Text_clickbait_detection_bert.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/navneetkrc/Flair_SOTA_NLP/blob/master/Text_clickbait_detection_bert.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZYHP1tWS-sR",
        "colab_type": "text"
      },
      "source": [
        "# Text Classification Task using BERT (ClickBait detection task)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwJfvEP5S-sV",
        "colab_type": "text"
      },
      "source": [
        "In this example we will demonstrate how to fine-tune and evaluate the BERT model on the ClickBait dataset using PyTorchWrapper."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7SiLdpa7S-sW",
        "colab_type": "text"
      },
      "source": [
        "#### Downloading Data\n",
        "First of all we download and extract the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTFhK_d6S-sY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir -p data/\n",
        "\n",
        "! wget -P data/ https://github.com/bhargaviparanjape/clickbait/raw/master/dataset/clickbait_data.gz\n",
        "! wget -P data/ https://github.com/bhargaviparanjape/clickbait/raw/master/dataset/non_clickbait_data.gz\n",
        "\n",
        "! gunzip -f data/clickbait_data.gz \n",
        "! gunzip -f data/non_clickbait_data.gz \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FGMyvD-7S-sc",
        "colab_type": "text"
      },
      "source": [
        "#### Additional libraries\n",
        "\n",
        "Next we need to install the `tranformers` library in order use the pretrained BERT model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jWKtI0tS-sd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPHAdQhSTlw8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install pytorch_wrapper"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFzFW_2TS-sf",
        "colab_type": "text"
      },
      "source": [
        "#### Import Statements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RngwsEXfS-sg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        },
        "outputId": "36cef0ef-733f-484b-ef93-c79de93ba34c"
      },
      "source": [
        "import torch\n",
        "import os\n",
        "import random\n",
        "import math\n",
        "\n",
        "from torch import nn\n",
        "from collections import Counter\n",
        "from glob import glob\n",
        "from torch.utils.data.dataset import Dataset\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "from torch.utils.data.sampler import SequentialSampler, SubsetRandomSampler\n",
        "from tqdm.auto import tqdm\n",
        "from transformers import BertModel, BertTokenizer\n",
        "\n",
        "from pytorch_wrapper import modules, System\n",
        "from pytorch_wrapper import functional as pwF\n",
        "from pytorch_wrapper import evaluators as evaluators\n",
        "from pytorch_wrapper.loss_wrappers import GenericPointWiseLossWrapper\n",
        "from pytorch_wrapper.training_callbacks import EarlyStoppingCriterionCallback\n",
        "from pytorch_wrapper.samplers import SubsetOrderedBatchWiseRandomSampler, SubsetOrderedSequentialSampler, \\\n",
        "    OrderedSequentialSampler\n",
        "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
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          "data": {
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              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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      "cell_type": "markdown",
      "metadata": {
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        "colab_type": "text"
      },
      "source": [
        "#### Dataset Definition\n",
        "Next we create the ClickBaitDataset class. We will use the pretrained BPE tokenizer provided by the `transformers` library in order to prepare the input for the BERT model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1fF1pk3S-sk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "source": [
        "bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "\n",
        "class ClickBaitDataset(Dataset):\n",
        "    def __init__(self):\n",
        "        self.ids = []\n",
        "        self.texts = []\n",
        "        self.texts_len = []\n",
        "        self.targets = []\n",
        "\n",
        "        current_id = 0\n",
        "        for filename in ['clickbait_data', 'non_clickbait_data']:\n",
        "            with open(f'data/{filename}') as fw:\n",
        "                for line in tqdm(fw):\n",
        "                    if line == '\\n':\n",
        "                        continue\n",
        "                    self.ids.append(current_id)\n",
        "                    text = bert_tokenizer.encode(line.lower(), add_special_tokens=True)\n",
        "                    self.texts.append(text)\n",
        "                    self.texts_len.append(len(text))\n",
        "                    self.targets.append(filename == 'clickbait_data')\n",
        "\n",
        "        self._shuffle_examples()\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "\n",
        "        return (\n",
        "            self.ids[index],\n",
        "            (\n",
        "                self.texts[index],\n",
        "                self.texts_len[index]\n",
        "            ),\n",
        "            self.targets[index]\n",
        "        )\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.ids)\n",
        "\n",
        "    def _shuffle_examples(self, seed=12345):\n",
        "        \"\"\"\n",
        "        Shuffles the examples with the given seed.\n",
        "        :param seed: The seed used for shuffling.\n",
        "        \"\"\"\n",
        "        random.seed(seed)\n",
        "        l = list(zip(self.ids, self.texts, self.texts_len, self.targets))\n",
        "        random.shuffle(l)\n",
        "        self.ids, self.texts, self.texts_len, self.targets = zip(*l)\n",
        "\n",
        "    @staticmethod\n",
        "    def collate_fn(batch):\n",
        "        \"\"\"\n",
        "        Function that combines a list of examples into a batch (Called internally by dataloaders).\n",
        "        \"\"\"\n",
        "        batch_zipped = list(zip(*batch))\n",
        "        input_zipped = list(zip(*batch_zipped[1]))\n",
        "\n",
        "        ids = batch_zipped[0]\n",
        "        texts = torch.tensor(ClickBaitDataset.pad_to_max(input_zipped[0]), dtype=torch.long)\n",
        "        texts_len = torch.tensor(input_zipped[1], dtype=torch.int)\n",
        "        targets = torch.tensor(batch_zipped[2], dtype=torch.float)\n",
        "\n",
        "        return {\n",
        "\n",
        "            'id': ids,\n",
        "            'input': [texts, texts_len],\n",
        "            'target': targets\n",
        "        }\n",
        "\n",
        "    @staticmethod\n",
        "    def pad_to_max(lst, max_len=None, pad_int=0):\n",
        "        \"\"\"\n",
        "        Pads the given list of list of tokens to the maximum length.\n",
        "        :param lst: List of list of tokens.\n",
        "        \"\"\"\n",
        "        pad = len(max(lst, key=len))\n",
        "        if max_len is not None:\n",
        "            pad = min(max_len, pad)\n",
        "\n",
        "        return [i + [pad_int] * (pad - len(i)) if len(i) <= pad else i[:pad] for i in lst]\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
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            "\n"
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sb5TbT79S-sn",
        "colab_type": "text"
      },
      "source": [
        "#### Model Definition\n",
        "In this example we will use the pretrained base uncased BERT model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqkwPymdS-so",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class BERTModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(BERTModel, self).__init__()\n",
        "\n",
        "        self.bert = BertModel.from_pretrained('bert-base-uncased')\n",
        "        self.output_linear = nn.Linear(768, 1)\n",
        "\n",
        "    def forward(self, text, text_len):\n",
        "        bert_last_hidden_states = self.bert(text)[0]\n",
        "        mask = pwF.create_mask_from_length(text_len, bert_last_hidden_states.shape[-2], zeros_at_end=True)\n",
        "        encoding = pwF.masked_mean_pooling(bert_last_hidden_states, mask, -2)\n",
        "        return self.output_linear(encoding).squeeze(-1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehMpKtzAS-sq",
        "colab_type": "text"
      },
      "source": [
        "#### Training\n",
        "\n",
        "Next we create the dataset object along with three data loaders (for training, validation, and testing). We will also make use of `SubsetOrderedBatchWiseRandomSampler` and `SubsetOrderedSequentialSampler` in order to batch together texts with similar lengths."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YY03sK82S-sr",
        "colab_type": "code",
        "colab": {
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      "source": [
        "train_val_test_dataset = ClickBaitDataset()\n",
        "\n",
        "eval_size = math.floor(0.1 * len(train_val_test_dataset))\n",
        "train_val_test_dataset_indexes = list(range(len(train_val_test_dataset)))\n",
        "train_split_indexes = train_val_test_dataset_indexes[2 * eval_size:]\n",
        "val_split_indexes = train_val_test_dataset_indexes[eval_size:2 * eval_size]\n",
        "test_split_indexes = train_val_test_dataset_indexes[:eval_size]\n",
        "\n",
        "batch_size = 32\n",
        "train_dataloader = DataLoader(\n",
        "    train_val_test_dataset,\n",
        "    sampler=SubsetOrderedBatchWiseRandomSampler(\n",
        "        train_split_indexes,\n",
        "        get_order_value_callable=lambda example_index: train_val_test_dataset[example_index][1][1],\n",
        "        batch_size=batch_size\n",
        "    ),\n",
        "    batch_size=batch_size,\n",
        "    collate_fn=ClickBaitDataset.collate_fn\n",
        ")\n",
        "\n",
        "val_dataloader = DataLoader(\n",
        "    train_val_test_dataset,\n",
        "    sampler=SubsetOrderedSequentialSampler(\n",
        "        val_split_indexes,\n",
        "        get_order_value_callable=lambda example_index: train_val_test_dataset[example_index][1][1]\n",
        "    ),\n",
        "    batch_size=batch_size,\n",
        "    collate_fn=ClickBaitDataset.collate_fn\n",
        ")\n",
        "\n",
        "test_dataloader = DataLoader(\n",
        "    train_val_test_dataset,\n",
        "    sampler=SubsetOrderedSequentialSampler(\n",
        "        test_split_indexes,\n",
        "        get_order_value_callable=lambda example_index: train_val_test_dataset[example_index][1][1]\n",
        "    ),\n",
        "    batch_size=batch_size,\n",
        "    collate_fn=ClickBaitDataset.collate_fn\n",
        ")\n"
      ],
      "execution_count": 8,
      "outputs": [
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      "cell_type": "markdown",
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      "source": [
        "Then we create the model and we wrap it with a `System` object."
      ]
    },
    {
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      "source": [
        "model = BERTModel()\n",
        "\n",
        "last_activation = nn.Sigmoid()\n",
        "if torch.cuda.is_available():\n",
        "    system = System(model, last_activation=last_activation, device=torch.device('cuda'))\n",
        "else:\n",
        "    system = System(model, last_activation=last_activation, device=torch.device('cpu'))\n"
      ],
      "execution_count": 9,
      "outputs": [
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        {
          "output_type": "display_data",
          "data": {
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              "model_id": "c21b782fd5444de4af1066e91c9b79e6",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1weefyF-S-sz",
        "colab_type": "text"
      },
      "source": [
        "Next we finetune the model on the training set, using a small learning rate (0.00005)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWO18kKwS-s0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "9ce7e08662f14c11b99854414b22a83b"
          ]
        },
        "outputId": "1030d51c-2586-459c-f75e-8495681315d0"
      },
      "source": [
        "loss_wrapper = GenericPointWiseLossWrapper(nn.BCEWithLogitsLoss())\n",
        "evals = {\n",
        "\n",
        "    'acc': evaluators.AccuracyEvaluator(),\n",
        "    'f1': evaluators.F1Evaluator(),\n",
        "    'auc': evaluators.AUROCEvaluator()\n",
        "\n",
        "}\n",
        "\n",
        "optimizer = torch.optim.Adam(\n",
        "    filter(lambda p: p.requires_grad, system.model.parameters()),\n",
        "    lr=0.00005\n",
        ")\n",
        "\n",
        "_ = system.train(\n",
        "    loss_wrapper,\n",
        "    optimizer,\n",
        "    train_data_loader=train_dataloader,\n",
        "    evaluators=evals,\n",
        "    evaluation_data_loaders={\n",
        "        'val': val_dataloader\n",
        "    },\n",
        "    callbacks=[\n",
        "        EarlyStoppingCriterionCallback(\n",
        "            patience=3,\n",
        "            evaluation_data_loader_key='val',\n",
        "            evaluator_key='f1',\n",
        "            tmp_best_state_filepath='data/click_bait_cur_best.weights'\n",
        "        )\n",
        "    ]\n",
        ")\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Epoch: 0\n",
            "\n",
            "Training...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a54bf82cd5ec42fabcceb285c42cafe6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=800.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Time elapsed: 4052\n",
            "\n",
            "Evaluating...\n",
            "\n",
            "val\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b67db3b02c7f449fae6074b72bfcdfef",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "acc: 98.84%\n",
            "binary-f1: 0.9883\n",
            "auroc: 0.9995\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Epoch: 1\n",
            "\n",
            "Training...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ec8137b549354402b6247c75755411f1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=800.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Time elapsed: 4067\n",
            "\n",
            "Evaluating...\n",
            "\n",
            "val\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b1fe99ef46f4c1a99548d54bbe983e1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "acc: 98.59%\n",
            "binary-f1: 0.9857\n",
            "auroc: 0.9992\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Epoch: 2\n",
            "\n",
            "Training...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            },
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              "HBox(children=(FloatProgress(value=0.0, max=800.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Time elapsed: 4082\n",
            "\n",
            "Evaluating...\n",
            "\n",
            "val\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "849b9e3cd4da4bc3b9f0fd9417d9c0f0",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "acc: 98.84%\n",
            "binary-f1: 0.9882\n",
            "auroc: 0.9993\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Epoch: 3\n",
            "\n",
            "Training...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e97e168c69e4caa91dd9cdc17316d8f",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, max=800.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Time elapsed: 4098\n",
            "\n",
            "Evaluating...\n",
            "\n",
            "val\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cbebf607baf44bc1b520e9b5919abadf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "acc: 98.84%\n",
            "binary-f1: 0.9883\n",
            "auroc: 0.9994\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Epoch: 4\n",
            "\n",
            "Training...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f6a8dc0ceb24e04a92bac999fef7f6a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=800.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Time elapsed: 4083\n",
            "\n",
            "Evaluating...\n",
            "\n",
            "val\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88492c03a4cd4198b6ee776e87d8974a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "acc: 98.59%\n",
            "binary-f1: 0.9858\n",
            "auroc: 0.9987\n",
            "\n",
            "Epoch chosen: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjmHwmiOS-s3",
        "colab_type": "text"
      },
      "source": [
        "Next we evaluate the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPxfNOTOS-s4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "977655954cfc4a708c71a138fed672c6",
            "79585d1e0df04b68b836802983dc1660",
            "73a35081b7464f5db5844ce71f1bb37b",
            "d886c9b501bd42cf9f34754d162d805c",
            "1dbec1c50715424a9bb7dc1c6a991dc7",
            "580e13e1e61646e4a5e5b99c54ba98aa",
            "ea5f69f114b8411db26834c26339b0c8",
            "214bb669709d4008afd9629a53d629aa"
          ]
        },
        "outputId": "efe41bc0-f420-49f7-df48-d3a5c7e064da"
      },
      "source": [
        "results = system.evaluate(test_dataloader, evals)\n",
        "for r in results:\n",
        "    print(results[r])\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "977655954cfc4a708c71a138fed672c6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "acc: 99.12%\n",
            "binary-f1: 0.9913\n",
            "auroc: 0.9992\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onDTW_jSS-s6",
        "colab_type": "text"
      },
      "source": [
        "We can also use the `predict` method in order to predict for all the examples returned by a `Dataloder`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-u03_7CS-s7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "9fa792abfcc244f2b419bb0300e19ff5",
            "791cfcab840c495cb66af0bb884826d5",
            "895385fd0b6c49a7b8e6363b0e33ab0b",
            "ad98e425547945e8b906e000a207f451",
            "9c0942044d594c228627235be65cc5c3",
            "037ddc0864a3418a8afe68371b3aa9ef",
            "3171b4ca39224809ae492b9192ee8030",
            "c554057aa2f74227af18853fc122e43c"
          ]
        },
        "outputId": "0c516742-8c14-430b-feab-05c4936f1d6e"
      },
      "source": [
        "predictions = system.predict(test_dataloader, perform_last_activation=True)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9fa792abfcc244f2b419bb0300e19ff5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dr8pXQ_fS-s9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a052fbff-ef00-4e97-b41d-309363bf18ea"
      },
      "source": [
        "example_id = 6\n",
        "input_loc = 1\n",
        "text_loc = 0\n",
        "\n",
        "print(bert_tokenizer.decode(train_val_test_dataset[example_id][input_loc][text_loc]))\n",
        "print(predictions['outputs'][example_id])\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[CLS] 7 very easy ways to eat healthier this week [SEP]\n",
            "0.0005689897807314992\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGWQGr6lrFPI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "da313e93-bf81-4f7f-d12b-d76b389f20ab"
      },
      "source": [
        "example_id = 4\n",
        "input_loc = 1\n",
        "text_loc = 0\n",
        "\n",
        "print(bert_tokenizer.decode(train_val_test_dataset[example_id][input_loc][text_loc]))\n",
        "print(predictions['outputs'][example_id])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[CLS] which classical composer best suits your taste [SEP]\n",
            "0.9998045563697815\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qd2Cq8gyS-tA",
        "colab_type": "text"
      },
      "source": [
        "Finally we save the model's weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQmuAXkXS-tB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "system.save_model_state('data/click_bait_final.weights')\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}