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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLysUG6meK5h"
      },
      "source": [
        "### Download from google disk preprocessed data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Upi2G_jzdOq8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7a6b9ef-9555-4929-dd3a-5e15f6caa66d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTXPgdnkdXaf"
      },
      "source": [
        "import shutil"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "BAD5eaKVdaPA",
        "outputId": "46f92ca7-d139-4e77-caba-393b4ace1adc"
      },
      "source": [
        "shutil.copy('.//drive//MyDrive//challenge//train.jsonl', './/')\n",
        "shutil.copy('.//drive//MyDrive//challenge//test.jsonl', './/')\n",
        "shutil.copy('.//drive//MyDrive//challenge//val.jsonl', './/')"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'.//val.jsonl'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBrwNDFNwGwo"
      },
      "source": [
        "### Imports and device and download stopwords"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbpKU9q-Dt8G"
      },
      "source": [
        "import json\n",
        "\n",
        "import torch\n",
        "import nltk\n",
        "import numpy as np\n",
        "import gensim.downloader as api\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import WordPunctTokenizer\n",
        "import spacy\n",
        "\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn.functional as F\n",
        "from tqdm.autonotebook import tqdm"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5uR-gqPv06B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5bf0b7e-64fa-4237-ac45-0900c4f5e28b"
      },
      "source": [
        "nltk.download('stopwords')\n",
        "nlp = spacy.load('en')"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHiErVv309pU"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKh1ZPIVwXMg"
      },
      "source": [
        "### Embedding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nd2ceDQo8PdQ"
      },
      "source": [
        "# word2vec = api.load('word2vec-google-news-300')\n",
        "word2vec = api.load('glove-wiki-gigaword-300')\n",
        "vocab_size = len(word2vec.vocab)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fm6vccg_yEb1"
      },
      "source": [
        "### RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdheu6GxyJUQ"
      },
      "source": [
        "class RNNBaseline(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, \n",
        "                 bidirectional, dropout, pad_idx, dropout_clf):\n",
        "        \n",
        "        super().__init__()\n",
        "\n",
        "        self.n_layers = n_layers\n",
        "        self.bidirectional = bidirectional\n",
        "        self.hidden_dim = hidden_dim\n",
        "        \n",
        "        self.embedding = nn.Embedding(vocab_size,\n",
        "                                      embedding_dim,\n",
        "                                      padding_idx=pad_idx)\n",
        "        \n",
        "        self.rnn_for_seq1 = nn.LSTM(embedding_dim,\n",
        "                           hidden_dim,\n",
        "                           num_layers=n_layers,\n",
        "                           dropout=dropout,\n",
        "                           bidirectional=bidirectional)\n",
        "        self.rnn_for_seq2 = nn.LSTM(embedding_dim,\n",
        "                           hidden_dim,\n",
        "                           num_layers=n_layers,\n",
        "                           dropout=dropout,\n",
        "                           bidirectional=bidirectional)        \n",
        "        self.dropout = nn.Dropout(p=dropout_clf)\n",
        "        self.fc1 = nn.Linear(hidden_dim * n_layers * (2 if bidirectional else 1) * 4, 256)\n",
        "        self.fc2 = nn.Linear(256, output_dim)\n",
        "        self.activ = nn.ReLU()\n",
        "        \n",
        "        \n",
        "    def forward(self, text):\n",
        "        \n",
        "        embedded1 = self.embedding(text['seq1'].permute(1, 0))\n",
        "        embedded2 = self.embedding(text['seq2'].permute(1, 0))\n",
        "\n",
        "        h0 = torch.zeros(self.n_layers * (2 if self.bidirectional else 1), embedded1.size(1), self.hidden_dim).to(text['seq2'][0].device)\n",
        "        c0 = torch.zeros(self.n_layers * (2 if self.bidirectional else 1), embedded1.size(1), self.hidden_dim).to(text['seq2'][0].device)\n",
        "        \n",
        "        output1, (hidden1, cell1) = self.rnn_for_seq1(embedded1, (h0, c0))\n",
        "        output2, (hidden2, cell2) = self.rnn_for_seq2(embedded2, (h0, c0))\n",
        "\n",
        "        hidden1 = hidden1.permute(1, 0, 2)\n",
        "        hidden2 = hidden2.permute(1, 0, 2)\n",
        "\n",
        "        hidden = torch.cat((hidden1, hidden1 * hidden2, torch.abs(hidden1 - hidden2), hidden2), dim=1).view(embedded1.size(1), -1)\n",
        "\n",
        "        hidden = self.dropout(hidden)\n",
        "        hidden = self.fc1(hidden)\n",
        "        hidden = self.activ(hidden)\n",
        "        hidden = self.dropout(hidden)\n",
        "        hidden = self.fc2(hidden)\n",
        "\n",
        "        return hidden"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggTiORJ-8t0J"
      },
      "source": [
        "emb_dim = 300\n",
        "hidden_dim = 256\n",
        "output_dim = 3\n",
        "n_layers = 1\n",
        "bidirectional = True\n",
        "dropout = 0.5\n",
        "PAD_IDX = 1\n",
        "patience = 3\n",
        "dropout_clf = 0.5"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIiM_ZBt9_91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af2e28ea-b88c-47d7-de2c-dec4ffb96911"
      },
      "source": [
        "model = RNNBaseline(\n",
        "    vocab_size=vocab_size,\n",
        "    embedding_dim=emb_dim,\n",
        "    hidden_dim=hidden_dim,\n",
        "    output_dim=output_dim,\n",
        "    n_layers=n_layers,\n",
        "    bidirectional=bidirectional,\n",
        "    dropout=dropout,\n",
        "    pad_idx=PAD_IDX,\n",
        "    dropout_clf=dropout_clf\n",
        ")"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:63: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1\n",
            "  \"num_layers={}\".format(dropout, num_layers))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjDPm6Owzr65"
      },
      "source": [
        "model.embedding.weight = nn.Parameter(torch.FloatTensor(word2vec.vectors))"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFeG88M--NbD"
      },
      "source": [
        "model = model.to(device)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olAS-mVI-VfT"
      },
      "source": [
        "opt = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
        "loss_func = nn.CrossEntropyLoss()\n",
        "max_epochs = 20"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtZsf3Fx5lJw"
      },
      "source": [
        "### Datasets and DataLoaders on corpus for RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAUqgDCb5nJS"
      },
      "source": [
        "class TextDatasetRNN(Dataset):\n",
        "    def __init__(self, dir_json, word2vec, max_length, tokenizer=None):\n",
        "        self.label_map = {'neutral':torch.tensor(0, dtype=torch.long),\n",
        "                          'contradiction':torch.tensor(1, dtype=torch.long),\n",
        "                          'entailment':torch.tensor(2, dtype=torch.long)}\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "        self.word2vec = word2vec\n",
        "\n",
        "        data = []\n",
        "        broken_data = []\n",
        "        with open(dir_json) as json_file:\n",
        "            for i, line in enumerate(json_file):\n",
        "                line = json.loads(line)\n",
        "                try:\n",
        "                    line_data = {\n",
        "                        'seq1':self.preprocessing_sequence(line['sentence1']),\n",
        "                        'seq2':self.preprocessing_sequence(line['sentence2']),\n",
        "                        'label':self.label_map[line['gold_label']]                        \n",
        "                    }\n",
        "                    data.append(line_data)\n",
        "                except (ValueError, KeyError):\n",
        "                    broken_data.append((i, line))\n",
        "        self.json_data = data\n",
        "        self.broken = broken_data\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.json_data)\n",
        "\n",
        "    def __getitem__(self, ind):\n",
        "        return self.json_data[ind]\n",
        "    \n",
        "    def preprocessing_sequence(self, seq):\n",
        "        tokens = [t for t in self.tokenizer.tokenize(seq.lower())]\n",
        "        embedings = [self.word2vec.index2word.index(t) for t in tokens if t in self.word2vec]\n",
        "        lenght = len(embedings)\n",
        "        if lenght == 0:\n",
        "            raise ValueError\n",
        "        if len(embedings) < self.max_length:\n",
        "            embedings.extend([1 for i in range(self.max_length - len(embedings))])\n",
        "        else:\n",
        "            lenght = self.max_length\n",
        "            embedings = embedings[:self.max_length]\n",
        "        return torch.from_numpy(np.array(embedings)).type(torch.long), torch.tensor(lenght, dtype=torch.long)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKWr55Pc5nJh"
      },
      "source": [
        "train_datasetRNN = TextDatasetRNN(dir_json='.//train.jsonl',\n",
        "                            tokenizer=WordPunctTokenizer(),\n",
        "                            word2vec=word2vec,\n",
        "                            max_length=10)\n",
        "val_datasetRNN = TextDatasetRNN('.//val.jsonl',\n",
        "                          tokenizer=WordPunctTokenizer(),\n",
        "                          word2vec=word2vec,\n",
        "                          max_length=10)\n",
        "test_datasetRNN = TextDatasetRNN('.//test.jsonl',\n",
        "                           tokenizer=WordPunctTokenizer(),\n",
        "                           word2vec=word2vec,\n",
        "                           max_length=10)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQ8Nz9G55nJh"
      },
      "source": [
        "train_loaderRNN = DataLoader(train_datasetRNN, batch_size=1000, shuffle=True)\n",
        "val_loaderRNN = DataLoader(val_datasetRNN, batch_size=1000)\n",
        "test_loaderRNN = DataLoader(test_datasetRNN, batch_size=1000)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aV27VygexYYi"
      },
      "source": [
        "### Info about GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8k7FLyPsLCwK"
      },
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zq1txKIrLIZc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f10e0d7-155a-470b-9427-5bb78e58e2bb"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Apr  9 08:22:39 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.67       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   51C    P0    28W /  70W |   1544MiB / 15109MiB |     10%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFSxfuj23887"
      },
      "source": [
        "### Training RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3d9s4XclPry"
      },
      "source": [
        "def freeze_word2vec(model, grad=False):\n",
        "    for x in model.embedding.parameters():\n",
        "        x.requires_grad = grad"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEbN6FCy4Ev6"
      },
      "source": [
        "max_epochs = 30\n",
        "patience = 3"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87dgw6ok9hR0",
        "colab": {
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          ]
        },
        "outputId": "6e77e7c3-0c70-457a-8760-56dc5d58b9a1"
      },
      "source": [
        "min_loss = np.inf\n",
        "\n",
        "cur_patience = 0\n",
        "\n",
        "for epoch in range(1, max_epochs + 1):\n",
        "    if epoch < 5:\n",
        "        freeze_word2vec(model)\n",
        "    else:\n",
        "        freeze_word2vec(model, True)        \n",
        "    train_loss = 0.0\n",
        "    model.train()\n",
        "    pbar = tqdm(enumerate(train_loaderRNN), total=len(train_loaderRNN), leave=False)\n",
        "    pbar.set_description(f\"Epoch {epoch}\")\n",
        "    for it, batch in pbar:\n",
        "        with torch.set_grad_enabled(True):\n",
        "\n",
        "            opt.zero_grad()\n",
        "            label = batch['label'].to(device)\n",
        "            data = {\n",
        "                'seq1':batch['seq1'][0].to(device),\n",
        "                'seq2':batch['seq2'][0].to(device),\n",
        "            }\n",
        "            output = model(data)\n",
        "\n",
        "            loss = loss_func(output, label)\n",
        "            loss.backward()\n",
        "            train_loss += loss.item()\n",
        "\n",
        "            opt.step()\n",
        "\n",
        "    train_loss /= len(train_loaderRNN)\n",
        "\n",
        "    val_loss = 0.0\n",
        "    model.eval()\n",
        "\n",
        "    pbar = tqdm(enumerate(val_loaderRNN), total=len(val_loaderRNN), leave=False)\n",
        "    pbar.set_description(f\"Epoch {epoch}\")\n",
        "    for it, batch in pbar:\n",
        "        # model(it.to(device))\n",
        "\n",
        "        with torch.no_grad():\n",
        "            label = batch['label'].to(device)\n",
        "            data = {\n",
        "                'seq1':batch['seq1'][0].to(device),\n",
        "                'seq2':batch['seq2'][0].to(device),\n",
        "            }\n",
        "            output = model(data)\n",
        "\n",
        "            loss = loss_func(output, label)\n",
        "            val_loss += loss.item()        \n",
        "\n",
        "    val_loss /= len(val_loaderRNN)\n",
        "    if val_loss < min_loss:\n",
        "        min_loss = val_loss\n",
        "        best_model = model.state_dict()\n",
        "    else:\n",
        "        cur_patience += 1\n",
        "        if cur_patience == patience:\n",
        "            cur_patience = 0\n",
        "            break\n",
        "    \n",
        "    print(f'Epoch: {epoch}, Training Loss: {train_loss:.6f}, Validation Loss: {val_loss:.6f}')\n",
        "model.load_state_dict(best_model)"
      ],
      "execution_count": 93,
      "outputs": [
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 0.888178, Validation Loss: 0.778381\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
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          "text": [
            "Epoch: 2, Training Loss: 0.764449, Validation Loss: 0.715644\n"
          ],
          "name": "stdout"
        },
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        {
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          "text": [
            "Epoch: 3, Training Loss: 0.718856, Validation Loss: 0.679882\n"
          ],
          "name": "stdout"
        },
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        {
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          "text": [
            "Epoch: 4, Training Loss: 0.689672, Validation Loss: 0.652491\n"
          ],
          "name": "stdout"
        },
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        {
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          "text": [
            "Epoch: 5, Training Loss: 0.658432, Validation Loss: 0.635241\n"
          ],
          "name": "stdout"
        },
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            "Epoch: 6, Training Loss: 0.625508, Validation Loss: 0.601217\n"
          ],
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            "Epoch: 7, Training Loss: 0.597686, Validation Loss: 0.592997\n"
          ],
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            "Epoch: 8, Training Loss: 0.575295, Validation Loss: 0.583982\n"
          ],
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            "Epoch: 9, Training Loss: 0.554769, Validation Loss: 0.580002\n"
          ],
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          "text": [
            "Epoch: 10, Training Loss: 0.536312, Validation Loss: 0.576852\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bef9e76ec3cb4477a483c71194916333",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=550.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c98a4bfc15814b9db5133b0e23d0b755",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 11, Training Loss: 0.518996, Validation Loss: 0.572651\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "230e7ddd27444f3d97bb1557a2f25fbb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=550.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b15c60d846714e14825c5429ced4ebe1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 12, Training Loss: 0.503467, Validation Loss: 0.568517\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c69c86796a9e4d45b3b40c46012903d0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=550.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2e8de71124d46e2938acc8e322d889c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 13, Training Loss: 0.486997, Validation Loss: 0.567727\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cb7d496859114b918c93643e497decd0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=550.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6743225137d2466eadd702d94b2741b3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 14, Training Loss: 0.472679, Validation Loss: 0.577422\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4415c724627346908896056471819fb2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=550.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3aafe1b82d748f9ac6496be6f85bd01",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 15, Training Loss: 0.458805, Validation Loss: 0.574813\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "076a006128cc4b9d8600462a279b8bc3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=550.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0067e8af4a6643b79c7b963e22a98612",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzFQdWffvHng"
      },
      "source": [
        "### Accuracy on RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cx9vIcmIvQhg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0c23c6c-39fa-4610-9ba4-c09d29cab73c"
      },
      "source": [
        "count = 0\n",
        "corr = 0\n",
        "for batch in test_loaderRNN:\n",
        "    with torch.no_grad():\n",
        "        label = batch['label'].to(device)\n",
        "        data = {\n",
        "            'seq1':batch['seq1'][0].to(device),\n",
        "            'seq2':batch['seq2'][0].to(device)\n",
        "            }\n",
        "        output = model(data)\n",
        "        pred = torch.argmax(output, dim=1)\n",
        "        count += pred.shape[0]\n",
        "        corr += torch.sum(pred == label).cpu()\n",
        "print(corr / (count))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(0.7689)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jp4AoEqgvQhj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdccc16f-b890-49af-9ff5-113fb1aaf132"
      },
      "source": [
        "count = 0\n",
        "corr = 0\n",
        "for batch in val_loaderRNN:\n",
        "    with torch.no_grad():\n",
        "        label = batch['label'].to(device)\n",
        "        data = {\n",
        "            'seq1':batch['seq1'][0].to(device),\n",
        "            'seq2':batch['seq2'][0].to(device)\n",
        "            }\n",
        "        output = model(data)\n",
        "        pred = torch.argmax(output, dim=1)\n",
        "        count += pred.shape[0]\n",
        "        corr += torch.sum(pred == label).cpu()\n",
        "print(corr / (count))"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(0.7706)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfbHT2ocolhM",
        "outputId": "53118777-127f-4c8b-a58a-fa0a8f63a1cf"
      },
      "source": [
        "count = 0\n",
        "corr = 0\n",
        "for batch in test_loaderRNN:\n",
        "    with torch.no_grad():\n",
        "        label = batch['label'].to(device)\n",
        "        data = {\n",
        "            'seq1':batch['seq1'][0].to(device),\n",
        "            'seq2':batch['seq2'][0].to(device)\n",
        "            }\n",
        "        output = model(data)\n",
        "        pred = torch.argmax(output, dim=1)\n",
        "        count += pred.shape[0]\n",
        "        corr += torch.sum(pred == label).cpu()\n",
        "print(corr / (count))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(0.7689)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7H_pJCZKxk0d"
      },
      "source": [
        "### Save model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MFKX3mwe-Bi"
      },
      "source": [
        "torch.save(model.state_dict(), './/best_modelRNN.pt')"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "et3hhoiBZaLE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8db1dd17-147d-46bb-e219-e51a7864c01a"
      },
      "source": [
        "shutil.copy('.//best_modelRNN.pt', './/drive//MyDrive//challenge//')"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'.//drive//MyDrive//challenge//best_modelRNN.pt'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RmlRyGGnC7h"
      },
      "source": [
        "model.load_state_dict(torch.load('.//drive//MyDrive//challenge//best_modelRNN.pt'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImNV1AYx1HLl"
      },
      "source": [
        "### F1 Score on test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NMGxJIPBqUs_"
      },
      "source": [
        "from sklearn.metrics import f1_score, classification_report"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4s9RX3z3Ub-"
      },
      "source": [
        "preds = np.array([])\n",
        "true = np.array([])"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "475a31b9c5dc412c94ba5affda596074",
            "759a865177d343029668dfbeebec36df",
            "decacdf304fc413abd93c006d5c3925f",
            "adcaaca6b3b643328967315f77672dd3",
            "c02844983a4a4ba5939308e56f372a36",
            "b7ff74d4ba25455c87b14928de6a016d",
            "c3882bf6b1ec4de48152c0b9e129a9a6",
            "468d1e276ab44101a5d0382ddde068d8"
          ]
        },
        "id": "FNgdnq5T3O1A",
        "outputId": "d66afdec-3767-4c84-ac81-f5939e643112"
      },
      "source": [
        "with torch.no_grad():\n",
        "    pbar = tqdm(enumerate(test_loaderRNN), total=len(test_loaderRNN), leave=False)\n",
        "    for it, batch in pbar:\n",
        "        label = batch['label'].cpu()\n",
        "        data = {\n",
        "            'seq1':batch['seq1'][0].to(device),\n",
        "            'seq2':batch['seq2'][0].to(device)\n",
        "        }        \n",
        "        output = model(data)\n",
        "        preds = np.concatenate((preds, torch.argmax(output, dim=1).cpu().numpy()))\n",
        "        true = np.concatenate((true, label.numpy()))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "475a31b9c5dc412c94ba5affda596074",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0G3Zyy2AgJ4F",
        "outputId": "116bd6cb-8f9d-437b-a2ea-1f29cd0e2f72"
      },
      "source": [
        "print(classification_report(true, preds))"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.72      0.74      0.73      3219\n",
            "         1.0       0.80      0.79      0.80      3237\n",
            "         2.0       0.79      0.77      0.78      3368\n",
            "\n",
            "    accuracy                           0.77      9824\n",
            "   macro avg       0.77      0.77      0.77      9824\n",
            "weighted avg       0.77      0.77      0.77      9824\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}