BERT_uncased.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NLROUCiPAsmP",
        "outputId": "f66b9fff-fa96-43d6-f399-7056e7b4e8e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n",
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            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
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            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9675 sha256=8f0c54c0c33a29a9f11c7bc7a97bfba9b9557b0211cf9976debf05c6fe9f8632\n",
            "  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n",
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            "Installing collected packages: wget\n",
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            "Collecting sentencepiece\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install torch\n",
        "!pip install sklearn\n",
        "!pip install wget\n",
        "!pip install sentencepiece"
      ],
      "id": "NLROUCiPAsmP"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IGuTwKyYAs9x"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizer, BertModel, BertForSequenceClassification, AdamW, BertConfig\n",
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import pandas as pd, numpy as np\n",
        "import random, torch, wget, os\n",
        "from tqdm import tqdm"
      ],
      "id": "IGuTwKyYAs9x"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "formal-skating",
        "outputId": "7d16ad88-caed-4fac-de2b-31456ec8ab1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: Tesla P100-PCIE-16GB\n"
          ]
        }
      ],
      "source": [
        "pd.set_option('precision', 2)\n",
        "\n",
        "local = False\n",
        "gpu = True\n",
        "epochs = 4\n",
        "batch_size = 32\n",
        "\n",
        "# If there's a GPU available...\n",
        "if torch.cuda.is_available() and not local:\n",
        "    # Tell PyTorch to use the GPU.    \n",
        "    device = torch.device(\"cuda\")\n",
        "\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
        "\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
        "\n",
        "# If not...\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")\n",
        "    gpu = False"
      ],
      "id": "formal-skating"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "greater-metallic"
      },
      "source": [
        "### Dowloading dataset"
      ],
      "id": "greater-metallic"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "defensive-domestic",
        "outputId": "778451c6-0144-48ec-f771-a6d8c2780416"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'webis_test.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "wget.download(\"https://ml-coding-test.s3.eu-west-1.amazonaws.com/webis_train.csv\", 'webis_train.csv')\n",
        "wget.download(\"https://ml-coding-test.s3.eu-west-1.amazonaws.com/webis_test.csv\", 'webis_test.csv')"
      ],
      "id": "defensive-domestic"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irish-least"
      },
      "source": [
        "### Reading dataset and splitting training data"
      ],
      "id": "irish-least"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "consistent-mauritius",
        "outputId": "efd62ee2-3b53-4c30-8048-a4703eae85d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training sentences: 17,535\n",
            "\n",
            "Number of validation sentences: 1,949\n",
            "\n",
            "Number of test sentences: 18,913\n",
            "\n",
            "Max length: 124\n",
            "\n"
          ]
        }
      ],
      "source": [
        "def cast_labels(data: pd.core.series.Series):\n",
        "    data[data==\"no-clickbait\"] = 0\n",
        "    data[data==\"clickbait\"] = 1\n",
        "    return data\n",
        "\n",
        "df = pd.read_csv(\"webis_train.csv\", delimiter=',', header=0)\n",
        "# Removing nan values to simplify training process / I will only use postText to classify each instance\n",
        "df = df[[\"postText\", \"truthClass\"]].dropna()\n",
        "training_sentences = df.postText\n",
        "training_labels = cast_labels(df.truthClass)\n",
        "\n",
        "training_sentences, validation_sentences, training_labels, validation_labels = train_test_split(training_sentences, \n",
        "                                                                                                training_labels, test_size=0.1, random_state=42, stratify=training_labels)\n",
        "\n",
        "df = pd.read_csv(\"webis_test.csv\", delimiter=',', header=0)\n",
        "# Removing nan values to simplify training process / I will only use postText to classify each instance\n",
        "df = df[[\"postText\", \"truthClass\"]].dropna()\n",
        "test_sentences = df.postText\n",
        "test_labels = cast_labels(df.truthClass)\n",
        "\n",
        "del df\n",
        "print('Number of training sentences: {:,}\\n'.format(training_sentences.shape[0]))\n",
        "print('Number of validation sentences: {:,}\\n'.format(validation_sentences.shape[0]))\n",
        "print('Number of test sentences: {:,}\\n'.format(test_sentences.shape[0]))\n",
        "max_length = max([max(map(len, training_sentences)), max(map(len, validation_sentences)), max(map(len, test_sentences))])\n",
        "print('Max length: {:,}\\n'.format(max_length))\n",
        "# manual setting max_length to 200 to ensure compatibility after tokenization.\n",
        "max_length = 200"
      ],
      "id": "consistent-mauritius"
    },
    {
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        "id": "characteristic-stereo",
        "outputId": "d106b8bd-e0fe-420c-ef8e-02f99621c09f"
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      "outputs": [
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          },
          "metadata": {}
        }
      ],
      "source": [
        "if local:\n",
        "    tokenizer = BertTokenizer.from_pretrained('./bert_models/bert-base-uncased/')\n",
        "else:\n",
        "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ],
      "id": "characteristic-stereo"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "meaningful-track"
      },
      "outputs": [],
      "source": [
        "def cast_data(data: pd.core.series.Series, max_length):\n",
        "    input_ids = []\n",
        "    attention_masks = []\n",
        "    for sent in data:\n",
        "        encoded_dict = tokenizer.encode_plus(\n",
        "                            sent,                      \n",
        "                            truncation=True,\n",
        "                            max_length = max_length,\n",
        "                            padding = \"max_length\",\n",
        "                            return_attention_mask = True,\n",
        "                            return_tensors = 'pt'\n",
        "                       )\n",
        "        input_ids.append(encoded_dict['input_ids'])\n",
        "        attention_masks.append(encoded_dict['attention_mask'])\n",
        "    return torch.cat(input_ids, dim=0), torch.cat(attention_masks, dim=0)"
      ],
      "id": "meaningful-track"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "perfect-correction"
      },
      "outputs": [],
      "source": [
        "training_ids, training_attention = cast_data(training_sentences, max_length)\n",
        "training_labels = torch.tensor(training_labels.to_list())\n",
        "validation_ids, validation_attention = cast_data(validation_sentences, max_length)\n",
        "validation_labels = torch.tensor(validation_labels.to_list())"
      ],
      "id": "perfect-correction"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "sweet-union"
      },
      "outputs": [],
      "source": [
        "training_dataset = TensorDataset(training_ids, training_attention, training_labels)\n",
        "train_dataloader = DataLoader(training_dataset,  sampler = RandomSampler(training_dataset),  batch_size = batch_size)\n",
        "validation_dataset = TensorDataset(validation_ids, validation_attention, validation_labels)\n",
        "validation_dataloader = DataLoader(validation_dataset, sampler = SequentialSampler(validation_dataset), batch_size = batch_size)"
      ],
      "id": "sweet-union"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
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      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "if local:\n",
        "    model = BertForSequenceClassification.from_pretrained(\"./bert_models/bert-base-cased/\", \n",
        "                                                          num_labels = 2, \n",
        "                                                          output_attentions = False, output_hidden_states = False)\n",
        "else:\n",
        "    model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", \n",
        "                                                          num_labels = 2, \n",
        "                                                          output_attentions = False, output_hidden_states = False)\n",
        "if gpu:\n",
        "    model.cuda()"
      ],
      "id": "employed-cologne"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quality-enhancement",
        "outputId": "66a64a22-5845-48bd-d2cd-272fd57d98d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "optimizer = AdamW(model.parameters(), lr = 2e-5)"
      ],
      "id": "quality-enhancement"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "periodic-explosion"
      },
      "outputs": [],
      "source": [
        "# Function to calculate the accuracy of our predictions vs labels\n",
        "def flat_accuracy(preds, labels):\n",
        "    labels_flat = labels.flatten()\n",
        "    return np.sum(np.argmax(preds, axis=1).flatten() == labels_flat) / len(labels_flat)"
      ],
      "id": "periodic-explosion"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "large-begin"
      },
      "source": [
        "### Model training"
      ],
      "id": "large-begin"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "occasional-protocol",
        "outputId": "21e30516-7a17-421e-c1ab-d946047302fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training epoch 0: 548it [05:30,  1.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  Average training loss: 0.37\n",
            "\n",
            "Running Validation...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing validation dataset: 100%|██████████| 61/61 [00:12<00:00,  4.95it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Accuracy: 0.85\n",
            "  Validation Loss: 0.33\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training epoch 1: 548it [05:30,  1.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  Average training loss: 0.29\n",
            "\n",
            "Running Validation...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing validation dataset: 100%|██████████| 61/61 [00:12<00:00,  4.96it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Accuracy: 0.85\n",
            "  Validation Loss: 0.34\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training epoch 2: 548it [05:30,  1.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  Average training loss: 0.19\n",
            "\n",
            "Running Validation...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing validation dataset: 100%|██████████| 61/61 [00:12<00:00,  4.96it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Accuracy: 0.84\n",
            "  Validation Loss: 0.43\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training epoch 3: 548it [05:30,  1.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  Average training loss: 0.12\n",
            "\n",
            "Running Validation...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing validation dataset: 100%|██████████| 61/61 [00:12<00:00,  4.96it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Accuracy: 0.84\n",
            "  Validation Loss: 0.58\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "seed_val = 42\n",
        "\n",
        "random.seed(seed_val)\n",
        "np.random.seed(seed_val)\n",
        "torch.manual_seed(seed_val)\n",
        "torch.cuda.manual_seed_all(seed_val)\n",
        "\n",
        "history = []\n",
        "\n",
        "for epoch_i in range(0, epochs):\n",
        "    total_train_loss = 0\n",
        "    \n",
        "    model.train()\n",
        "\n",
        "    progress_bar = tqdm(enumerate(train_dataloader), desc=\"Training epoch \"+str(epoch_i), miniters=20)\n",
        "    for step, batch in progress_bar:\n",
        "        b_input_ids = batch[0].to(device)\n",
        "        b_input_mask = batch[1].to(device)\n",
        "        b_labels = batch[2].to(device)\n",
        "\n",
        "        model.zero_grad()        \n",
        "\n",
        "        loss, logits = model(b_input_ids, \n",
        "                             token_type_ids=None, \n",
        "                             attention_mask=b_input_mask, \n",
        "                             labels=b_labels).to_tuple()\n",
        "\n",
        "        total_train_loss += loss.item()\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "        optimizer.step()\n",
        "\n",
        "    avg_train_loss = total_train_loss / len(train_dataloader)            \n",
        "    \n",
        "\n",
        "    \n",
        "    print(\"\")\n",
        "    print(\"  Average training loss: {0:.2f}\".format(avg_train_loss))\n",
        "    \n",
        "    \n",
        "    print(\"\")\n",
        "    print(\"Running Validation...\")\n",
        "\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    total_eval_accuracy = 0\n",
        "    total_eval_loss = 0\n",
        "    nb_eval_steps = 0\n",
        "    progress_bar = tqdm(validation_dataloader, desc=\"Processing validation dataset\", miniters=20)\n",
        "\n",
        "    # Evaluate data for one epoch\n",
        "    for batch in progress_bar:        \n",
        "        b_input_ids = batch[0].to(device)\n",
        "        b_input_mask = batch[1].to(device)\n",
        "        b_labels = batch[2].to(device)\n",
        "        \n",
        "        with torch.no_grad():        \n",
        "            loss, logits = model(b_input_ids, \n",
        "                                   token_type_ids=None, \n",
        "                                   attention_mask=b_input_mask,\n",
        "                                   labels=b_labels).to_tuple()\n",
        "            \n",
        "        total_eval_loss += loss.item()\n",
        "\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "        label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "        total_eval_accuracy += flat_accuracy(logits, label_ids)\n",
        "    avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n",
        "    print(\"  Accuracy: {0:.2f}\".format(avg_val_accuracy))\n",
        "    \n",
        "    avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
        "    \n",
        "    print(\"  Validation Loss: {0:.2f}\".format(avg_val_loss))\n",
        "\n",
        "    \n",
        "    history.append(\n",
        "        {\n",
        "            'epoch': epoch_i + 1,\n",
        "            'Training Loss': avg_train_loss,\n",
        "            'Valid. Loss': avg_val_loss,\n",
        "            'Valid. Accur.': avg_val_accuracy\n",
        "        }\n",
        "    )"
      ],
      "id": "occasional-protocol"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "disturbed-preliminary"
      },
      "source": [
        "### Training stats"
      ],
      "id": "disturbed-preliminary"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "acute-reporter",
        "outputId": "44a7c0cf-3ac2-49b3-f02e-087171dc5377"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Training Loss  Valid. Loss  Valid. Accur.\n",
              "epoch                                           \n",
              "1               0.37         0.33           0.85\n",
              "2               0.29         0.34           0.85\n",
              "3               0.19         0.43           0.84\n",
              "4               0.12         0.58           0.84"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Valid. Loss</th>\n",
              "      <th>Valid. Accur.</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>epoch</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.29</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.19</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.12</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.84</td>\n",
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              "  </tbody>\n",
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              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-805f8888-6be7-4deb-b523-4a91882503d1 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-805f8888-6be7-4deb-b523-4a91882503d1');\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",
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          },
          "metadata": {},
          "execution_count": 14
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      ],
      "source": [
        "df_stats = pd.DataFrame(data=history).set_index('epoch')\n",
        "df_stats"
      ],
      "id": "acute-reporter"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
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          "height": 427
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        "id": "recovered-artwork",
        "outputId": "9ec77f38-b563-499f-e087-e827f5d09543"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Use plot styling from seaborn.\n",
        "sns.set(style='darkgrid')\n",
        "\n",
        "# Increase the plot size and font size.\n",
        "sns.set(font_scale=1.5)\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
        "\n",
        "# Plot the learning curve.\n",
        "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n",
        "plt.plot(df_stats['Valid. Loss'], 'g-o', label=\"Validation\")\n",
        "\n",
        "# Label the plot.\n",
        "plt.title(\"Training & Validation Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.xticks([1, 2, 3, 4])\n",
        "plt.show()"
      ],
      "id": "recovered-artwork"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "consecutive-bibliography"
      },
      "source": [
        "## Testing the model"
      ],
      "id": "consecutive-bibliography"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "smooth-wonder"
      },
      "outputs": [],
      "source": [
        "test_ids, test_attention = cast_data(test_sentences, max_length)\n",
        "test_labels = torch.tensor(test_labels.to_list())\n",
        "prediction_data = TensorDataset(test_ids, test_attention, test_labels)\n",
        "prediction_sampler = SequentialSampler(prediction_data)\n",
        "prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=32)"
      ],
      "id": "smooth-wonder"
    },
    {
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      "execution_count": 17,
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        "model.eval()\n",
        "predictions , true_labels = [], []\n",
        "for batch in prediction_dataloader:\n",
        "    batch = tuple(t.to(device) for t in batch)\n",
        "    b_input_ids, b_input_mask, b_labels = batch\n",
        "  \n",
        "    with torch.no_grad():\n",
        "        outputs = model(b_input_ids, token_type_ids=None, \n",
        "                          attention_mask=b_input_mask).to_tuple()\n",
        "    logits = outputs[0]\n",
        "    logits = logits.detach().cpu().numpy()\n",
        "    label_ids = b_labels.to('cpu').numpy()\n",
        "    predictions+=[np.argmax(x) for x in logits.tolist()]\n",
        "    true_labels+=label_ids.tolist()"
      ],
      "id": "expired-province"
    },
    {
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      "execution_count": 18,
      "metadata": {
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        "outputId": "c394c830-e53c-4946-c718-c80161c24b5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision 0.7985546980053372\n",
            "Recall 0.7870747833773197\n",
            "Fscore 0.7925186303770153\n"
          ]
        }
      ],
      "source": [
        "precision, recall, fscore, _ = precision_recall_fscore_support(true_labels, predictions, average=\"macro\")\n",
        "print(\"Precision\", precision)\n",
        "print(\"Recall\", recall)\n",
        "print(\"Fscore\", fscore)"
      ],
      "id": "unable-orientation"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "buried-genome"
      },
      "outputs": [],
      "source": [
        ""
      ],
      "id": "buried-genome"
    }
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