PhoBERT_CNN_for_UIT_HSD.ipynb

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        "outputId": "46d38a9c-3132-45fd-a247-7009cf7a190e"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers -qq\n",
        "!pip install sentencepiece -qq\n",
        "!pip install tokenizer -qq"
      ],
      "metadata": {
        "id": "tL1iFjECh3_6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Load Dataset"
      ],
      "metadata": {
        "id": "C-pzooQXh5S7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WTMXPNE2W6NJ"
      },
      "outputs": [],
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv('/content/train.csv')\n",
        "dev = pd.read_csv('/content/dev.csv')\n",
        "test = pd.read_csv('/content/test.csv')\n",
        "\n",
        "\n",
        "train['free_text'] = train['free_text'].astype(str)\n",
        "X_train = train['free_text']\n",
        "y_train = train[\"label_id\"]\n",
        "\n",
        "dev['free_text'] = dev['free_text'].astype(str)\n",
        "X_valid = dev['free_text']\n",
        "y_valid = dev['label_id']\n",
        "\n",
        "test['free_text'] = test['free_text'].astype(str)\n",
        "X_test = test['free_text']\n",
        "y_test = test['label_id']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "La-lPuGjCxbb"
      },
      "source": [
        "## Set Cuda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SnuQNrQ5Cxi_"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torchtext import data\n",
        "from torchtext import datasets\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "SEED = 1234\n",
        "\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "\n",
        "torch.cuda.is_available()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Extract feature by using PhoBERT"
      ],
      "metadata": {
        "id": "gHV2PrVfiBLg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MreD1ev6AgXM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from glob import glob\n",
        "\n",
        "train_sentences = list(train['free_text'].values)\n",
        "train_labels = list(train['label_id'].values)\n",
        "\n",
        "dev_sentences = list(dev['free_text'].values)\n",
        "dev_labels = list(dev['label_id'].values)\n",
        "\n",
        "test_sentences = list(test['free_text'].values)\n",
        "test_labels = list(test['label_id'].values)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load tokenizer of PhoBERT"
      ],
      "metadata": {
        "id": "rNVMN4YPiNjp"
      }
    },
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained('vinai/phobert-base')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5PNp5aRVA-Hb"
      },
      "outputs": [],
      "source": [
        "#choose max_length for phobert model based on the input length\n",
        "\n",
        "max_length = 0\n",
        "list_len=[]\n",
        "for sentence in train_sentences:\n",
        "    length = len(tokenizer.tokenize(sentence))\n",
        "    list_len.append(length)\n",
        "    \n",
        "from collections import Counter\n",
        "Counter(list_len).most_common(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dX5lBYCTD9bU"
      },
      "outputs": [],
      "source": [
        "# Encode train label\n",
        "\n",
        "from sklearn import preprocessing\n",
        "\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(train_labels)\n",
        "encoded_labels = le.transform(train_labels)\n",
        "encoded_test_labels = le.transform(dev_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9XdWNkpFBgH",
        "outputId": "803344d0-31de-499b-ce40-8dfb859f6ba7"
      },
      "outputs": [
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2291: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original:  fan cứng nè ❤️ reaction cute coi mấy hợp lí =] ] ]\n",
            "Token IDs: tensor([    0,  1818,  2951, 21091,     3,     3,  2938, 43230,  5109,  3915,\n",
            "          774,   849,  2288, 12826, 23196,  2705,  2705,  2705,     2,     1],\n",
            "       device='cuda:0')\n"
          ]
        }
      ],
      "source": [
        "# Tokens IDs tensor\n",
        "\n",
        "def encoder_generator(sentences,labels):\n",
        "    \n",
        "    sent_index = []\n",
        "    input_ids = []\n",
        "    attention_masks =[]\n",
        "\n",
        "    for index,sent in enumerate(sentences):\n",
        "        \n",
        "        sent_index.append(index)\n",
        "        \n",
        "        encoded_dict = tokenizer.encode_plus(sent,\n",
        "                                             add_special_tokens=True,\n",
        "                                             max_length=20,\n",
        "                                             pad_to_max_length=True,\n",
        "                                             truncation = True,\n",
        "                                             return_attention_mask=True,\n",
        "                                             return_tensors='pt')\n",
        "        input_ids.append(encoded_dict['input_ids'])\n",
        "\n",
        "        attention_masks.append(encoded_dict['attention_mask'])\n",
        "\n",
        "    input_ids = torch.cat(input_ids,dim=0).cuda()\n",
        "    attention_masks = torch.cat(attention_masks,dim=0).cuda()\n",
        "    labels = torch.tensor(labels).cuda()\n",
        "    sent_index = torch.tensor(sent_index).cuda()\n",
        "\n",
        "    return sent_index,input_ids,attention_masks,labels\n",
        "\n",
        "train_sent_index,train_input_ids,train_attention_masks,train_encoded_label_tensors = encoder_generator(train_sentences,encoded_labels)\n",
        "dev_sent_index,dev_input_ids,dev_attention_masks,dev_encoded_label_tensors = encoder_generator(dev_sentences,encoded_test_labels)\n",
        "print('Original: ', train_sentences[0])\n",
        "print('Token IDs:', train_input_ids[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BYbqZ7NMMWw",
        "outputId": "e93d046d-7eb1-4ccc-fda4-75285808e33b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train data samples is 46882\n",
            "valid data samples is 5441\n"
          ]
        }
      ],
      "source": [
        "# Connvert train, dev input by using TensorDataset\n",
        "\n",
        "from torch.utils.data import TensorDataset,random_split\n",
        "\n",
        "train_dataset = TensorDataset(train_input_ids,train_attention_masks,train_encoded_label_tensors)\n",
        "dev_dataset = TensorDataset(dev_input_ids,dev_attention_masks,dev_encoded_label_tensors)\n",
        "\n",
        "print('train data samples is {}'.format(len(train_dataset)))\n",
        "print(\"valid data samples is {}\".format(len(dev_dataset)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Em9Q1v0BMZZX"
      },
      "outputs": [],
      "source": [
        "# Set cuda by using device\n",
        "\n",
        "from torch.utils.data import DataLoader,RandomSampler,SequentialSampler\n",
        "\n",
        "bs=128\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "train_data_loader = DataLoader(train_dataset,\n",
        "                              sampler=RandomSampler(train_dataset),\n",
        "                              batch_size=bs)\n",
        "valid_data_loader = DataLoader(dev_dataset,\n",
        "                              sampler=RandomSampler(dev_dataset),\n",
        "                              batch_size=bs)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load model PhoBERT"
      ],
      "metadata": {
        "id": "xEtD7kn4jbTl"
      }
    },
    {
      "cell_type": "code",
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at vinai/phobert-base were not used when initializing RobertaModel: ['lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.bias', 'lm_head.decoder.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight']\n",
            "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "phoBert = AutoModel.from_pretrained('vinai/phobert-base')\n",
        "phoBert = phoBert.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build CNN "
      ],
      "metadata": {
        "id": "zEv63LPpjkcK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q44-J-QWMgKQ"
      },
      "outputs": [],
      "source": [
        "# Build class CNN\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self, embedding_dim, n_filters, filter_sizes, output_dim, \n",
        "                 dropout, pad_idx):\n",
        "        \n",
        "        super().__init__()\n",
        "        \n",
        "        self.fc_input = nn.Linear(embedding_dim,embedding_dim)\n",
        "        \n",
        "        self.conv_0 = nn.Conv1d(in_channels = embedding_dim, \n",
        "                                out_channels = n_filters, \n",
        "                                kernel_size = filter_sizes[0])\n",
        "        \n",
        "        self.conv_1 = nn.Conv1d(in_channels = embedding_dim, \n",
        "                                out_channels = n_filters, \n",
        "                                kernel_size = filter_sizes[1])\n",
        "        \n",
        "        self.conv_2 = nn.Conv1d(in_channels = embedding_dim, \n",
        "                                out_channels = n_filters, \n",
        "                                kernel_size = filter_sizes[2])\n",
        "        \n",
        "        self.conv_3 = nn.Conv1d(in_channels = embedding_dim, \n",
        "                                out_channels = n_filters, \n",
        "                                kernel_size = filter_sizes[3])\n",
        "\n",
        "        self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)\n",
        "        \n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        \n",
        "    def forward(self, encoded):\n",
        "                \n",
        "        #embedded = [batch size, sent len, emb dim]\n",
        "        embedded = self.fc_input(encoded)\n",
        "        #print(embedded.shape)\n",
        "        \n",
        "        embedded = embedded.permute(0, 2, 1)\n",
        "        #print(embedded.shape)\n",
        "        \n",
        "        #embedded = [batch size, emb dim, sent len]\n",
        "        \n",
        "        conved_0 = F.relu(self.conv_0(embedded))\n",
        "        conved_1 = F.relu(self.conv_1(embedded))\n",
        "        conved_2 = F.relu(self.conv_2(embedded))\n",
        "        conved_3 = F.relu(self.conv_3(embedded))\n",
        "            \n",
        "        #conved_n = [batch size, n_filters, sent len - filter_sizes[n] + 1]\n",
        "        \n",
        "        pooled_0 = F.max_pool1d(conved_0, conved_0.shape[2]).squeeze(2)\n",
        "        pooled_1 = F.max_pool1d(conved_1, conved_1.shape[2]).squeeze(2)\n",
        "        pooled_2 = F.max_pool1d(conved_2, conved_2.shape[2]).squeeze(2)\n",
        "        pooled_3 = F.max_pool1d(conved_3, conved_3.shape[2]).squeeze(2)\n",
        "        \n",
        "        #pooled_n = [batch size, n_fibatlters]\n",
        "        \n",
        "        cat = self.dropout(torch.cat((pooled_0, pooled_1, pooled_2, pooled_3), dim = 1).cuda())\n",
        "\n",
        "        #cat = [batch size, n_filters * len(filter_sizes)]\n",
        "            \n",
        "        result =  self.fc(cat)\n",
        "        \n",
        "        #print(result.shape)\n",
        "        \n",
        "        return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nTo_bpEeMqgr"
      },
      "outputs": [],
      "source": [
        "# Hyperparameters\n",
        "\n",
        "EMBEDDING_DIM = 768\n",
        "N_FILTERS = 32\n",
        "FILTER_SIZES = [1,2,3,5]\n",
        "OUTPUT_DIM = len(le.classes_)\n",
        "DROPOUT = 0.1\n",
        "PAD_IDX = tokenizer.pad_token_id\n",
        "\n",
        "cnn = CNN(EMBEDDING_DIM, N_FILTERS, FILTER_SIZES, OUTPUT_DIM, DROPOUT, PAD_IDX)\n",
        "cnn = cnn.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ITmi7WZDMsl6"
      },
      "outputs": [],
      "source": [
        "# Optimizer and criterion\n",
        "\n",
        "import torch.optim as optim\n",
        "\n",
        "model_prameters = list(phoBert.parameters())+list(cnn.parameters())\n",
        "\n",
        "optimizer = optim.Adam(model_prameters,lr=2e-5,eps=1e-8)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "criterion = criterion.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oyp8HhAxMvQH"
      },
      "outputs": [],
      "source": [
        "# Calculate accuracy per batch during train\n",
        "\n",
        "def categorical_accuracy(preds, y):\n",
        "    \"\"\"\n",
        "    Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8\n",
        "    \"\"\"\n",
        "    max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability\n",
        "    correct = max_preds.squeeze(1).eq(y)\n",
        "    return correct.sum() / torch.FloatTensor([y.shape[0]]).cuda()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TrVcBEVoMxov"
      },
      "outputs": [],
      "source": [
        "# Def for training\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "def train():\n",
        "    \n",
        "    epoch_loss = 0\n",
        "    epoch_acc = 0\n",
        "    \n",
        "    phoBert.train()\n",
        "    cnn.train()\n",
        "    \n",
        "    for batch in tqdm(train_data_loader):\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",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        embedded = phoBert(b_input_ids,b_input_mask)[0]\n",
        "        \n",
        "        predictions = cnn(embedded)\n",
        "        \n",
        "        loss = criterion(predictions, b_labels)\n",
        "        \n",
        "        acc = categorical_accuracy(predictions, b_labels)\n",
        "        \n",
        "        loss.backward()\n",
        "        \n",
        "        optimizer.step()\n",
        "        \n",
        "        epoch_loss += loss.item()\n",
        "        epoch_acc += acc.item()\n",
        "        \n",
        "    return epoch_loss / len(train_data_loader), epoch_acc / len(train_data_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nTQGFfnFMz-d"
      },
      "outputs": [],
      "source": [
        "# Class for predict label\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "def predictions_labels(preds,labels):\n",
        "    pred = np.argmax(preds,axis=1).flatten()\n",
        "    label = labels.flatten()\n",
        "    return pred,label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c_4Y1RhlM17e"
      },
      "outputs": [],
      "source": [
        "# Evaluate loss, acc  and f1-macro\n",
        "\n",
        "from sklearn.metrics import classification_report,accuracy_score,f1_score\n",
        "def eval():\n",
        "    epoch_loss = 0\n",
        "    \n",
        "    total_predictions = []\n",
        "    total_true = []\n",
        "    \n",
        "    all_true_labels = []\n",
        "    all_pred_labels = []\n",
        "    \n",
        "    phoBert.eval()\n",
        "    cnn.eval()\n",
        "    \n",
        "    with torch.no_grad():\n",
        "    \n",
        "        for batch in tqdm(valid_data_loader):\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",
        "            embedded = phoBert(b_input_ids,b_input_mask)[0]\n",
        "            predictions = cnn(embedded)\n",
        "\n",
        "            loss = criterion(predictions, b_labels)\n",
        "            epoch_loss += loss.item()\n",
        "            \n",
        "            predictions = predictions.detach().cpu().numpy()\n",
        "\n",
        "            label_ids = b_labels.to('cpu').numpy()\n",
        "    \n",
        "            pred,true = predictions_labels(predictions,label_ids)\n",
        "        \n",
        "            all_pred_labels.extend(pred)\n",
        "            all_true_labels.extend(true)\n",
        "\n",
        "    print(classification_report(all_pred_labels,all_true_labels))\n",
        "    avg_val_accuracy = accuracy_score(all_pred_labels,all_true_labels)\n",
        "    macro_f1_score = f1_score(all_pred_labels,all_true_labels,average='macro')\n",
        "    \n",
        "    avg_val_loss = epoch_loss/len(valid_data_loader)\n",
        "\n",
        "    print(\"accuracy = {0:.2f}\".format(avg_val_accuracy))\n",
        "            \n",
        "    return avg_val_loss,avg_val_accuracy,macro_f1_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ICyenO-tM35S"
      },
      "outputs": [],
      "source": [
        "# Time for training\n",
        "\n",
        "import time\n",
        "def epoch_time(start_time, end_time):\n",
        "    elapsed_time = end_time - start_time\n",
        "    elapsed_mins = int(elapsed_time / 60)\n",
        "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
        "    return elapsed_mins, elapsed_secs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6OrAOzamNC0e"
      },
      "outputs": [],
      "source": [
        "# Set device and gpu\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "torch.cuda.get_device_name(0)\n",
        "\n",
        "phoBert.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "KbJBkClEkam2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ZfHllZjgM5uw"
      },
      "outputs": [],
      "source": [
        "epochs = 20\n",
        "\n",
        "best_macro_f1 = float('0')\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    \n",
        "    start_time = time.time()\n",
        "    train_loss,train_acc = train()\n",
        "    valid_loss,valid_acc,macro_f1 = eval()\n",
        "    end_time = time.time()\n",
        "    \n",
        "        \n",
        "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
        "        \n",
        "    if macro_f1 > best_macro_f1:\n",
        "        best_macro_f1 = macro_f1\n",
        "        torch.save(phoBert,'/content/phobert_cnn_model_part1_'+'task2a_2.pt')\n",
        "        torch.save(cnn,'/content/phobert_cnn_model_part2_'+'task2a_2.pt')\n",
        "        print(\"model saved\")\n",
        "    \n",
        "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
        "    print(f'\\tTrain Loss: {train_loss:.3f} | Train acc: {train_acc*100:.2f}%')\n",
        "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. acc: {valid_acc*100:.2f}%')\n",
        "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. F1: {macro_f1*100:.2f}%')\n",
        "    print('=============Epoch Ended==============')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "KyQlcVfnCWHw"
      },
      "outputs": [],
      "source": [
        "# Save PhoBERT and CNN \n",
        "\n",
        "torch.save(phoBert,'module2_part1.pt')\n",
        "torch.save(cnn,'module2_part2.pt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCzo5AKq8lka"
      },
      "source": [
        "## EVALUATING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ftons15Hw8lB"
      },
      "outputs": [],
      "source": [
        "# Load phobert and cnn\n",
        "\n",
        "import torch\n",
        "phoBert = torch.load(r'/content/phobert_cnn_model_part1_task2a_2.pt')\n",
        "cnn = torch.load(r'/content/phobert_cnn_model_part2_task2a_2.pt')\n",
        "phoBert.eval()\n",
        "cnn.eval()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predict label from true label"
      ],
      "metadata": {
        "id": "N6sFPdMFlPRp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "U9Co-AvE_Ehq"
      },
      "outputs": [],
      "source": [
        "test_sent_index, test_input_ids, test_attention_masks, test_encoded_label_tensors = encoder_generator(test_sentences,test_labels)\n",
        "test_dataset = TensorDataset(test_input_ids,test_attention_masks,test_encoded_label_tensors)\n",
        "\n",
        "test_data_loader = DataLoader(test_dataset,\n",
        "                              sampler=RandomSampler(test_dataset),\n",
        "                              batch_size=bs)\n",
        "\n",
        "all_pred_labels = []\n",
        "all_true_labels = []\n",
        "\n",
        "with torch.no_grad():\n",
        "  for batch in tqdm(test_data_loader):\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",
        "    embedded = phoBert(b_input_ids,b_input_mask)[0]\n",
        "    predictions = cnn(embedded)\n",
        "\n",
        "    \n",
        "    predictions = predictions.detach().cpu().numpy()\n",
        "\n",
        "    label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "    pred, true = predictions_labels(predictions, label_ids)\n",
        "\n",
        "    all_pred_labels.extend(pred)\n",
        "    all_true_labels.extend(true)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ESaLntjdAfYF"
      },
      "outputs": [],
      "source": [
        "# The final score in the test set (classification report)\n",
        "\n",
        "print(classification_report(all_pred_labels,all_true_labels, digits = 4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "2zqXgAK1zy6Y"
      },
      "outputs": [],
      "source": [
        "# Confusion matrix in thetest set\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(all_true_labels, all_pred_labels)\n",
        "cm"
      ]
    }
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