c3b0aae0-8b07-4b23-d4d9-b56b9c81fef1",
        "id": "Gm_cbw2IkMqk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7613 [00:00<?, ? examples/s]"
            ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "row = tok_ds[0]\n",
        "row['input'], row['input_ids']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6904c4f3-5289-4239-e449-ce5941949c34",
        "id": "9ghatLU1kMqk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[CLS]Our Deeds are the Reason of this [HB]earthquake[HE] May ALLAH Forgive us all',\n",
              " [1,\n",
              "  1,\n",
              "  581,\n",
              "  65453,\n",
              "  281,\n",
              "  262,\n",
              "  18037,\n",
              "  265,\n",
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              "  903,\n",
              "  4924,\n",
              "  17018,\n",
              "  43632,\n",
              "  381,\n",
              "  305,\n",
              "  2])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tok_ds = tok_ds.rename_columns({'target':'labels'})"
      ],
      "metadata": {
        "id": "trn-WYorkMql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating training and validation set\n",
        "#dds = DictionaryDataset\n",
        "dds = tok_ds.train_test_split(test_size=0.25, seed=42)\n",
        "dds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93355362-695a-4bb9-93f6-4f56a9346dc7",
        "id": "jmBcmTs_kMql"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'keyword', 'location', 'text', 'labels', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 5709\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'keyword', 'location', 'text', 'labels', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1904\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model training"
      ],
      "metadata": {
        "id": "n_Dery4skMql"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds_cb = np.arange(1)\n",
        "labels_cb = np.arange(1)"
      ],
      "metadata": {
        "id": "mdc1m60zkMql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    metric = evaluate.load(\"f1\")\n",
        "    preds, labels = eval_preds\n",
        "    if len(preds) > 0:\n",
        "        global preds_cb\n",
        "        global labels_cb\n",
        "        preds_cb = np.copy(preds)\n",
        "        labels_cb = np.copy(labels)   \n",
        "    return metric.compute(predictions=preds, references=labels)"
      ],
      "metadata": {
        "id": "l1iZ2VwUkMql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Parameters for training\n",
        "lr = 5e-6\n",
        "bs = 4 \n",
        "gradient_accumulation_steps = 1\n",
        "epochs = 2\n",
        "bs2 = 4\n",
        "gradient_accumulation_steps2 = 1\n",
        "epochs2 = 1"
      ],
      "metadata": {
        "id": "nlH9za1lkMql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(dataset, tokenizer, seed=42, lr=5e-6, epochs=2, bs=4,\n",
        "                model_name='microsoft/deberta-v3-large', gradient_accumulation_steps=1,\n",
        "                reshuffle=0, epochs2=1, bs2=4, gradient_accumulation_steps2=1, fp16=True):\n",
        "    \n",
        "    dds = dataset.train_test_split(0.25, seed=seed)\n",
        "    \n",
        "    args = TrainingArguments('outputs', learning_rate=lr, warmup_ratio=0.1,\n",
        "                             lr_scheduler_type='cosine', fp16=True,\n",
        "                             evaluation_strategy=\"epoch\", per_device_train_batch_size=bs,\n",
        "                             per_device_eval_batch_size=bs*2, num_train_epochs=epochs,\n",
        "                             weight_decay=0.01, report_to='none',\n",
        "                             gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "                             save_steps=-1)\n",
        "    \n",
        "    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=1)\n",
        "    \n",
        "    trainer = Trainer(model, args, train_dataset=dds['train'], eval_dataset=dds['test'],\n",
        "                  tokenizer=tokenizer, compute_metrics=compute_metrics)\n",
        "    \n",
        "    trainer.train()\n",
        "    model = trainer.model \n",
        "    dds = tok_ds.train_test_split(0.25, seed=42)\n",
        "    epochs=epochs2\n",
        "    args = TrainingArguments('outputs', learning_rate=lr/2, warmup_ratio=0.1,\n",
        "                                 lr_scheduler_type='cosine', fp16=True,\n",
        "                                 evaluation_strategy=\"epoch\", per_device_train_batch_size=bs2,\n",
        "                                 per_device_eval_batch_size=bs2*2, num_train_epochs=epochs2,\n",
        "                                 weight_decay=0.01, report_to='none',\n",
        "                                 gradient_accumulation_steps=gradient_accumulation_steps2,\n",
        "                                 save_steps=-1)\n",
        "    trainer = Trainer(model, args, train_dataset=dataset, eval_dataset=dds['test'],\n",
        "                      tokenizer=tokenizer, compute_metrics=compute_metrics)\n",
        "    trainer.train()\n",
        "    return trainer"
      ],
      "metadata": {
        "id": "bikJgr3JkMql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = train_model(dataset=tok_ds, tokenizer=tokenizer, lr=lr, seed=42,\n",
        "                          epochs=epochs, bs=bs, model_name=model_name,\n",
        "                          gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "                          epochs2=epochs2, bs2=bs2,\n",
        "                          gradient_accumulation_steps2=gradient_accumulation_steps2, fp16=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338,
          "referenced_widgets": [
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        "outputId": "3fbf9d62-6920-4b76-abab-282283c13cca",
        "id": "aOHICnyAkMql"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      \n",
              "      <progress value='2856' max='2856' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2856/2856 17:40, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
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              "Downloading builder script:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
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              "      [1904/1904 12:36, Epoch 1/1]\n",
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              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='238' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 30/238 00:02 < 00:15, 13.73 it/s]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
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          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1904' max='1904' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1904/1904 12:49, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.115700</td>\n",
              "      <td>0.125995</td>\n",
              "      <td>0.761062</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "24a09369-c3e5-415c-ab85-a2f148f0c338",
        "id": "fQ_9KcEwl3xU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='238' max='238' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [238/238 00:21]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.12599541246891022,\n",
              " 'eval_f1': 0.7610619469026549,\n",
              " 'eval_runtime': 22.1327,\n",
              " 'eval_samples_per_second': 86.027,\n",
              " 'eval_steps_per_second': 10.753,\n",
              " 'epoch': 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(datadir)\n",
        "eval_df = pd.read_csv('test.csv')\n",
        "eval_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d4984bfa-6019-42ba-9a6d-c574fdf5b14c",
        "id": "1Z1G1czgl3xU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id keyword location                                               text\n",
              "0   0     NaN      NaN                 Just happened a terrible car crash\n",
              "1   2     NaN      NaN  Heard about #earthquake is different cities, s...\n",
              "2   3     NaN      NaN  there is a forest fire at spot pond, geese are...\n",
              "3   9     NaN      NaN           Apocalypse lighting. #Spokane #wildfires\n",
              "4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>location</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9a02d57a-e1c5-4255-8e4f-ec2b52651cde 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-9a02d57a-e1c5-4255-8e4f-ec2b52651cde');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_df = clean_df(eval_df)"
      ],
      "metadata": {
        "id": "xMudF8e3l3xV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_ds = Dataset.from_pandas(eval_df).map(tok_func, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "90aefcdfd99e4cc0926131b44c9a9602",
            "5eab524b76ec40eea14a9b81d38e1415",
            "79275e7c19ac4a459908329459b01736",
            "44d5c64c3dd741c790e91df5ae572d61",
            "35678480f73243c1a6add6d77e803eb2",
            "5283b595884b4c29b5b52a92038a7837",
            "faa423e4c1994cc0b05a00545aa7c379",
            "57f55ae48fcd422db6dea617f8879475",
            "106b42b1199f4189a7646adb6718fd55",
            "00c27fc2429f4015930188320a082707",
            "da00c68adce6420c875df8dfd0680b84"
          ]
        },
        "outputId": "669d518c-f531-4e3a-b549-9d670ef3f860",
        "id": "Z6u4l4Mrl3xV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3263 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90aefcdfd99e4cc0926131b44c9a9602"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = torch.empty((0,len(eval_df)))\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72fd35a8-0275-4582-e624-a0b6d02e6e39",
        "id": "zBCbfh4Nl3xV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([], size=(0, 3263))"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = trainer.predict(eval_ds).predictions.astype(float)\n",
        "prediction = torch.tensor(preds)\n",
        "predictions = torch.cat([predictions,prediction.T],dim=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "689783f8-60a8-4e22-d403-d2912dd3ff3c",
        "id": "A9xQ46Fel3xV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = predictions.numpy()"
      ],
      "metadata": {
        "id": "T4wPYfIvl3xV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds2 = [ 1 if element > 0.6 else 0 for element in preds.squeeze()]\n",
        "preds = preds2"
      ],
      "metadata": {
        "id": "UL9DqXDtl3xW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "\n",
        "submission = datasets.Dataset.from_dict({\n",
        "    'id': eval_ds['id'],\n",
        "    'target': preds\n",
        "})\n",
        "submission.to_csv('submission.csv', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "90a86e60abbd4613b0134264e8cf62b6",
            "900b15bb8668410ca10c04c02dbda7a8",
            "1195c2d839354458b9fe77db6be7f2de",
            "2108adae3cdc40dca1858cc4f55034b9",
            "f9741156a70048208fefbe305ac4401f",
            "e82dec607adc4445bea2ff7c6b992fa4",
            "3460cc34e1fe47ebad6e9dbc1b642531",
            "f1ec18e718774906b72973945f36fb11",
            "eaaafc52069b4020adcf93252890958a",
            "d7d8f2313e40421393415f1a0cc7655e",
            "973b7182475c40318662b95429fb650d"
          ]
        },
        "outputId": "4f3ad186-132a-4994-c6d0-296561651393",
        "id": "l1LCgVtBl3xW"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating CSV from Arrow format:   0%|          | 0/4 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90a86e60abbd4613b0134264e8cf62b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22746"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('submission.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "cdc0fa84-c74a-48d1-ce71-bd33813bb3d0",
        "id": "NuWF3tq8l3xW"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_119f2046-f672-48f8-9806-3a76bc4921eb\", \"submission.csv\", 22746)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy: 84.55"
      ],
      "metadata": {
        "id": "AU3SZFJvto-a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Microsoft DeBERTa model on cleaned data**"
      ],
      "metadata": {
        "id": "i4-Soob8iYUA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Predefined special tokens\n",
        "tokenizer.all_special_tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-GvOZtr2oPvA",
        "outputId": "a2466e51-d553-4a33-ca73-8c1849cb1349"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " '[SEP]',\n",
              " '[UNK]',\n",
              " '[PAD]',\n",
              " '[MASK]',\n",
              " '[MB]',\n",
              " '[ME]',\n",
              " '[U]',\n",
              " '[HB]',\n",
              " '[HE]']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_token_list = [\n",
        "    \"[MB]\", #mention beginning\n",
        "    \"[ME]\", #mention end\n",
        "    \"[U]\",  #URL\n",
        "    \"[HB]\", #hashtag beginning\n",
        "    \"[HE]\"  #hashtag end \n",
        "    ]\n",
        "\n",
        "tokenizer.add_special_tokens({'additional_special_tokens': new_token_list})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xLal-FqSpPl4",
        "outputId": "5960c106-ae5c-4ddc-c4a3-36b94f44ae5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(datadir)\n",
        "train = pd.read_csv(\"train.csv\")\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qnWQoKWRpzRq",
        "outputId": "c554d0a3-b9e6-4061-dd1d-e478b6503c77"
      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "   id keyword location                                               text  \\\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
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              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
              "\n",
              "   target  \n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['target'] = train['target'].astype(float)"
      ],
      "metadata": {
        "id": "SHoYS_bP_ZQk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Cleaning"
      ],
      "metadata": {
        "id": "UQySOeqks9Cx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_url(df: pd.DataFrame, column: str) -> pd.DataFrame:\n",
        "    \"\"\"\n",
        "    Replaces URLs in the specified column of a pandas DataFrame with the '[U]' placeholder.\n",
        "    \n",
        "    Args:\n",
        "        df (pandas.DataFrame): The DataFrame containing the column to modify.\n",
        "        column (str): The name of the column to modify.\n",
        "        \n",
        "    Returns:\n",
        "        pandas.DataFrame: The modified DataFrame with URLs replaced with '[U]'.\n",
        "    \"\"\"\n",
        "    df[column] = df[column].replace(r\"(http|https)://[^\\s]+\", \"[U]\", regex=True)\n",
        "    return df"
      ],
      "metadata": {
        "id": "Ojbk6KpwrlCP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wrap_urls(df, col_name):\n",
        "    def wrap_url(match):\n",
        "        return \"[UB]{}[UE]\".format(match.group())\n",
        "    df[col_name] = df[col_name].apply(lambda x: re.sub(r\"(https?://\\S+)\", wrap_url, x))\n",
        "    return df"
      ],
      "metadata": {
        "id": "pttKARawsLms"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_mentions(df: pd.DataFrame, column: str) -> pd.DataFrame:\n",
        "    # Use a regular expression to match mentions, which start with \"@\" followed by letters, numbers, or underscores\n",
        "    df[column] = df[column].str.replace(r\"@\\w+\", \"[M]\")\n",
        "    return df"
      ],
      "metadata": {
        "id": "Iiiiidm9saan"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wrap_mentions(df: pd.DataFrame, col_name: str) -> pd.DataFrame:\n",
        "    def replace_mention(match):\n",
        "        return f\"[MB]{match.group()}[ME]\"\n",
        "    df[col_name] = df[col_name].apply(lambda x: re.sub(r\"@\\w+\", replace_mention, x))\n",
        "    return df"
      ],
      "metadata": {
        "id": "atF2jK9vslU3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wrap_hashtags(df: pd.DataFrame, column: str) -> pd.DataFrame:\n",
        "    def replace_hashtag(match):\n",
        "        return \"[HB]{}[HE]\".format(match.group())\n",
        "    df[column] = df[column].apply(lambda x: re.sub(r\"#\\w+\", replace_hashtag, x))\n",
        "    return df"
      ],
      "metadata": {
        "id": "JJy6W8M6tBIW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_non_ascii(df, column):\n",
        "    df[column] = df[column].apply(lambda x: \"\".join(char for char in x if ord(char) < 128))\n",
        "    return df"
      ],
      "metadata": {
        "id": "MLG6anhitcrF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_nan(df, column):\n",
        "    \"\"\"Remove all occurrences of the string \"nan\" from a column in a DataFrame.\"\"\"\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"nan\", \"\") if isinstance(x, str) else x)\n",
        "    return df"
      ],
      "metadata": {
        "id": "A06I-heBvUsp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_special_cases(df, column):\n",
        "    \"\"\"Remove all occurrences of certain strings from a column in a DataFrame.\"\"\"\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"%20\", \" \"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"&amp;\", \" \"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"\\n\", \" \"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"@\", \"\"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"#\", \"\"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"Ûª\", \"'\"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"Û÷\", \"'\"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"&lt;\", \"\"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"&gt\", \"\"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"[KB][N][KE]\", \"\"))\n",
        "    df[column] = df[column].apply(lambda x: x.replace(\"[LB][N][LE]\", \"\"))\n",
        "    return df"
      ],
      "metadata": {
        "id": "FXyPvzn-vWwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_df(df):\n",
        "    df = df.fillna('[N]')\n",
        "    df['input'] = \"[CLS]\" + df.text\n",
        "    df = wrap_mentions(df, 'text')\n",
        "    df = wrap_mentions(df, 'input')\n",
        "    df = wrap_hashtags(df, 'text')\n",
        "    df = wrap_hashtags(df, 'input')\n",
        "    df = remove_special_cases(df, 'input')\n",
        "    df = remove_special_cases(df, 'text')\n",
        "    df = remove_non_ascii(df, 'input')\n",
        "    df = replace_url(df, 'text')\n",
        "    df = replace_url(df, 'input')\n",
        "    return df"
      ],
      "metadata": {
        "id": "4x6fFboiv6Gx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = clean_df(train)"
      ],
      "metadata": {
        "id": "NA-sNofRwB3I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = Dataset.from_pandas(train)\n",
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHlfwLch0Ru_",
        "outputId": "afd07ac7-ecfb-4105-b512-495257b98fb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'keyword', 'location', 'text', 'target', 'input'],\n",
              "    num_rows: 7613\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tok_func(x): return tokenizer(x[\"input\"])"
      ],
      "metadata": {
        "id": "TcuMusOG0zHh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Parallel processing\n",
        "tok_ds = ds.map(tok_func, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "2db0a5b2a7bb47ad94e06cef35c3c59a",
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            "c440559e7ce745cdb1b520f2bf580fcf",
            "c08ca71b1476479190baf22c1c99f34a",
            "dab8e21281e9422ba4bcab0528a64328",
            "45e9895cf03b4c008be2458849469cbd",
            "7b9019e670a1485583fb563353ee0cc5",
            "6d0066ae1daf4c4cb8a1aaba2f6a4daf",
            "f8a4244db3074372afaa96b9bcbd5303"
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        },
        "id": "pjDzsBuR1Anq",
        "outputId": "8a71940e-cd61-48f6-bb3a-31fd2b23cc75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7613 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2db0a5b2a7bb47ad94e06cef35c3c59a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "row = tok_ds[0]\n",
        "row['input'], row['input_ids']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGsw0Lbv1N9D",
        "outputId": "6904c4f3-5289-4239-e449-ce5941949c34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[CLS]Our Deeds are the Reason of this [HB]earthquake[HE] May ALLAH Forgive us all',\n",
              " [1,\n",
              "  1,\n",
              "  581,\n",
              "  65453,\n",
              "  281,\n",
              "  262,\n",
              "  18037,\n",
              "  265,\n",
              "  291,\n",
              "  128004,\n",
              "  10612,\n",
              "  128005,\n",
              "  903,\n",
              "  4924,\n",
              "  17018,\n",
              "  43632,\n",
              "  381,\n",
              "  305,\n",
              "  2])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tok_ds = tok_ds.rename_columns({'target':'labels'})"
      ],
      "metadata": {
        "id": "31bjQx7c1VXX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating training and validation set\n",
        "#dds = DictionaryDataset\n",
        "dds = tok_ds.train_test_split(test_size=0.25, seed=42)\n",
        "dds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tbol8qPW1bX0",
        "outputId": "85e5caf5-70d7-4b4b-d793-09e33d03f16e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'keyword', 'location', 'text', 'labels', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 5709\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'keyword', 'location', 'text', 'labels', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1904\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model training"
      ],
      "metadata": {
        "id": "Iq5LkifT2Y3I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds_cb = np.arange(1)\n",
        "labels_cb = np.arange(1)"
      ],
      "metadata": {
        "id": "QTElewi61mP-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    metric = evaluate.load(\"f1\")\n",
        "    preds, labels = eval_preds\n",
        "    if len(preds) > 0:\n",
        "        global preds_cb\n",
        "        global labels_cb\n",
        "        preds_cb = np.copy(preds)\n",
        "        labels_cb = np.copy(labels)   \n",
        "    return metric.compute(predictions=preds, references=labels)"
      ],
      "metadata": {
        "id": "-slEr_CK2be4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Parameters for training\n",
        "lr = 5e-6\n",
        "bs = 4 \n",
        "gradient_accumulation_steps = 1\n",
        "epochs = 2\n",
        "bs2 = 4\n",
        "gradient_accumulation_steps2 = 1\n",
        "epochs2 = 1"
      ],
      "metadata": {
        "id": "RgeNOxb93pbW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(dataset, tokenizer, seed=42, lr=5e-6, epochs=2, bs=4,\n",
        "                model_name='microsoft/deberta-v3-large', gradient_accumulation_steps=1,\n",
        "                reshuffle=0, epochs2=1, bs2=4, gradient_accumulation_steps2=1, fp16=True):\n",
        "    \n",
        "    dds = dataset.train_test_split(0.25, seed=seed)\n",
        "    \n",
        "    args = TrainingArguments('outputs', learning_rate=lr, warmup_ratio=0.1,\n",
        "                             lr_scheduler_type='cosine', fp16=True,\n",
        "                             evaluation_strategy=\"epoch\", per_device_train_batch_size=bs,\n",
        "                             per_device_eval_batch_size=bs*2, num_train_epochs=epochs,\n",
        "                             weight_decay=0.01, report_to='none',\n",
        "                             gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "                             save_steps=-1)\n",
        "    \n",
        "    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=1)\n",
        "    \n",
        "    trainer = Trainer(model, args, train_dataset=dds['train'], eval_dataset=dds['test'],\n",
        "                  tokenizer=tokenizer, compute_metrics=compute_metrics)\n",
        "    \n",
        "    trainer.train()\n",
        "    model = trainer.model \n",
        "    dds = tok_ds.train_test_split(0.25, seed=42)\n",
        "    epochs=epochs2\n",
        "    args = TrainingArguments('outputs', learning_rate=lr/2, warmup_ratio=0.1,\n",
        "                                 lr_scheduler_type='cosine', fp16=True,\n",
        "                                 evaluation_strategy=\"epoch\", per_device_train_batch_size=bs2,\n",
        "                                 per_device_eval_batch_size=bs2*2, num_train_epochs=epochs2,\n",
        "                                 weight_decay=0.01, report_to='none',\n",
        "                                 gradient_accumulation_steps=gradient_accumulation_steps2,\n",
        "                                 save_steps=-1)\n",
        "    trainer = Trainer(model, args, train_dataset=dataset, eval_dataset=dds['test'],\n",
        "                      tokenizer=tokenizer, compute_metrics=compute_metrics)\n",
        "    trainer.train()\n",
        "    return trainer"
      ],
      "metadata": {
        "id": "UGFWjV9i4Auq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = train_model(dataset=tok_ds, tokenizer=tokenizer, lr=lr, seed=42,\n",
        "                          epochs=epochs, bs=bs, model_name=model_name,\n",
        "                          gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "                          reshuffle=reshuffle, epochs2=epochs2, bs2=bs2,\n",
        "                          gradient_accumulation_steps2=gradient_accumulation_steps2, fp16=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259,
          "referenced_widgets": [
            "899fd0dab45540c49b340b209a54963a",
            "593a3cb1d33c443d9a4c69d0080eeaa4",
            "57c596a16dbf4c4e80f8d7bb2fb562d2",
            "d5a6e68073f4462d95a34129c46c3027",
            "02a40da158824703b7db63fe6f277995",
            "2ccb47ea322740c9a7326507ea2420b2",
            "d3405ef67a6443ee94b2401c0717a391",
            "9dcdb1da0c904b94a5b77db361b6de79",
            "7a36d295e6ca4f56acd12dd9eb5c30a3",
            "8aee0d4e330b4b7b85d926c113f72373",
            "f866d457a13b47d493243e29c8a95d4a"
          ]
        },
        "id": "bmKI9ESF7oEx",
        "outputId": "57034ec8-ea69-4265-f88d-6c5c6652f8e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2856' max='2856' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2856/2856 19:05, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.155400</td>\n",
              "      <td>0.141534</td>\n",
              "      <td>0.583830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.122600</td>\n",
              "      <td>0.146558</td>\n",
              "      <td>0.749638</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1904' max='1904' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1904/1904 13:28, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.118400</td>\n",
              "      <td>0.123141</td>\n",
              "      <td>0.770563</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "RIEaqOTfGlXA",
        "outputId": "425453e3-8773-4b4a-82bb-016204bdcfc8"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='238' max='238' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [238/238 00:17]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.12314070016145706,\n",
              " 'eval_f1': 0.7705627705627704,\n",
              " 'eval_runtime': 17.9725,\n",
              " 'eval_samples_per_second': 105.94,\n",
              " 'eval_steps_per_second': 13.242,\n",
              " 'epoch': 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(datadir)\n",
        "eval_df = pd.read_csv('test.csv')\n",
        "eval_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "o-JpB7BpGujk",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id keyword location                                               text\n",
              "0   0     NaN      NaN                 Just happened a terrible car crash\n",
              "1   2     NaN      NaN  Heard about #earthquake is different cities, s...\n",
              "2   3     NaN      NaN  there is a forest fire at spot pond, geese are...\n",
              "3   9     NaN      NaN           Apocalypse lighting. #Spokane #wildfires\n",
              "4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-f0baa817-3350-461c-bc37-faf636b8dcec 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-f0baa817-3350-461c-bc37-faf636b8dcec');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_df = clean_df(eval_df)"
      ],
      "metadata": {
        "id": "9NZq4ACtHsE8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_ds = Dataset.from_pandas(eval_df).map(tok_func, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "b0d3666bf75c4ffd8fb8ffab6dd27bf8",
            "77ac96fd1fc64362a466c63aac65fdb4",
            "7d3492cb2e9f46fea064f24a38043198",
            "6ef30a4b2d2a4993a2aa53eeb97b2ccc",
            "6de390255d3e4ae2963a8a56a600b977",
            "77aacbf2cb014238854a1d1cf5c0c066",
            "e8a16b58d240486abdf7b800d87791ba",
            "dde21125f6774259afb2f5112015221d",
            "7f6b4a64200343048de25089cacf8f68",
            "d0ba23641eb84045acec88eb4f64bb34",
            "e065c368d60e4d859e6abf1b9b481bf1"
          ]
        },
        "id": "586yExlmHufQ",
        "outputId": "f3227632-45c4-4519-d985-ef1a1f771667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3263 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0d3666bf75c4ffd8fb8ffab6dd27bf8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = torch.empty((0,len(eval_df)))\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMQ12LRtHwJi",
        "outputId": "4b54b012-3d78-4d02-d39c-0ef519cc22e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([], size=(0, 3263))"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = trainer.predict(eval_ds).predictions.astype(float)\n",
        "prediction = torch.tensor(preds)\n",
        "predictions = torch.cat([predictions,prediction.T],dim=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "N87jZMxSI7zI",
        "outputId": "409fdfc5-03ce-4da1-c7d2-69fe9b066b21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = predictions.numpy()"
      ],
      "metadata": {
        "id": "L0v0q-KZJAWm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds2 = [ 1 if element > 0.6 else 0 for element in preds.squeeze()]\n",
        "preds = preds2"
      ],
      "metadata": {
        "id": "Dqo_bQCEKnrM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "\n",
        "submission = datasets.Dataset.from_dict({\n",
        "    'id': eval_ds['id'],\n",
        "    'target': preds\n",
        "})\n",
        "submission.to_csv('submission.csv', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "97478921dd69458a9e68268b2b54d48b",
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            "5c44a78f2679429b9d5481abf006bc71",
            "93fe5fd2bbc349548caf838c532f3107",
            "975ae96918ce485394ac0840a511f038",
            "ba8174a865b341edb20edfac1b5ae956",
            "ef5dc6e4208d4da183c1bc060c70a070",
            "7f3d2edff92a4f03a65e52dda24dd48a",
            "417f8b3b91f3470dbfeb9bc0b3b6198e",
            "edb1d7fb5502441ea34e90b16eab8130"
          ]
        },
        "id": "gl3-0eXNK1OF",
        "outputId": "ffd69719-c852-4a24-c64d-26d605adbb62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating CSV from Arrow format:   0%|          | 0/4 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97478921dd69458a9e68268b2b54d48b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22746"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('submission.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "ObQlWIRtLTVm",
        "outputId": "56493899-2f1e-4011-e421-76711c79859a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_373f2e31-5776-419a-a44d-e6d8d313600b\", \"submission.csv\", 22746)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy: 84.49%"
      ],
      "metadata": {
        "id": "Z7Z7dh6betlL"
      }
    }
  ]
}