{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x4N1GRI4PB61","outputId":"02ff3dba-b805-47ac-d31c-faa12a301762"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8v3yfIgiPC-e"},"outputs":[],"source":["path =  \"/content/drive/My Drive/KhoaLuan/\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d2pFgULmOl9s"},"outputs":[],"source":["import torch\n","import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","from sklearn.model_selection import StratifiedKFold\n","from sklearn.metrics import classification_report, confusion_matrix\n","\n","import torch.nn as nn\n","from torch.optim import AdamW\n","from torch.utils.data import Dataset, DataLoader\n","\n","from transformers import get_linear_schedule_with_warmup, AutoTokenizer, AutoModel, logging\n","\n","import warnings\n","import time\n","import pickle\n","warnings.filterwarnings(\"ignore\")\n","\n","logging.set_verbosity_error()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QoQ54muyOl9x"},"outputs":[],"source":["def seed_everything(seed_value):\n","    np.random.seed(seed_value)\n","    torch.manual_seed(seed_value)\n","\n","    if torch.cuda.is_available():\n","        torch.cuda.manual_seed(seed_value)\n","        torch.cuda.manual_seed_all(seed_value)\n","        torch.backends.cudnn.deterministic = True\n","        torch.backends.cudnn.benchmark = True\n","\n","seed_everything(86)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["cb4ab79526d440ca83477ff3a04a08a1","5b8c0cae78494560b77e662f62390b6a","a9fd54f8549448c7a0bcddb54f6291a0","975e44350bac401e9a252d91690f403c","96c86a9389b54e93a0b8bc4f1f8062c5","dcf1858dfb5d41cba8b389c8ce5e25df","f1283c390edb43d7a594b1698edb88d6","3194b7dfd003461f820aaf76f6f28e06","e6b4b08966444e48a27761a636f82d2f","29abb95da6bb456b8c93a908b224d595","9fce6620292549bd8680cab5fd9897e3","55efea9e72ee4c2a8dcd996d94b9d7d5","9f49bdbc16574981a5690557df4e86ed","ceec605689804459b4f4ef4a04ed350b","6c0a0e0995044ac68407772af686d110","e25ee9487fa74a46afa7312c114dc3c3","6b9b61cb50434953bdc9ffcc4927ce88","6e4032fac7cd44c287999dc2cc0140f3","b5e76d03302d4b1abff06a50cdcb1ae7","302f6ad8468d4b1ca5e84ff1be0a26cf","607a095cb82f46579d5225425c46d74c","37cfc2155b6942b0bdc2dc15d38d318b","b1a6ea2be64045e79757be9464101305","31f972a384f5484bb2e650f51ab0c07b","9087321a22a04369b3725d5e2c1b66ff","38afe0e6db004016a5041b44d20af4cd","ede18324dd254411adc81ec2060c1205","0b76d88f6b944d168ac720508995afb4","acdf3e8b1b6947fbbeaa4a9dc6bcfd1a","d4bd359fdc0d463fbf13a7939347c025","f0568aaa8be14d69938b16b28520bf58","72905e749d2f4319829bc847485ec12e","d31b652832bf4705a51240f52d3e123e","3f323509e43341078d14adba6014d4ac","b60001ec86c240819b4d1a3c6c36ef53","145fd1ddd7594e028b80d854b6ab1aee","1d3278e129d34725a5464966ea79096c","edf4474b02894f178f85e6753a9a8626","aa41fbef0b4440cbb3d286bb56e76221","304f1513ed774371af3f0a31a2ab62ae","4e66e5fd590049f3900ed96122be7765","c574f94bd30c4f919b27178f7663f04e","080e1c9cf237477abb8a4a886e24909d","454a25bd88f64938b4b479400167c1a0","14adb10381eb4f4481093c14aa902146","98f0555d5062403a9699411c89d313c7","d096bd334125477f9e566c5ee4ef77a4","91446629c2554a59aa3f32b845fb7551","d73cf8f1c1254f5d87846e0e35bf41b8","111f8a76f73a4752bd64cf80bf30c318","040ddedda1174ab6b9408e41c97e2e5c","83729c714d754ebfa84fef037d3c932e","578f14272fdf477dbc8693ab7bdf984e","be7cb164ab3e41eaa6c72209f0c6094e","d1b2dbd6bb054f70aac7f1fd493e3c19"]},"id":"au9SE5_YOl9x","outputId":"c0b300c6-23f9-44e8-b157-bce1e834caa8"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"cb4ab79526d440ca83477ff3a04a08a1","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/311 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"55efea9e72ee4c2a8dcd996d94b9d7d5","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/895k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b1a6ea2be64045e79757be9464101305","version_major":2,"version_minor":0},"text/plain":["bpe.codes:   0%|          | 0.00/1.14M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3f323509e43341078d14adba6014d4ac","version_major":2,"version_minor":0},"text/plain":["added_tokens.json:   0%|          | 0.00/22.0 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"14adb10381eb4f4481093c14aa902146","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json:   0%|          | 0.00/167 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["model_name = \"bluenguyen/longformer-phobert-base-4096\"  # vinai/phobert-base-v2\n","max_len = 512   # 256\n","n_classes = 13\n","tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ItGXacmyOl9y"},"outputs":[],"source":["device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","EPOCHS = 5\n","N_SPLITS = 5"]},{"cell_type":"markdown","metadata":{"id":"01RxDq7COl9y"},"source":["## Data"]},{"cell_type":"markdown","metadata":{"id":"l4ynf_f2Ol9z"},"source":["### Get data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nYjLbabaOl90"},"outputs":[],"source":["# Function to read data from JSON file\n","def get_data(path):\n","    df = pd.read_json(path, lines=True)\n","    return df\n","\n","TRAIN_PATH = path + \"train_data_162k.json\"\n","TEST_PATH = path + \"test_data_162k.json\"\n","VAL_PATH = path + \"val_data_162k.json\"\n","\n","# Read the data from JSON files\n","train_df = get_data(TRAIN_PATH)\n","test_df = get_data(TEST_PATH)\n","valid_df = get_data(VAL_PATH)\n","\n","# Combine train and validation data\n","train_df = pd.concat([train_df, valid_df], ignore_index=True)\n","\n","# Apply StratifiedKFold\n","skf = StratifiedKFold(n_splits=N_SPLITS)\n","for fold, (_, val_) in enumerate(skf.split(X=train_df, y=train_df.category)):\n","    train_df.loc[val_, \"kfold\"] = fold"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Lv08-h2nOl90","outputId":"417804bf-64a2-4f7a-ad2f-4d4990eb59c3"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>category</th>\n","      <th>processed_content</th>\n","      <th>kfold</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>96994</th>\n","      <td>Xe co</td>\n","      <td>fiat giới_thiệu động_cơ siêu_sạch air triển_lã...</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>22834</th>\n","      <td>Phap luat</td>\n","      <td>cận_cảnh hiện_trường quán karaoke xảy vụ bắn c...</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>14391</th>\n","      <td>Nha dat</td>\n","      <td>thổi giá kích_cầu đợt ấm đột_biến thị_trường đ...</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>100151</th>\n","      <td>Xa hoi</td>\n","      <td>quảng_ninh người_dân nuôi ngao run phóng_viên ...</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>31397</th>\n","      <td>Giao duc</td>\n","      <td>hội tư_vấn xét tuyển đại_học cao_đẳng diễn onl...</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         category                                  processed_content  kfold\n","96994       Xe co  fiat giới_thiệu động_cơ siêu_sạch air triển_lã...    3.0\n","22834   Phap luat  cận_cảnh hiện_trường quán karaoke xảy vụ bắn c...    1.0\n","14391     Nha dat  thổi giá kích_cầu đợt ấm đột_biến thị_trường đ...    2.0\n","100151     Xa hoi  quảng_ninh người_dân nuôi ngao run phóng_viên ...    0.0\n","31397    Giao duc  hội tư_vấn xét tuyển đại_học cao_đẳng diễn onl...    1.0"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["train_df.sample(5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0NQYr2vPOl91","outputId":"857865a2-3ddd-4d20-bced-2d101e767b7d"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 138223 entries, 0 to 138222\n","Data columns (total 3 columns):\n"," #   Column             Non-Null Count   Dtype  \n","---  ------             --------------   -----  \n"," 0   category           138223 non-null  object \n"," 1   processed_content  138223 non-null  object \n"," 2   kfold              138223 non-null  float64\n","dtypes: float64(1), object(2)\n","memory usage: 3.2+ MB\n","<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 24126 entries, 0 to 24125\n","Data columns (total 2 columns):\n"," #   Column             Non-Null Count  Dtype \n","---  ------             --------------  ----- \n"," 0   category           24126 non-null  object\n"," 1   processed_content  24126 non-null  object\n","dtypes: object(2)\n","memory usage: 377.1+ KB\n"]},{"data":{"text/plain":["(None, None)"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["train_df.info(), test_df.info()"]},{"cell_type":"markdown","metadata":{"id":"3su2QfYwOl92"},"source":["### Distribution of Categories"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":767},"id":"QceYF-mFOl92","outputId":"6d47e4c4-5613-45ae-ff2e-bc83ff1fd557"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plotting with Seaborn\n","plt.figure(figsize=(12, 8))\n","\n","# Get the unique categories\n","news_categories = train_df['category'].unique()\n","\n","colors = sns.color_palette(\"husl\", len(news_categories))\n","\n","color_mapping = {article: colors[i] for i, article in enumerate(news_categories)}\n","\n","# Plot with each bar having a different color\n","ax = sns.countplot(x='category', data=train_df, palette=color_mapping)\n","\n","# Add title and labels\n","plt.title('Distribution of Categories')\n","plt.xlabel('News Category')\n","plt.ylabel('Count')\n","plt.xticks(rotation=45)  # Rotate x labels for better readability\n","\n","# Display the plot\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"ZNxd_KCGOl93"},"source":["### Distribution of length of Sentence"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"j81aV78YOl93","outputId":"01d28bdb-0f7f-4b6c-e437-b2296f58056f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# Combine processed content from train and test dataframes\n","all_data = train_df.processed_content.tolist() + test_df.processed_content.tolist()\n","# Encode the text\n","encoded_text = [tokenizer.encode(text, add_special_tokens=True) for text in all_data]\n","\n","# Calculate the length of each encoded text\n","token_lens = [len(text) for text in encoded_text]\n","\n","# Plot the distribution of token lengths\n","plt.figure(figsize=(10, 6))\n","sns.histplot(token_lens, kde=True)\n","plt.xlim([0, max(token_lens)])\n","plt.xlabel('Token Count')\n","plt.ylabel('Frequency')\n","plt.title('Distribution of Token Count per Sentence')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"BjMv5-vvOl93"},"source":["## Model PhoBert"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Mz4mEyJqOl93"},"outputs":[],"source":["class NewsDataset(Dataset):\n","    def __init__(self, df, tokenizer, max_len):\n","        self.df = df\n","        self.max_len = max_len\n","        self.tokenizer = tokenizer\n","\n","    def __len__(self):\n","        return len(self.df)\n","\n","    def __getitem__(self, index):\n","        \"\"\"\n","        To customize dataset, inherit from Dataset class and implement\n","        __len__ & __getitem__\n","        __getitem__ should return\n","            data:\n","                input_ids\n","                attention_masks\n","                text\n","                targets\n","        \"\"\"\n","        row = self.df.iloc[index]\n","        text, label = self.get_input_data(row)\n","\n","        # Encode_plus will:\n","        # (1) split text into token\n","        # (2) Add the '[CLS]' and '[SEP]' token to the start and end\n","        # (3) Truncate/Pad sentence to max length\n","        # (4) Map token to their IDS\n","        # (5) Create attention mask\n","        # (6) Return a dictionary of outputs\n","        encoding = self.tokenizer.encode_plus(\n","            text,\n","            truncation=True,\n","            add_special_tokens=True,\n","            max_length=self.max_len,\n","            padding='max_length',\n","            return_attention_mask=True,\n","            return_token_type_ids=False,\n","            return_tensors='pt',\n","        )\n","\n","        return {\n","            'text': text,\n","            'input_ids': encoding['input_ids'].flatten(),\n","            'attention_masks': encoding['attention_mask'].flatten(),\n","            'targets': torch.tensor(label, dtype=torch.long),\n","        }\n","\n","\n","    def labelencoder(self, text):\n","        label_map = {\n","            'Cong nghe': 0, 'Doi song': 1, 'Giai tri': 2, 'Giao duc': 3, 'Khoa hoc': 4,\n","            'Kinh te': 5, 'Nha dat': 6, 'Phap luat': 7, 'The gioi': 8, 'The thao': 9,\n","            'Van hoa': 10, 'Xa hoi': 11, 'Xe co': 12\n","        }\n","        return label_map.get(text, -1)\n","\n","    def get_input_data(self, row):\n","        text = row['processed_content']\n","        label = self.labelencoder(row['category'])\n","        return text, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zMq3qvM0Ol94"},"outputs":[],"source":["class NewsClassifier(nn.Module):\n","    def __init__(self, n_classes, model_name):\n","        super(NewsClassifier, self).__init__()\n","        # Load a pre-trained BERT model\n","        self.bert = AutoModel.from_pretrained(model_name)\n","        # Dropout layer to prevent overfitting\n","        self.drop = nn.Dropout(p=0.3)\n","        # Fully-connected layer to convert BERT's hidden state to the number of classes to predict\n","        self.fc = nn.Linear(self.bert.config.hidden_size, n_classes)\n","        # Initialize weights and biases of the fully-connected layer using the normal distribution method\n","        nn.init.normal_(self.fc.weight, std=0.02)\n","        nn.init.normal_(self.fc.bias, 0)\n","\n","    def forward(self, input_ids, attention_mask):\n","        # Get the output from the BERT model\n","        last_hidden_state, output = self.bert(\n","            input_ids=input_ids,\n","            attention_mask=attention_mask,\n","            return_dict=False\n","        )\n","        # Apply dropout\n","        x = self.drop(output)\n","        # Pass through the fully-connected layer to get predictions\n","        x = self.fc(x)\n","        return x"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X7wuM4oAOl94"},"outputs":[],"source":["def prepare_loaders(df, fold):\n","    df_train = df[df.kfold != fold].reset_index(drop=True)\n","    df_valid = df[df.kfold == fold].reset_index(drop=True)\n","\n","    train_dataset = NewsDataset(df_train, tokenizer, max_len)\n","    valid_dataset = NewsDataset(df_valid, tokenizer, max_len)\n","\n","    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=2)\n","    valid_loader = DataLoader(valid_dataset, batch_size=16, shuffle=True, num_workers=2)\n","\n","    return train_loader, valid_loader"]},{"cell_type":"markdown","metadata":{"id":"h4gy1xRfOl94"},"source":["### Train"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BRIOWDxqOl94"},"outputs":[],"source":["# Function to train the model for one epoch\n","def train(model, criterion, optimizer, train_loader, lr_scheduler):\n","    model.train()\n","    losses = []\n","    correct = 0\n","\n","    for batch_idx, data in enumerate(train_loader):\n","        input_ids = data['input_ids'].to(device)\n","        attention_mask = data['attention_masks'].to(device)\n","        targets = data['targets'].to(device)\n","\n","        optimizer.zero_grad()\n","        outputs = model(\n","            input_ids=input_ids,\n","            attention_mask=attention_mask\n","        )\n","\n","        loss = criterion(outputs, targets)\n","        _, pred = torch.max(outputs, dim=1)\n","\n","        correct += torch.sum(pred == targets)\n","        losses.append(loss.item())\n","        loss.backward()\n","        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n","        optimizer.step()\n","        lr_scheduler.step()\n","\n","        if batch_idx % 1000 == 0:\n","            print(f'Batch {batch_idx}/{len(train_loader)} - Loss: {loss.item():.4f}, Accuracy: {correct.double() / ((batch_idx + 1) * train_loader.batch_size):.4f}')\n","\n","    train_accuracy = correct.double() / len(train_loader.dataset)\n","    avg_loss = np.mean(losses)\n","    print(f'Train Accuracy: {train_accuracy:.4f} Loss: {avg_loss:.4f}')\n","\n","# Function to evaluate the model\n","def eval(model, criterion, valid_loader, test_loader=None):\n","    model.eval()\n","    losses = []\n","    correct = 0\n","\n","    with torch.no_grad():\n","        data_loader = test_loader if test_loader else valid_loader\n","        for batch_idx, data in enumerate(data_loader):\n","            input_ids = data['input_ids'].to(device)\n","            attention_mask = data['attention_masks'].to(device)\n","            targets = data['targets'].to(device)\n","\n","            outputs = model(\n","                input_ids=input_ids,\n","                attention_mask=attention_mask\n","            )\n","\n","            loss = criterion(outputs, targets)\n","            _, pred = torch.max(outputs, dim=1)\n","\n","            correct += torch.sum(pred == targets)\n","            losses.append(loss.item())\n","\n","    dataset_size = len(test_loader.dataset) if test_loader else len(valid_loader.dataset)\n","    accuracy = correct.double() / dataset_size\n","    avg_loss = np.mean(losses)\n","\n","    if test_loader:\n","        print(f'Test Accuracy: {accuracy:.4f} Loss: {avg_loss:.4f}')\n","    else:\n","        print(f'Valid Accuracy: {accuracy:.4f} Loss: {avg_loss:.4f}')\n","\n","    return accuracy\n","\n","\n","total_start_time = time.time()\n","\n","# Main training loop\n","for fold in range(skf.n_splits):\n","    print(f'----------- Fold: {fold + 1} ------------------')\n","    train_loader, valid_loader = prepare_loaders(train_df, fold=fold)\n","    model = NewsClassifier(n_classes=13).to(device)\n","    criterion = nn.CrossEntropyLoss()\n","    optimizer = AdamW(model.parameters(), lr=2e-5)\n","\n","    lr_scheduler = get_linear_schedule_with_warmup(\n","        optimizer,\n","        num_warmup_steps=0,\n","        num_training_steps=len(train_loader) * EPOCHS\n","    )\n","    best_acc = 0\n","\n","    for epoch in range(EPOCHS):\n","        print(f'Epoch {epoch + 1}/{EPOCHS}')\n","        print('-' * 30)\n","\n","        train(model, criterion, optimizer, train_loader, lr_scheduler)\n","        val_acc = eval(model, criterion, valid_loader)\n","\n","        if val_acc > best_acc:\n","            torch.save(model.state_dict(), f'phobert_fold{fold + 1}.pth')\n","            best_acc = val_acc\n","        print(f'Best Accuracy for Fold {fold + 1}: {best_acc:.4f}')\n","        print()\n","    print(f'Finished Fold {fold + 1} with Best Accuracy: {best_acc:.4f}')\n","    print('--------------------------------------')\n","\n","\n","total_end_time = time.time()\n","\n","total_duration = total_end_time - total_start_time\n","print(f'Total training time: {total_duration:.2f} seconds')\n"]},{"cell_type":"markdown","metadata":{"id":"qwlF0GZOOl95"},"source":["### Test"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_SCp99aOOl95"},"outputs":[],"source":["# Function to decode numeric labels into their corresponding text names\n","def decode_labels(labels):\n","    label_map = {\n","        0: 'Cong nghe', 1: 'Doi song', 2: 'Giai tri', 3: 'Giao duc', 4: 'Khoa hoc',\n","        5: 'Kinh te', 6: 'Nha dat', 7: 'Phap luat', 8: 'The gioi', 9: 'The thao',\n","        10: 'Van hoa', 11: 'Xa hoi', 12: 'Xe co'\n","    }\n","    return [label_map[label] for label in labels]\n","\n","# Function to test the model\n","def test(data_loader):\n","    models = []\n","    for fold in range(skf.n_splits):\n","        model = NewsClassifier(n_classes=13)\n","        model.to(device)\n","        model.load_state_dict(torch.load(f'{path}phobert_fold{fold+1}.pth'))\n","        model.eval()\n","        models.append(model)\n","\n","    texts = []\n","    predicts = []\n","    predict_probs = []\n","    real_values = []\n","\n","    for data in data_loader:\n","        text = data['text']\n","        input_ids = data['input_ids'].to(device)\n","        attention_mask = data['attention_masks'].to(device)\n","        targets = data['targets'].to(device)\n","\n","        total_outs = []\n","        for model in models:\n","            with torch.no_grad():\n","                outputs = model(\n","                    input_ids=input_ids,\n","                    attention_mask=attention_mask\n","                )\n","                total_outs.append(outputs)\n","\n","        # Taking the average of predictions from 5 models\n","        total_outs = torch.stack(total_outs)\n","        _, pred = torch.max(total_outs.mean(0), dim=1)\n","        texts.extend(text)\n","        predicts.extend(pred)\n","        predict_probs.extend(total_outs.mean(0))\n","        real_values.extend(targets)\n","\n","    predicts = torch.stack(predicts).cpu().numpy()\n","    predict_probs = torch.stack(predict_probs).cpu().numpy()\n","    real_values = torch.stack(real_values).cpu().numpy()\n","\n","    # Decode numeric labels into text labels\n","    decoded_real_values = decode_labels(real_values)\n","    decoded_predicts = decode_labels(predicts)\n","\n","    # Generate classification report\n","    report = classification_report(decoded_real_values, decoded_predicts, output_dict=True)\n","\n","    # Convert to DataFrame\n","    df_report = pd.DataFrame(report).transpose()\n","\n","    return df_report, real_values, predicts"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["df3cbd2c4920474bad84ec99eda619c0","85effd0ef07646eebdd4f3f11600d7a9","20b90fbcd3e847e9ba32754533006005","17b8728e992b4492b462fc43e818aeb0","3f8b887fb7734f5c8f6039635e56bd1e","fdebb25d116543588433193b23754ee3","19bbaf575d524985a9500f970cc26ae5","fd5ffeec51ad471880fe0727281064bb","33f3c907f87d457f9668159dc751474d","5a1c448958014944975d9aa23ea6e731","c861c40db6514b1c8edeef8807893947","2596e14f143b4b84993ee6c11f2a70c1","e851ec492a5f4e55aabab75132452164","9806430ca6034e7993a4a10db28688a8","244f1f1d736748fab96fca0e84953a81","82c39e0cbb9349e085d2cd661fb653f5","baefa134fff04b9f930d9970256453b7","ba6cdff06d4f4813989193ada3e9efb6","e0a021045cb74752b3d4766d26226cc0","25086f17dc4747e38effd3c43acff41f","37c0a263d21046758c860ab98154dc0a","43291225a3294dd0a90d480264337e72"]},"id":"p7laJqSVOl96","outputId":"6739c89c-841d-4339-d811-c26e36516e6b"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"df3cbd2c4920474bad84ec99eda619c0","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/916 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2596e14f143b4b84993ee6c11f2a70c1","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin:   0%|          | 0.00/637M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["# Create dataloader for test set\n","test_dataset = NewsDataset(test_df, tokenizer, max_len)\n","test_loader = DataLoader(test_dataset, batch_size=16, shuffle=True, num_workers=2)\n","df_report, real_values, predicts = test(test_loader)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"id":"3PfIY0jkg16s","outputId":"7ea15cdf-afb6-4e0b-9be3-c91653646a30"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"df_report\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03124322214613196,\n        \"min\": 0.8457655636567583,\n        \"max\": 0.9698681732580038,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          0.9432234432234432,\n          0.8915956151035322,\n          0.9139344262295082\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03650359365888964,\n        \"min\": 0.8543909348441926,\n        \"max\": 0.9826187717265353,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.9826187717265353,\n          0.8543909348441926,\n          0.9426479560707749\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0333532065632639,\n        \"min\": 0.8500563697857947,\n        \"max\": 0.974152785755313,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          0.9429356118400978,\n          0.8870039382005452,\n          0.8946840521564694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7668.022603263057,\n        \"min\": 0.9163972477824753,\n        \"max\": 24126.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          1726.0,\n          1765.0,\n          1639.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe","variable_name":"df_report"},"text/html":["\n","  <div id=\"df-eb3b33e3-9506-46b1-8f8a-ac96047f9733\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1-score</th>\n","      <th>support</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Cong nghe</th>\n","      <td>0.943223</td>\n","      <td>0.942648</td>\n","      <td>0.942936</td>\n","      <td>1639.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Doi song</th>\n","      <td>0.891596</td>\n","      <td>0.882459</td>\n","      <td>0.887004</td>\n","      <td>1659.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Giai tri</th>\n","      <td>0.932446</td>\n","      <td>0.931493</td>\n","      <td>0.931969</td>\n","      <td>1956.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Giao duc</th>\n","      <td>0.927056</td>\n","      <td>0.945148</td>\n","      <td>0.936015</td>\n","      <td>1896.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Khoa hoc</th>\n","      <td>0.900331</td>\n","      <td>0.905463</td>\n","      <td>0.902890</td>\n","      <td>2105.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Kinh te</th>\n","      <td>0.913934</td>\n","      <td>0.876228</td>\n","      <td>0.894684</td>\n","      <td>2036.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Nha dat</th>\n","      <td>0.910014</td>\n","      <td>0.921926</td>\n","      <td>0.915931</td>\n","      <td>2139.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Phap luat</th>\n","      <td>0.903488</td>\n","      <td>0.894646</td>\n","      <td>0.899045</td>\n","      <td>1737.000000</td>\n","    </tr>\n","    <tr>\n","      <th>The gioi</th>\n","      <td>0.932762</td>\n","      <td>0.935847</td>\n","      <td>0.934302</td>\n","      <td>1512.000000</td>\n","    </tr>\n","    <tr>\n","      <th>The thao</th>\n","      <td>0.965831</td>\n","      <td>0.982619</td>\n","      <td>0.974153</td>\n","      <td>1726.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Van hoa</th>\n","      <td>0.872737</td>\n","      <td>0.863266</td>\n","      <td>0.867976</td>\n","      <td>1843.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Xa hoi</th>\n","      <td>0.845766</td>\n","      <td>0.854391</td>\n","      <td>0.850056</td>\n","      <td>1765.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Xe co</th>\n","      <td>0.969868</td>\n","      <td>0.974917</td>\n","      <td>0.972386</td>\n","      <td>2113.000000</td>\n","    </tr>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.916397</td>\n","      <td>0.916397</td>\n","      <td>0.916397</td>\n","      <td>0.916397</td>\n","    </tr>\n","    <tr>\n","      <th>macro avg</th>\n","      <td>0.916081</td>\n","      <td>0.916235</td>\n","      <td>0.916104</td>\n","      <td>24126.000000</td>\n","    </tr>\n","    <tr>\n","      <th>weighted avg</th>\n","      <td>0.916291</td>\n","      <td>0.916397</td>\n","      <td>0.916287</td>\n","      <td>24126.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb3b33e3-9506-46b1-8f8a-ac96047f9733')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    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0.845766  0.854391  0.850056   1765.000000\n","Xe co          0.969868  0.974917  0.972386   2113.000000\n","accuracy       0.916397  0.916397  0.916397      0.916397\n","macro avg      0.916081  0.916235  0.916104  24126.000000\n","weighted avg   0.916291  0.916397  0.916287  24126.000000"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["df_report"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"geDv4lH3lSyr"},"outputs":[],"source":["df_report.to_csv(path + 'classification_report_longformer.csv', index=True) # classification_report_phobertbase.csv\n","data_to_save = [real_values, predicts]\n","\n","\n","with open(path + 'real_values_and_predicts_longformer.pkl', 'wb') as f: # real_values_and_predicts_phobertbase.pkl\n","    pickle.dump(data_to_save, f)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":779},"id":"2TNxZ20ehyfM","outputId":"6d0153ef-6871-464f-e815-bfcc7359509f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["class_names = ['Cong nghe', 'Doi song', 'Giai tri', 'Giao duc', 'Khoa hoc', 'Kinh te',\n","               'Nha dat', 'Phap luat', 'The gioi', 'The thao', 'Van hoa', 'Xa hoi', 'Xe co']\n","\n","cm = confusion_matrix(real_values, predicts)\n","plt.figure(figsize=(10, 8))\n","sns.heatmap(cm, annot=True, fmt='d', xticklabels=class_names, yticklabels=class_names)\n","\n","\n","plt.title('Confusion Matrix for longformer-phobert-base-4096')\n","plt.xlabel('Predicted Labels')\n","plt.ylabel('True Labels')\n","\n","plt.savefig(path + 'confusion_matrix_longformer-phobert-base-4096.png')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9ClXZpNPjPGL","outputId":"6b187cf8-5958-48ab-9b9b-aadb969fe3bc"},"outputs":[{"name":"stdout","output_type":"stream","text":["--------------------------------------------------\n","biệt_đội chuột_lang đột_phá công_nghệ ndđt chuột_lang đạo_diễn đột_phá công_nghệ hình_ảnh chiều công_nghệ kỹ_xảo hàng_đầu phim kết_hợp tuyệt_vời diễn_xuất hình_ảnh hoạt_hình phim ra_mắt khán_giả việt_nam rạp toàn_quốc phim tựa gốc thể_loại hước xoay quanh câu_chuyện nhân_viên tình_báo chính_phủ biệt_đội chuột lang chuột chỉ_huy tài_ba chuột chuyên_gia vũ_khí chuột bậc thầy võ_thuật vẻ quyến_rũ cộng trinh_sát chuyên_gia máy_tính chuột_chũi mũi chuyến hành_trình gặp_gỡ vô_số thành_viên vương_quốc thú_vật chuột lang lang_thang gã chuột_đồng hung_dữ trang_bị thiết_bị tối_tân chuột lang huấn_luyện chuyên_nghiệp nắm vận_mệnh thế_giới đứng nguy_cơ giải_tán tồn_tại phim tham_gia tên_tuổi hollywood nicolas_cage penelope_cruz chuyên_gia kỹ_xảo điện_ảnh giải oscar đạo_diễn giành giải_oscar biệt_đội chuột_lang ứng_dụng công_nghệ kỹ_xảo dựng_hình chiều diễn_xuất chuột trở_nên sống_động hơn_bao_giờ_hết rạp công_nghệ_cao khán_giả nhân_vật lao thẳng màn_hình phim khởi_chiếu cụm rạp megastar toàn_quốc chuột công_nghệ kỹ_xảo đột_phá diễn_xuất nhện hình_ảnh d oscar chuột_chũi darwin\n","Predicted: (Xe co) --vs-- Real label: (Kinh te)\n","--------------------------------------------------\n","phim hành_động khán_giả thỏa_mãn john_wick chapter logan atomic_blonde blade phim fan dòng phim hành_động thỏa_mãn mùa thu john_wick lặng_lẽ ra_mắt mau_chóng tác_phẩm sở_hữu câu_chuyện tối_giản kỳ_cục gã sát_thủ giải_nghệ keanu_reeves quyết_định trở_lại giang_hồ trả_thù chó cưng phim khán_giả thế_giới ngầm sáng_tạo trường đoạn hành_động gọn tàn_bạo khoan_nhượng john_wick chapter vượt_trội mảng hành_động ảnh summit john_wick chapter trình_làng phụ tin_yêu hâm_mộ phim vượt_trội tập thế_giới ngầm hoành_tráng nhân_vật bá_đạo pha hành_động mãn nhãn ác_liệt dài_hơi keanu_reeves nâng tầm nhân_vật john_wick đẳng_cấp biến biểu_tượng hiếm_hoi dòng phim hành_động kết_thúc kịch sát_thủ john_wick trở_lại wolverine hugh_jackman nhân_vật khán_giả yêu_thích bậc thương_hiệu phim xmen chờ chuyến đi logan hâm_mộ chứng_kiến chồn trổ_tài hành_động đậm_chất bạo_lực nguyên_tác truyện_tranh logan hình_ảnh dị_nhân wolverine bạo_lực đẫm máu nguyên_tác truyện_tranh ảnh fox vô_số khen_ngợi câu_chuyện cảm_xúc diễn_xuất tuyệt_vời dàn diễn_viên logan hành_động ấn_tượng kỳ_vọng tác_phẩm hoành_tráng kỹ_xảo hoa_mỹ phim siêu anh_hùng thất_vọng logan ghi_điểm hướng trần_trụi tàn_bạo máu_me chết hàng_loạt đòn đánh xuất móng logan bé laura mục_đích đoạt mạng giúp khắc_họa nét thế_giới tàn_bạo dị_nhân giúp logan trở_nên khác_biệt tác_phẩm siêu anh_hùng hiếm loạt phim hành_động trải phong_độ ổn_định xu_hướng fast_furious bất_chấp kêu_ca ngao_ngán lẫn châm_biếm cốt thế_giới nhàm_chán hành_động ảo_diệu khán_giả đại_chúng đổ_xô rạp thương_hiệu tung tập phim giúp bạc tỷ thương_hiệu fast_furious điên_rồ giúp nhà_sản_xuất thu ảnh universal fast furious duy_trì công_thức cốt truyện đơn_giản lặp phim trở_nên hấp_dẫn chủ_yếu hành_động hoành_tráng đậm_tính giải_trí nhà_sản_xuất ý_tưởng hành_động ảo_diệu trường đoạn hàng ôtô thao_túng tàn_phá new_york màn đua xe tàu ngầm hạt_nhân phim giới phê_bình tỏ mặn_mà chừng mục_đích giải_trí khán_giả đại_chúng đội_ngũ biên_kịch loạt fast_furious úp_mở dominic_toretto vin_diesel đồng_đội không_gian tiếp phi_vụ không_tưởng đại_diện dòng phim siêu anh_hùng dc rạp hồi phim nhân_vật ngôi_sao gal_gadot gặt_hái thành_công vang_dội wonder woman lòng_giới phê_bình khán_giả mà_còn thắng phòng vé doanh_thu hành_động wonder_woman hoành_tráng mà_còn truyền cảm_hứng khán_giả ảnh warner_bros khả_năng chỉ_đạo tài_tình nữ đạo_diễn patty_jenkins diễn_xuất ăn_ý gal_gadot chris_pine wonder_woman hành_động chất_lượng phim thành_công miêu_tả sức_mạnh năng_lực thông_qua hàng_loạt trường đoạn chiến_đấu ấn_tượng dài_hơi cảm_xúc cảnh wonder_woman giải_phóng no trận nhân_vật vị thần chiến_tranh ares bom minh_chứng rõ_ràng bóng_hồng hề thua_kém phái dòng phim siêu anh_hùng phim đạo_diễn edgar_wright nằm tác_phẩm điện_ảnh độc_đáo hấp_dẫn hài_hước kịch_tính phong_cách baby_driver ngựa ô phòng vé giúp sony thu khoản ngân_sách sản_xuất baby driver nằm tác_phẩm điện_ảnh độc_đáo bậc ảnh sony hành_động baby_driver dạng hoành_tráng hiệu_ứng phim trở_nên hấp_dẫn kịch_tính pha truy_đuổi đấu súng tiết_tấu đậm_tính bạo_lực nhạc đa_dạng thể_loại đạo_diễn edgar_wright xây_dựng phim hàng_loạt nhân_vật độc_lạ bầu_không_khí baby_driver gương_mặt đĩa sáng_tạo nghệ_thuật giúp tác_phẩm kích_thích khán_giả theo_dõi chiến_lang phim hoa_ngữ ồn_ào ban_đầu rạp lặng_lẽ tác_phẩm hành_động ngô_kinh đạo_diễn kiêm sắm vai nhanh_chóng địa_chấn khổng_lồ phim càn_quét phòng vé trung_quốc phá vỡ hàng_loạt kỷ_lục quê_nhà lọt danh_sách top phim ăn_khách toàn_cầu thành_tích chiến lang tranh_cãi mặt nội_dung thực_chất hành_động lôi_cuốn tài_năng ngô_kinh ảnh group công_bằng tạm bỏ_qua chủ_nghĩa anh_hùng dân_tộc nội_dung chiến_lang phim hành_động dạng đậm_tính giải_trí sở_hữu chất_lượng vượt_trội hẳn đầu ra_mắt phim tiết_tấu trường đoạn hành_động công_phu dài_hơi đậm dấu_ấn tác_phẩm thể_loại hollywood hồi thập_niên thành_công thương_mại chiến_lang giúp ngô_kinh tác_phẩm chất_lượng tương_lai vai diễn xuất_sắc mad_max theron tài_năng diễn_xuất hành_động tác_phẩm đề_tài điệp_viên atomic_blonde nội_dung rối_rắm bù_đắp khán_giả hàng_loạt pha biểu_diễn võ_nghệ ấn_tượng charlize_theron ảnh công_bằng phim sở_hữu đề_tài tương_đối cũ kịch_bản atomic_blonde đôi_lúc trở_nên rối_rắm gọn_gàng chi_tiết cần_thiết song diễn_xuất lôi_cuốn theron khán_giả phút chót atomic_blonde ghi_điểm hành_động đồng đạo_diễn john_wick pha đánh_đấm phim mạnh_mẽ tàn_bạo nét nữ linh_hoạt hình_tượng nữ_điệp_viên charlize_theron minh_tinh nam_phi tiết_lộ pha hành_động phim nữ_điệp_viên charlize_theron tung_hoành màn_ảnh rộng chứng_kiến đả_nữ lợi_hại kém đất_nước hàn_quốc báo_thù phim hành_động đẫm máu điên_rồ điện_ảnh hàn_quốc ảnh next đẹp vào_vai nữ mật_vụ ngầm khả_năng chiến_đấu siêu_đẳng đạo_diễn siêu_phẩm hành_động vô tiền khoáng_hậu lịch_sử điện_ảnh xứ kim chi phim sở_hữu trường đoạn hành_động đẫm máu táo_bạo dài_hơi hiệu_ứng thủ_pháp quay_phim góc máy rung cú máy đầu chiến_đấu cỗ máy giết ngăn_cản vũ_khí đa_dạng đạo_diễn kỳ_cựu kỷ_niệm tác_phẩm điện_ảnh sự_nghiệp phim liveaction đóng chuyển_thể manga lĩnh_vực phim nhiệt_tình khai_thác blade dựa truyện_tranh tác_giả blade chuyển_thể truyện_tranh nhật_bản đẫm máu phim đạo_diễn ảnh warner_bros nội_dung phim hành_trình dính nguyền mạng kẻ_ác thoát cảnh sống_mòn trung_thành phong_cách bạo_lực nguyên_tác blade ngập_tràn cảnh giao đấu máu_me nhân_vật binh_khí kỳ_quái phim chưa_thể độ thành_công siêu_phẩm dự_án hành_động thú_vị độc_đáo coi phim chuyển_thể manga hiếm_hoi thành_công hẹn chiến_tranh làm_mưa_làm_gió phòng vé khắp toàn_cầu rạp hôm thành_công the_last_jedi hiện dấu_hỏi tranh_cãi mặt nội_dung chia_rẽ giới phê_bình khán_giả đại_chúng hâm_mộ trung_thành loạt phim the_last_jedi chiêu_đãi hâm_mộ hàng_loạt cảnh hành_động đặc_sản thương_hiệu star_wars ảnh disney hành_động bom star wars the_last_jedi đem khán_giả trận không_gian hoành_tráng mãn nhãn rực_rỡ cạnh trận giao_đấu gươm ánh_sáng thần_lực đặc_sản star_wars mắt tác_phẩm giải_trí đáng_giá dịp hành_động lôi_cuốn phim đạo_diễn khán_giả tác_phẩm phòng vé hollywood warner_bros furious trường đoạn wonder_woman hành_động john_wick charlize_theron atomic_blonde baby_driver chiến_lang the_last_jedi\n","Predicted: (Van hoa) --vs-- Real label: (Cong nghe)\n","--------------------------------------------------\n","hân scandal mệt_mỏi sức_sống chung_hân đồng_nghiệp hâm_mộ lo_lắng vụ rạn_nứt tình_cảm vợ_chồng cặp vàng nghệ_sĩ trương_bá_chi tạ_đình_phong lôi thành_viên nhạc nữ diễn_viên tạ_đình_phong đồng_nghiệp nguyên_nhân rõ_ràng chia_tay anh_chàng tạ vợ bé trung_quốc hân_đồng sự_kiện ca_khúc vàng trung_quốc tổ_chức hồng_kông ca_sĩ nổi_tiếng dễ_dàng cạnh chung_hân đi thân_thiết nhạc thái_trác_nghiên nữ diễn_viên ca_sĩ thừa_nhận quay_phim tô_châu vắt kiệt_sức dính tin_đồn kẻ áp_lực hân_đồng sự_kiện thừa_nhận lo_lắng hy_vọng nghỉ_ngơi hân_đồng cười chung_hân nỗi buồn tiều_tụy giấu nổi t n hân tô_châu diễn_viên vợ bé thái_trác_nghiên đồng_nghiệp vắt_kiệt twins\n","Predicted: (Kinh te) --vs-- Real label: (Giao duc)\n","--------------------------------------------------\n","vơi tiếng gọi bản_năng chinh_chiến liên_hoan phim quốc_tế xuất_ngoại màn_ảnh rộng chơi_vơi phim chất nghệ_thuật đạo_diễn bùi_thạc_chuyên trình_làng khán_giả háo_hức người_yêu điện_ảnh chơi_vơi kém xôn_xao đầu gái nhảy ra_mắt công_chúng chơi_vơi mê_cung cảm_xúc xây quan_hệ chằng_chịt con_người con_rối yếu_ớt hành_động tiếng gọi bản_năng làn gió chơi_vơi phim khai_thác mảng đề_tài nhạy_cảm đồng_tính ngoại_tình dựa diễn_tiến phức_tạp rối_rắm mạch cảm_xúc duyên đàn_bà nhạy_cảm khao_khát đàn_ông yêu âu_yếm chiều_chuộng kiêng_cữ trót hứa đem xui_xẻo gã đỏ_đen duyên sắp_xếp vòng_tay đàn_ông hạnh_phúc trần_trụi khán_giả tình_huống éo_le kịch_tính tồn_tại phim_ảnh xã_hội hoàn_cảnh đưa_đẩy duyên yêu chồng chiến_thắng rơi tình_thế éo_le dằn_vặt ân_hận chơi_vơi độc_đáo phim ta cảm_nhận hiện_diện tình đồng_tính duyên cầm liên_hệ nhân_vật gọi_là tình_yêu đồng_giới cầm quỵ đau_khổ đám_cưới duyên vòng_tay thổ cầm thỏa nỗi hờn_ghen hạnh_phúc xứng_đáng phụ_nữ bản_thể trái lập mảnh_dẻ yếu_ớt gai_góc mạnh_mẽ gắn_kết bản_thể hòa_hợp đa_chiều sâu đậm day_dứt tình_yêu thông_thường chơi_vơi nằm tự_nhiên phim thấm khán_giả nhịp sống phim trôi bình_thản hề tính_toán kết_quả logic phim bối_rối nhân_vật đạo_diễn tài_tình không_gian lát cắt cuộc_sống đời_thường nhân_vật nổi_bật phân_cảnh xuất_sắc chơi_vơi hài_hòa vơi táo_bạo chừng_mực chi_tiết phim ngừng ngưỡng đứng lưng_chừng cảm_giác hụt_hẫng chờ lý_giải tiếp khán_giả hẫng_hụt cuộc_sống cộng_hưởng giai_điệu liệu câu trả_lời sức nằm vai diễn đỗ_hải_yến thể_hiện nhị sâu_sắc pao linh_đan mong_đợi chinh_phục êkíp đạo_diễn bùi_thạc_chuyên khả_năng diễn quốc_tế nữ nhân_vật hết_sức phức_tạp johnny_trí nguyễn_một tên_tuổi gắn liền đổi_mới điện_ảnh việt lựa_chọn tay_chơi lão_làng sở khanh duy_khoa ngôi_sao nổi mai_điểm hẹn thể_hiện chàng tài_xế taxi ngây_thơ mẹ kèm_cặp nhân_vật đời vơi ra_mắt khán_giả nhà_đầu hứa_hẹn thi_vị nghệ_thuật điện_ảnh cuộc_sống đạo_diễn bùi_thạc_chuyên hơi khiêm_tốn nhận_định chơi_vơi cơn_sốt phim sân_nhà đông rạp phim dạng nghệ_thuật kén khán_giả chơi_vơi dấu_ấn công_chúng nhật bản_năng tiếng gọi khán_giả đạo_diễn nghệ_thuật bản_thể đồng_tính\n","Predicted: (Xe co) --vs-- Real label: (Khoa hoc)\n","--------------------------------------------------\n","diễn sân_khấu giải_a giải_thưởng hội nghệ_sĩ sân_khấu việt_nam trao giải hà_nội tác_phẩm xuất_sắc diễn giải_a trị_giá đồng giải bao_gồm trọn_đời trung_hiếu thăng_long cải_lương đạo_diễn hoa_lư lê_hùng chiến_trường tiếng súng nỏ đỗ_đức_thịnh mỹ_nhân lương_giang miền cải_lương nsnd_doãn hoàng_giang chẳng non dân_ca hồn cảnh_diễn nỏ_thần tác_phẩm giải a _ảnh t quyên thể_loại ấn_phẩm sách nghiên_cứu lý_luận giải_a đồng tác_phẩm đào_tấn giải_thưởng kịch_bản sân_khấu giải_a giải b đồng giai_nhân anh_hùng tác_giả chu_thơm điện_biên btc trao giải_thưởng tác_phẩm nghệ_sĩ nhân_dân kịch nỏ liên_châu cải_lương giải_thưởng linh_khí trung_hiếu đào_tấn\n","Predicted: (Xa hoi) --vs-- Real label: (Van hoa)\n","--------------------------------------------------\n","huyền_thoại elizabeth_taylor qua_đời nữ_hoàng_cleopatra trút hơi thở bệnh_viện angeles triệu_chứng sung nữ diễn_viên nằm viện điều_trị trai taylor mẹ phụ_nữ phi_thường trao cuộc_sống cảm_xúc tình_yêu_thương nồng_nàn đi vô_cùng đau_xót tự_hào đóng_góp thế_giới thành_công lĩnh_vực điện_ảnh kinh_doanh hoạt_động từ_thiện chống căn_bệnh thế_kỷ aids tự_hào đi tâm_hồn mãi sống trái_tim nữ diễn_viên huyền_thoại hollywood diễn giành liên_tiếp giải_thưởng oscar phim tình_yêu điện_ảnh tận đời taylor tiết_lộ đón sinh_nhật giường_bệnh lễ trao giải_oscar diễn hồi cuộc_sống taylor trải thăng_trầm kết_hôn đàn_ông richard_burton trải kết_hôn ly_dị nhắc tình_bạn thân_thiết vua nhạc_pop michael_jackson michael qua_đời taylor có_mặt lễ_tang tâm_hồn tưởng_tượng cuộc_sống oscar elizabeth_taylor qua_đời sung_huyết tâm_hồn kết_hôn huyền_thoại cleopatra đi nữ_diễn_viên tự_hào\n","Predicted: (Xa hoi) --vs-- Real label: (Phap luat)\n","--------------------------------------------------\n","huỳnh_hiểu_minh lâm_phong phẫu_thuật thẩm_mỹ zing_để giúp khuôn_mặt trở_nên hoàn_hảo ngôi_sao nam phẫu_thuật thẩm_mỹ điển_hình huỳnh_hiểu_minh lâm_phong nam diễn_viên trung_quốc huỳnh_hiểu_minh vốn nổi_tiếng màn_ảnh cằm chẻ miễn đụng hàng dư_luận cằm chẻ sản_phẩm phẫu_thuật thẩm_mỹ tờ lá cải đăng ảnh ngôi_sao diệp_vấn nổi_tiếng ảnh hâm_mộ dễ_dàng khuôn_mặt nam diễn_viên nổi_tiếng cằm huỳnh_hiểu_minh chẻ vết sẹo cằm bằng_chứng tài_tử phong_thanh một_mực vết sẹo tai_nạn trẻ nghi_án dao_kéo huỳnh_hiểu_minh đồng_nghiệp lâm_phong ngôi_sao sóng_gió gia_tộc đỉnh_cao thành_công sự_nghiệp lĩnh_vực âm_nhạc giải_nam ca_sỹ nổi_tiếng châu_á thái_bình_dương đầu cạnh tham_gia phim tựa pearl dự_kiến tổ_chức series hòa_nhạc hong_kong danh_tiếng diều gió lâm_phong nhanh_chóng nhân_vật dư_luận mổ_xẻ khuôn_mặt tờ lá chàng diễn_viên kiêm ca_sỹ lâm tiến_hành phẫu_thuật thẩm_mỹ kiểm_chứng ảnh chụp tờ khuôn_mặt mí mắt nhấn sâu mũi chỉnh_sửa một_chút bộ_phận nét khuôn_mặt lâm_phong cằm ảnh chụp trở cằm nam diễn_viên dường_như nhô một_chút chẻ lâm_phong huỳnh_hiểu_minh phẫu_thuật thẩm_mỹ khuôn_mặt lá sẹo nam_diễn_viên tờ ảnh chụp diệp_vấn\n","Predicted: (Giai tri) --vs-- Real label: (Doi song)\n","--------------------------------------------------\n","lê_hạ_anh ngọt_ngào pha điên_điên góp mặt ấn_tượng phim_điện_ảnh đình_đám gái hôm_qua yêu đi đừng sợ hotgirl lê_hạ_anh chứng_minh năng_lực diễn_xuất sự_nghiệp gái xinh_đẹp chuyển_mình mạnh_mẽ hạ_anh ấn_tượng công_chúng đồng_loạt phim điện_ảnh gái hôm_qua yêu đi đừng sợ khán_giả vai diễn may_mắn sự_nghiệp diễn_xuất liên_tiếp phim điện_ảnh liên_tiếp vai diễn màu_sắc vai diễn gái hôm_qua tiếp_cận khán_giả hình_ảnh dịu_dàng nữ thoáng chút buồn trái_ngược đời đạo_diễn tin_tưởng cố_gắng hết_sức truyền_tải nhân_vật mềm_mỏng yêu đi đừng sợ vai diễn ấn_tượng hóa_trang thành ma hù_dọa tác_phẩm hoàn_thành khóc cảnh hồi_tưởng vân phương cực_kỳ vai diễn khâu hóa_trang hơi cực_khổ cười sợ_hãi đoàn tạo_hình lê_hạ_anh phim yêu đi đừng sợ vai diễn chiếm đất diễn hạ_anh nét điện_ảnh vai diễn hạ_anh vô_cùng nhẹ_nhàng tinh_tế khung ảnh hình_tượng theo_đuổi nhẹ_nhàng nữ tinh_tế hình_ảnh diễn_viên nữ yêu_thích hình_ảnh hướng công_chúng đông điện_ảnh thử sức dạng vai màu_sắc ví_dụ hành_động hài bi số_phận cười khán_giả nét duyên lê_hạ_anh gái hôm_qua diễn_xuất đam_mê chứng_minh vai diễn vai mỉm cười sốt_ruột sốt_ruột đoạn đường vai diễn trau_dồi học_hỏi rèn_luyện kinh_nghiệm diễn_xuất nắm_bắt bỡ_ngỡ hoàn_thành trong_nghiệp diễn_xuất trau_dồi kỹ_năng kiến_thức nghề quan_hệ đóng vai_trò quyết_định thành_công diễn_viên trẻ ngành_nghề kiến_thức kỹ_năng quan_hệ xã_hội quan_hệ đồng_nghiệp quyết_định thành_công diễn_viên tạo_dựng quan_hệ xung_quanh hạ trưởng_thành kinh_nghiệm diễn_xuất lẫn ngoại_hình ảnh mễ_thuận gọi_là hot girl danh_xưng thuận_lợi đi casting vai diễn con_người ta trưởng_thành dám hot girl cười casting phim gửi đơn đăng_ký chờ đươc hẹn casting casting học_hỏi bài_học kỹ_năng trưởng_thành diễn bước_đầu thuận_lợi cười vẻ xinh_đẹp ngọt_ngào hạ_anh gặp_gỡ bè_bạn ngọt_ngào điên_điên đa_số trẻ cảm ngọt_ngào pha chút điên_điên cười lôi_cuốn gặp_gỡ giao_tiếp bạn_bè gái xinh_xắn yêu vô_tư nấc cạnh bạn_bè yêu quý hạ_anh dần rũ hình_tượng hotgirl gọi mong_muốn phim trường tham_gia hoạt_động bổ_sung năng_lượng phim yêu_thích học_hỏi diễn đi tập gym cà_phê ăn_uống ngủ dạo bận_rộn dự_án liên_tục phim_trường bật_mí dự_án dạo hơi bận_rộn dự_án bí_mật bật_mí giấu chút_xíu khán_giả yêu_thương theo_dõi ủng_hộ đường hạ_anh trò_chuyện lê_hạ_anh hạ_anh yêu đi đừng sợ trưởng_thành đạo_diễn chứng_minh\n","Predicted: (Nha dat) --vs-- Real label: (Phap luat)\n","--------------------------------------------------\n","mc kỳ_cựu lại_văn_sâm ngẫu_hứng dịch sai giải_thưởng liên_hoan_phim quốc_tế việt_nam chữ tốt_đẹp thành_công điện_ảnh việt_nam liên_hoan tầm_cỡ quốc_tế sân_nhà chữ xa_xỉ khâu tổ_chức lễ khai_mạc chặt_chẽ tổ_chức rời_rạc kịch_bản địa_điểm chương_trình truyền_hình lễ trao giải bế_mạc tối_qua hạt_sạn to_đùng nhắc đoạn trích cánh tôn_vinh đạo_diễn hồng_sến cống_hiến điện_ảnh nước_nhà phát_biểu xúc_động nghệ_sĩ như_quỳnh khán_giả đoạn trích bồi_hồi cảnh kinh_điển mẫu_mực phim đoạn kinh_điển diễn_viên đứa con_nhỏ ngụp máy_bay địch giữa_chừng ý_đồ dựng đoạn phim sự_cố kỹ_thuật chìm đi êm_ái cắt ngang cánh lắc_đầu ngao_ngán hồng_sến phim đi rõ_ràng liên_hoan phim tôn_vinh nhắc mc lại_văn_sâm vinh_danh phụ_nữ công_bố giải diễn_viên nữ xuất_sắc câu bình_luận mc mỹ_uyên kiểu hiện_diện vô_duyên khán_phòng vỗ_tay chúc_mừng phụ_nữ việt_nam kịch_bản_gốc hơi lạc_điệu ngẫu_hứng mc kỳ_cựu lại_văn_sâm chê_trách dịch_thuật lễ rõ_ràng ban tổ_chức dịch đại_biểu nước_ngoài cán_bộ phiên_dịch đọc nội_dung phim lúng_túng nhất_thời dịch phim giải thành nghệ_sĩ phát_biểu thoải_mái sân_khấu kịch_bản đầu cạnh câu_chuyện thú_vị câu_chuyện đạo_diễn đặng_nhật_minh quay_phim phim đông_dương dở phiên_dịch chuyển_tải nội_dung sự_cố tiếc mc lại_văn_sâm chủ_động kiêm phiên_dịch ngô_ngạn_tổ nam diễn_viên hongkong phát_biểu niềm vinh_dự tham_dự liên_hoan phim thành_phố kỷ_niệm năm_tuổi mc nhanh_chóng dịch ngô_ngạn_tổ hạnh_phúc hâm_mộ việt_nam những_tưởng chữa_cháy tại_chỗ nào_ngờ ngô_ngạn_tổ phát_biểu câu thông_điệp khích_lệ kiêm nhân_viên phiên_dịch mạnh_dạn liều_mạng lĩnh chuyển_ngữ kết_quả câu ý_nghĩa mục_đích liên_hoan phim quốc_tế điện_ảnh thế_giới khán_giả địa_phương điện_ảnh địa_phương khán_giả thế_giới biến thành câu khán_giả xếp_hàng phim liên_hoan chao lại_văn_sâm khán_giả truyền_hình xung_quanh phì_cười liên_hoan phim giải_thưởng danh_giá điều_đọng điện_ảnh việt_nam chứng_tỏ bạn_bè quốc_tế học_hỏi xuất_trận ý_kiến quan_chức lẫn nghệ_sĩ diễn_viên phát_biểu tham_dự kiểu tổ_chức ngẫu_hứng bề_bộn thế_này liên_hoan phim quốc_tế việt_nam gọi_là tầm quốc_tế p v_vietnam mc lại_văn_sâm ngô_ngạn_tổ phiên_dịch liên_hoan_phim quốc_tế điện_ảnh ngẫu_hứng liên_hoan_phim phát_biểu phim\n","Predicted: (Kinh te) --vs-- Real label: (Xa hoi)\n","--------------------------------------------------\n","midu thanh_hằng tàn_nhẫn bóp mũi mẹ chồng teaser giới_thiệu phim mẹ chồng tung đoạn teaser hé_lộ tình_tiết quan_hệ phim tiếp_nối uất_ức khổ_đau ba_trân_thanh_hằng chịu_đựng phẫn_uất nhân gấp_bội chứng_kiến cảnh mẹ chồng cưới vợ bé chồng vợ bé màu_sắc bi_kịch diễn_biến teaser lan_khuê dâu trướng mẹ chồng thanh_hằng vai_trò nàng dâu hai_phước lâm_vinh nỗi lo lan_khuê midu vào_vai tuyết_mai vợ hai vai dâu mẹ chồng thanh_hằng tiếp_quản phim lâm_vinh_hải chàng trai khờ khạo ngốc nghếch tình_yêu anh_chàng mẹ thanh_hằng vợ lan_khuê lan khuê vợ lâm_vinh_hải chăm_sóc chơi_đùa cô_dâu trẻ_trung xinh_xắn lâm_vinh_hải cô_dâu cư_xử lạ phụ_nữ liệu cuộc_chiến ngầm phụ_nữ đối_lập ngoại_hình lẫn phong_thái chế_độ đa_thê chú_ý cảnh teaser nhân_vật thanh_hằng bóp mũi mẹ chồng diễm_my x bi_thương nếm_trải sức_ép gìn_giữ duy_trì nếp dòng_họ ba_trân phụ_nữ lòng_dạ độc_ác bóp mũi mẹ chồng diễm_my uất_ức ba_trân phụ_nữ hiểm_ác tàn_nhẫn poster nhân_vật tung sắc mẹ chồng dự_kiến khởi_chiếu toàn_quốc khuê ba_trân_thanh_hằng hai_phước lâm_vinh khuê phẫn_uất ngọc_quyên uất_ức diễm_my tuyết_mai nếm_trải poster chơi_đùa gìn_giữ tiếp_quản\n","Predicted: (Van hoa) --vs-- Real label: (The gioi)\n","--------------------------------------------------\n","minh_béo mẹ đi ra_mắt cân sức khỏe sức_khỏe nhẹ_cân khỏe nhẹ nhập_viện hoài bệnh_hoạn nghiêm_trọng mẹ đi ra_mắt tận mỹ khó_tính đi minh_béo minh_béo gắn học trường sân_khấu điện_ảnh gọi nghệ_danh gia_đình quyết_định minh_béo dễ_thương nhẹ_nhõm ngờ ấn_tượng cân cân sức khỏe sức_khỏe nhẹ_cân khỏe nhẹ nhập_viện hoài bệnh_hoạn nghiêm_trọng khuyến_khích béo_phì gen ba_mẹ quy_định cơ_địa trời ban sức khỏe cố cuộc_sống vẻ tố_chất tận_dụng cống_hiến cuộc_sống mặc_cảm minh béo diễn_viên công_việc yêu_thích nỗ_lực hết_mình hình_ảnh kém ấn_tượng chậm đấy cười ăn_uống kiêng_cữ thoải_mái cơm rau uống trái câu uống hoa_quả tập_thể_dục thường_xuyên bài_tập_thể_dục tập đi tuần đi bơi thi bước_nhảy hoàn_vũ luyện bộ_môn trông trẻ bí_quyết tinh_thần khoái yêu_đời vui_vẻ trầm_tư u_buồn cười đi ý_kiến cười nếp nhăn ông_bà ta câu nụ cười thuốc_bổ nụ cười thoái mái nụ cười khán_giả cuộc_đời tươi_trẻ cuộc_sống gia_đình hẳn đầy_ắp niềm vui cuộc_sống suy_nghĩ làm_việc cuộc_sống niềm buồn gia_đình buồn sống rât nói_thẳng đôi_khi kia hôn_sự đơn duyên_số hợp thương_yêu hi_sinh kết_hôn xuất_phát tình_cảm chân_thành kết_hôn vội_vàng nhanh_chóng chia_tay nghệ_sĩ công_khai tình_cảm mặt bí_mật minh_béo công_bố riêng_tư mặt khán_giả một_chút riêng_tư khán_giả kết_hôn tổ_chức nhẹ_nhàng yên_ắng rình_rang mẹ can_thiệp tình_cảm út mẹ đi ra_mắt tận mỹ cười khó_tính đi chân_thành tâm_đầu hề dễ_dàng đôi_khi báo_chí kết_hôn vợ đi hướng đi tu quan_niệm hạnh_phúc gia_đình minh_béo ta hạnh_phúc đôi_khi cạnh quan_niệm hạnh_phúc đơn_giản mãn_nguyện gia_đình sống nghiêm_túc khán_giả gia_đình tổ_chức đi đi mẹ mong_muốn gia_đình gia_đình đầm_ấm nằm vòng bí_mật gia_đình vn minh_béo gia_đình mẹ cân sức_khỏe nụ cười cuộc_sống bệnh_hoạn uống nhập_viện\n","Predicted: (Khoa hoc) --vs-- Real label: (Van hoa)\n","--------------------------------------------------\n","minh_hằng tranh_cãi cảnh sướng tê minh_hằng môi điêu_luyện minh_hằng vai_trò ca_sĩ kiêm diễn_viên điện_ảnh nghệ_thuật gái ngấp_nghé tạo_dựng tên_tuổi sự_nghiệp gia_sản ước_mơ minh_hằng gắn liền hình_ảnh trong_sáng dễ_thương ảnh hạnh_phúc sự_nghiệp diễn tham_gia phim thành_công gọi_giấc mơ giải_cứu thần chết hạnh_phúc nữ diễn_viên tuyên_bố từ_chối dự_án phim kịch_bản phân_cảnh nhạy_cảm hình_ảnh minh_hằng thoáng hở táo_bạo phim minh_hằng lương_mạnh_hải đối_tác quen_thuộc nụ_hôn nóng_bỏng làng phim dính tin_đồn tình_ái hạnh_phúc phiên_bản việt_hóa khóc đi khóc cặp đôi ví_von tiên đồng ngọc_nữ màn_ảnh việt hâm_mộ minh_hằng diễn phim đóng khóa môi điêu_luyện cặp đôi ví_von tiên đồng ngọc_nữ màn_ảnh yêu phim tình_cảm nhẹ_nhàng tình_yêu đi xuyên kiếp kiếp đạo_diễn dustin nguyễn phim khán_giả dễ_dàng cuốn_hút câu_chuyện yếu_tố siêu_nhiên ma_mị nhân_vật linh minh_hằng đảm_nhận cảnh yêu táo_bạo minh_hằng yêu phim tình_yêu lứa cảnh ân_ái gia_vị ngọt_ngào thích_thú fan minh_hằng lột_xác táo_bạo vai diễn nhí_nhảnh trong_sáng thuở hạnh_phúc tiết_lộ đối_mặt đóng cảnh yêu uống bia giúp độ phiêu dũng_cảm phim cảnh cưỡng_bức minh_hằng thể_hiện ấn_tượng diễn_đạt cảnh giằng_xé đau_đớn nhân_vật minh_hằng hoảng_loạn đi cảnh nhạy_cảm phim yêu hình_ảnh tranh_cãi minh_hằng fan ngỏ_ý bất_bình thần_tượng scandal chấp_nhận đóng cảnh nóng cảnh yêu nghệ_thuật rõ_ràng trình_độ diễn_xuất minh_hằng tiến_bộ minh_hằng ôm hôn_bạn diễn thắm_thiết phim vương ra_mắt công_chúng mưa tranh_cãi lùm_xùm nam diễn_viên điển_trai phim đua khoe_thân thu_hút chú_ý khán_giả cảnh phim đỏ_mặt minh_hằng cảnh_hôn cháy_bỏng diễn kém cảnh phim minh_hằng gợi_cảm áo trắng ướt_sũng nụ cháy minh_hằng táo_bạo cảnh khoe_vóc_dáng màn_ảnh phim hạnh_phúc đi bikini vô_cùng quyến_rũ cảnh vải minh_hằng đi lệ_phí tình_yêu minh_hằng fan nghẹt_thở màn khóa môi diễn huy_khánh huy_khánh đàn_ông kinh_nghiệm diễn sâu nụ_hôn màn_ảnh dữ_dội đỏ_mặt cảnh khóa môi tranh_cãi minh_hằng ma vương giải_cứu thần chết hạnh_phúc gọi_giấc mơ lệ_phí tình_yêu yêu cưỡng_bức lột_xác quan_hệ tình_dục hoảng_loạn\n","Predicted: (The thao) --vs-- Real label: (Giai tri)\n","--------------------------------------------------\n","gia_sư tiểu_thuyết nhận_dạng hạnh_phúc coi tiểu_thuyết giáo_dục thế_kỷ định_nghĩa thực_tiễn tình_yêu hạnh_phúc nguyên bạn_đọc thời agnes gia_sư tiểu_thuyết đầu_tay anne tiểu_thuyết_gia nổi_tiếng emily đồi gió hú charlotte nổi_tiếng đồi gió hú đóng_góp quý_giá văn_học nhân_văn thông_điệp hữu_ích xuất_bản đầu tái_bản giới_thiệu rộng_rãi thế_giới george coi tiểu_thuyết_gia hiện_đại khen tác_phẩm văn_xuôi hoàn_hảo văn_học tiểu_thuyết gia_sư nhà_xuất_bản hà_nội công_ty hành câu_chuyện gái mục_sư nghèo sống miền bắc thế_kỷ sinh tình yêu_thương giáo_dục tiến_bộ sáng_suốt cha_mẹ truyền cảm_hứng hôn_nhân dựa tình_yêu tôn_trọng lẫn khát_khao vun_đắp hạnh_phúc agnes quyết_tâm thế_giới cuộc_sống tự_lập hữu_ích gia_đình lâm cảnh túng_thiếu công_việc gia_sư lường cực_nhọc đối_mặt tiểu_thư công_tử gia_đình giàu_có phục_vụ ngạo_mạn hống hách ngang bướng coi_thường tri_thức giáo_dục tình trách_nhiệm lựa_chọn kêu_ca than_vãn cố_gắng chịu_đựng thử_thách công_việc gia_sư phân_biệt bất_công cô_đơn cố_gắng thu_lượm chút thành_công ít_ỏi động_viên tích_góp bài_học quý_giá trải_nghiệm hành_trình gian_nan can_ngăn buộc chứng_kiến cảnh học_trò nàng tiểu_thư xinh_đẹp kiêu_ngạo chăm_chút vẻ bề_ngoài hào_nhoáng hôn_nhân tình_yêu thỏa_mãn tham_vọng sở_hữu dinh_thự đẹp khu vườn cảnh bao quanh bất_chấp sống chồng tồi_tệ ngắn đám_cưới tiểu_thư thú_nhận nàng sống cuộc_đời phí hoài bất_hạnh thất_vọng agnes trái_ngược hẳn học_trò nổi_bật kho báu tâm_hồn trí_tuệ ngừng trau_dồi trân_trọng agnes kiên_nhẫn đợi niềm hy_vọng hạnh_phúc đích_thực mẹ trường tư_thục tình_cờ mục_sư đàn_ông nhân_cách kính_trọng thầm yêu gia_sư tình_yêu ấp_ủ thổ_lộ hạnh_phúc mỉm cười sống cuộc_sống hạnh_phúc cha_mẹ công_trình vun_đắp đứa sinh tình yêu_thương coi tiểu_thuyết giáo_dục thế_kỷ định_nghĩa thực_tiễn tình_yêu hạnh_phúc nguyên bạn_đọc thời trẻ trang_bị vốn_sống ảo_tưởng sai_lầm mê_cung phù_phiếm phù_du sống bất_hạnh nuối_tiếc nuôi_dạy môi_trường giáo_dục nhận_dạng hạnh_phúc đích_thực tiếp_cận tận_hưởng hạnh_phúc bạn_đọc câu trả_lời trang tiểu_thuyết sinh_động chân_thực nhật_ký chiêm_nghiệm chủ_đề tiểu_thuyết gia_sư đồi gió tiểu_thuyết nhà_xuất_bản hà_nội cộng_hòa ireland\n","Predicted: (Xa hoi) --vs-- Real label: (Kinh te)\n","--------------------------------------------------\n","ngắm bản_sao giải_thưởng thường_niên hiệp_hội diễn_viên mỹ diễn tạp_chí people thống_kê thú_vị con_người bình_thường sở_hữu ngoại_hình y_hệt ngôi_sao vinh_danh đề_cử giải bản_sao hoàn_hảo mỹ_nhân hoa_ngữ giải_thưởng tôn_vinh đóng_góp diễn_viên môn nghệ_thuật thứ_bảy đứng oscar cầu_vàng quy_mô uy_tín so_sánh ngôi_sao trao award giải_thưởng hiệp_hội diễn_viên mỹ tôn_vinh diễn_viên cống_hiến làng phim_ảnh giải_thưởng tổ_chức thường_niên giải_oscar quy_mô uy_tín gái thành_phố colorado vô_cùng hãnh_diện quyến_rũ vinh_dự sinh_viên trẻ gương_mặt kiểu tóc tương_đồng angie sở_hữu hình_xăm cơ_thể frank leonardo_dicaprio bay los_angeles kỷ_niệm sinh_nhật ô_tô đại_lộ hollywood hướng_dẫn tour leonardo_dicaprio thàng phố missouri tâm_sự chàng sinh_viên làm_việc bảo_tàng titanic công_việc trông đấy thổ_lộ jennifer_aniston phim friends phát_sóng truyền_hình tóc ngắn nhuộm đen bảo trông gái show truyền_hình mẹ mái_tóc vàng nhầm_lẫn tâm_sự gương gương_mặt vợ cũ brad_pitt mời công_bố giải david george_clooney george_clooney gương_mặt trang_bìa tạp_chí people danh_hiệu quý quyến_rũ đương_đại tình_cờ đứng sạp phụ_nữ hàng chằm_chằm rời ta lắm thành_phố khoảnh_khắc chú_ý duy_nhất david george_clooney david làm_việc phim ngắm_nghía chụp hình david brad_pitt chỗ ta trông brad ông_xã david học xong đại_học cắt tóc ngắn liên_tục nhầm hilary_duff lớp bảo bé thần_tượng teen hilary_duff đi học trung_học gọi lizzie mẫu tóc trẻ thổ_lộ sara katherine đi chằm_chằm mặt bảo katherine_heigl sara ngôi_sao cưới vinh_danh tối truyền_hình chiếu phim katherine chú_ý mặc_dù giới_thiệu new_haven đứng xếp_hàng địa_điểm công_cộng gọi nhà_thơ nữ xinh_đẹp diễn_viên đề_cử award giọng kỳ_quặc thành_phố utah film_festival paparazzi chĩa ống_kính buồn_cười chuyên_gia trang_điểm ngưỡng_mộ quyến_rũ tài_năng nữ diễn_viên yêu_mến katie reese_witherspoon suốt liền nhầm nàng luật_sư bằng_cấp reese_witherspoon mẹ chụp chụp gái mary fan cuồng_nhiệt ngôi_sao line reese giành tượng vàng danh_hiệu nữ diễn_viên xuất_sắc giải vai diễn ngôi_sao hiệp_hội diễn_viên tạp_chí people giải_thưởng vinh_danh bản_sao đề_cử brad_pitt chằm_chằm dicaprio\n","Predicted: (Nha dat) --vs-- Real label: (Giao duc)\n","--------------------------------------------------\n","phim giả_tình hollywood kết có_hậu 2s ao cặp đôi nồng_nàn tình ta thư ban_đầu mái ấm gia_đình hạnh_phúc julia roberts cộng_tác phim nhựa tình_cảm đàn_bà đẹp trúng tiếng sét ái_tình quay_phim quyết_định đám_cưới gia_đình bé sinh_đôi henry phụ_nữ đẹp mê_hoặc thế_giới gia_đình mẹ đi chợ cừ khoản chăm_sóc dạy_dỗ con_cái chìa_khóa sắc_đẹp mỉm cười yêu quý gia_đình sống hạnh_phúc biệt_thự hạng new_york california mỹ kém hạnh_phúc đóng phim quyết_định kết_hôn chào_đón đứa đầu_lòng cặp đôi sống malibu nữ diễn_viên tâm_sự tờ marie_claire lạ_kì cảm_giác yêu david jennifer garner ben_affleck phim_trường gặp_gỡ duyên_số diễn_viên nổi_tiếng ben_affleck jennifer_garner độ ben affleck vào_vai chàng luật_sư mù tài_nghệ siêu_phàm biệt_danh jennifer_garner vào_vai mặc_dù người_thương chàng cặp jennifer_lopez nàng đính_hôn duy_trì quan_hệ tình_bạn ra_mắt thành cặp tổ_ấm bé gái xinh_xắn bé trai kháu_khỉnh bí_mật hạnh_phúc jennifer_garner tờ marie mắng mỏ giận hành_động lạ_lùng la_hét giải_quyết xung_đột trưởng_thành hollywood vánh ngôi_sao cơm_bữa gia_đình hạnh_phúc cặp đôi ben ngưỡng_mộ zac vanessa_hudgens cặp teen zac_efron vanessa_hudgens đổ đóng cặp phim nhạc_kịch hấp_dẫn giới trẻ high loạt scandal ảnh nóng tung mạng tình_cảm nồng_thắm thư bình_chọn hoàn_hảo hollywood katherine cặp đôi katherine_heigl trường_quay mv tình đẹp ngôi_sao xinh_đẹp đoạt giải emmy katherine_heigl tình_yêu đích_thực cuộc_đời đóng mv rocker chồng kết_hôn park nuôi bé gái hàn_quốc nữ diễn_viên nụ cười khả_ái hollywood con_nuôi bày_tỏ degeneres cưới chấp_nhận xảy hiện sống hạnh_phúc jessica alba nhà_sản_xuất ghen_tị chàng trai thế_giới giành trái_tim phụ_nữ quyến_rũ thế_giới quen yêu alba đóng phim fantastic_four hồi đám_cưới đón bé gái đầu_lòng diễn_viên xinh_đẹp tâm_sự tờ ok kết_hôn cần_thiết đồng_hành suốt cuộc_đời trao tình_yêu vô_điều_kiện will_smith diễn_viên will_smith coi tài_hoa thành_công lĩnh_vực âm_nhạc điện_ảnh truyền_hình đóng cặp phim_truyền_hình will_smith jada yêu jada ngã_lòng khiếu hài_hước dịu_dàng will_smith kết_hôn trai jaden gái trai jaden khán_giả phim hạnh_phúc bố phát_biểu tờ gia_đình số_một will_smith chồng mẫu mực yêu gia_đình sống thuốc_lá rượu gia_đình thân_yêu hayden christensen đóng phim nổi_tiếng công_chiếu hồi diễn yêu bí_mật đính_hôn rachel hạnh_phúc cầu_hôn người_yêu hưởng_thụ hạnh_phúc aishwarya_rai cặp đôi ấn_độ đóng phim phim chính_thức yêu đám_cưới chờ đón ấn_độ hoa_hậu thế_giới aishwarya_rai ấn_độ tổ_chức linh_đình hồi miley cyrus liam_hemsworth đóng phim the_last_song phim giả_tình hạnh_phúc hẹn_hò miley úc gặp_mặt cha_mẹ liam anh_chàng vui_mừng tuyệt_khi miley sinh thế_giới thế_giới miley thổ_lộ liam tình_yêu đầu angelina_jolie brad_pitt cặp đôi đình_đám hollywood đóng phim hành_động mr nam tài_tử brad_pitt đẹp angelina_jolie nhóc_tì bao_gồm con_đẻ con_nuôi cặp vàng báo_giới tốn giấy_mực xung_quanh tin_đồn chia_tay tái_hợp áp_lực nuôi dạy đứa trẻ công_việc bận_rộn tình_cảm trục_trặc cuộc_sống hôn_nhân coi cặp đôi đẹp tài_năng ảnh_hưởng hollywood robert_pattinson kristen_stewart tin_đồn tình_cảm chàng ma cà_rồng đẹp_trai robert nàng người_yêu xinh_đẹp kristen chạng_vạng ra_mắt fan phim phủ_nhận tin_đồn kristen hẹn_hò trai trăng_non tình_cảm tiến_triển mạnh_dạn công_bố báo_giới phim_trường se_duyên ngôi_sao công_bố hẹn_hò lễ trao giải_bafta đánh_quả lẻ thu_hiền tổng_hợp will_smith jennifer_garner hạnh_phúc katherine angelina_jolie phim bé gái\n","Predicted: (Phap luat) --vs-- Real label: (Kinh te)\n"]}],"source":["def check_wrong(real_values, predicts):\n","    wrong_arr = []\n","    wrong_label = []\n","    for i in range(len(predicts)):\n","        if predicts[i] != real_values[i]:\n","            wrong_arr.append(i)\n","            wrong_label.append(predicts[i])\n","    return wrong_arr, wrong_label\n","\n","for i in range(15):\n","    print('-'*50)\n","    wrong_arr, wrong_label = check_wrong(real_values, predicts)\n","    print(test_df.iloc[wrong_arr[i]].processed_content)\n","    print(f'Predicted: ({class_names[wrong_label[i]]}) --vs-- Real label: ({class_names[real_values[wrong_arr[i]]]})')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eFJh4_cnnQ-F"},"outputs":[],"source":["# Load model\n","models = []\n","for fold in range(skf.n_splits):\n","    model = NewsClassifier(n_classes=13)\n","    model.to(device)\n","    model.load_state_dict(torch.load(f'{path}phobert_fold{fold+1}.pth'))\n","    model.eval()\n","    models.append(model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_gbQSKLAjqL2"},"outputs":[],"source":["def infer(text, tokenizer, models, class_names, max_len=512):\n","    encoded_review = tokenizer.encode_plus(\n","        text,\n","        max_length=max_len,\n","        truncation=True,\n","        add_special_tokens=True,\n","        padding='max_length',\n","        return_attention_mask=True,\n","        return_token_type_ids=False,\n","        return_tensors='pt',\n","    )\n","\n","    input_ids = encoded_review['input_ids'].to(device)\n","    attention_mask = encoded_review['attention_mask'].to(device)\n","\n","    # Taking the average of predictions from 5 models\n","    total_outs = []\n","    for model in models:\n","        model.eval()\n","        with torch.no_grad():\n","            output = model(input_ids, attention_mask)\n","            total_outs.append(output)\n","\n","    total_outs = torch.stack(total_outs)\n","    mean_output = total_outs.mean(0)\n","    _, y_pred = torch.max(mean_output, dim=1)\n","\n","    confidence_scores = mean_output.softmax(dim=1).cpu().numpy()\n","\n","    # Create DataFrame with confidence scores\n","    df_confidence = pd.DataFrame(confidence_scores, columns=class_names)\n","\n","    predicted_label = class_names[y_pred.item()]\n","\n","\n","    return df_confidence, predicted_label\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_8_J85GEm_s2"},"outputs":[],"source":["\n","text = \"\"\"\n","Dự án Trung tâm Nghiên cứu khoa học công nghệ hạt nhân (CNST) được thực hiện theo Hiệp định Liên Chính phủ ký năm 2011. Dự án đã được Chính phủ Việt Nam phê duyệt chủ trương đầu tư năm 2018. CNST dự kiến đặt tại TP Long Khánh, Đồng Nai. Trung tâm này sẽ có lò phản ứng hạt nhân dạng bể, công suất 10 MW, sử dụng nhiên liệu độ giàu thấp do Nga chế tạo. CNST tập trung lĩnh vực vật liệu chiếu xạ, khoa học sinh học, đồng vị phóng xạ, kỹ thuật lò phản ứng, an toàn bức xạ; nghiên cứu điều chế dược chất mới trong điều trị ung thư, nghiên cứu chiếu xạ silic - vật liệu bán dẫn, tán xạ góc nhỏ...\n","\n","Ông Trần Chí Thành, Viện trưởng Viện Năng lượng nguyên tử (Bộ Khoa học và Công nghệ) cho biết \"đây là lần đầu tiên Việt Nam triển khai một dự án về xây dựng lò phản ứng hạt nhân nghiên cứu công suất lớn\".\n","\n","Theo ông Thành, để triển khai Dự án CNST, Bộ Khoa học và Công nghệ đã có những phương án chuẩn bị nguồn nhân lực quản lý và triển khai ở các giai đoạn khác nhau. Bộ cũng đưa ra kế hoạch chuẩn bị nguồn nhân lực cho vận hành đảm bảo an toàn, khai thác hiệu quả Trung tâm sau khi đi vào hoạt động.\n","\n","Để hỗ trợ thẩm tra, thẩm định Báo cáo nghiên cứu khả thi, Báo cáo phân tích an toàn và hồ sơ thiết kế, Bộ Khoa học và Công nghệ đề nghị Tập đoàn Năng lượng Nguyên tử Quốc gia Liên bang Nga (Rosatom) tạo điều kiện cho một số cán bộ Việt Nam tham gia thực hiện thiết kế cơ sở của lò phản ứng và các tính toán, phân tích an toàn đi kèm. Rosatom cũng giúp Việt Nam trong đào tạo cán bộ vận hành lò phản ứng nghiên cứu.\n","\n","Viện Năng lượng nguyên tử Việt Nam cũng xây dựng các nhóm chuyên môn sâu về vật lý lò, thiết kế sử dụng kênh ngang, sản xuất đồng vị phóng xạ trên lò nghiên cứu, nghiên cứu vật liệu, chiếu xạ silic làm bán dẫn, nghiên cứu phân tích kích hoạt, bảo vệ môi trường, an toàn hạt nhân. Điều này nhằm xây dựng nguồn cán bộ nghiên cứu, ứng dụng khai thác hiệu quả lò nghiên cứu mới, đảm bảo an toàn khi CNST đi vào hoạt động\n","\n","Trước đó tháng 10/2017, Viện ký thỏa thuận hợp tác với Trường Đại học nghiên cứu Bách khoa Tomsk và Đại học Nghiên cứu Hạt nhân Quốc gia Nga (MEPhI) vào tháng 12/2023, về hợp tác nghiên cứu và đào tạo cán bộ trong các lĩnh vực năng lượng nguyên tử có liên quan.\n","\n","Viện trưởng Trần Chí Thành cho biết thêm, trước mắt Việt Nam và Nga sẽ tập trung đẩy mạnh triển khai thực hiện Dự án đảm bảo đúng tiến độ, hiệu quả, tuân thủ các quy định của Cơ quan Năng lượng nguyên tử quốc tế (IAEA).\n","\n","Theo Quy hoạch phát triển, ứng dụng năng lượng nguyên tử giai đoạn 2021 - 2030, tầm nhìn 2050, hướng nghiên cứu ứng dụng năng lượng nguyên tử sẽ tập trung cả khoa học cơ bản (vật lý hạt nhân, vật lý lò, an toàn và thủy nhiệt, tự động điều khiển, vật liệu, hóa học ...) và ứng dụng trong y tế (y học bức xạ) nông nghiệp; công nghiệp; tài nguyên môi trường (nước ngầm, ô nhiễm, phát tán phóng xạ, xói mòn đất, chất thải phóng xạ, đuôi quặng)...\n","\n","Ngoài ra, trong quy hoạch phát triển ứng dụng năng lượng nguyên tử giai đoạn tới sẽ nghiên cứu tiền khả thi dự án xây dựng tổ hợp máy gia tốc lớn đặt tại miền Bắc, xây dựng các phòng thí nghiệm công nghệ và an toàn hạt nhân...\n","\"\"\"\n","df_confidence, predicted_label = infer(text, tokenizer, models, class_names, max_len=512)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":89},"id":"5wuJYJAWpAHr","outputId":"de872e44-c395-4b5b-972a-2ee93e19de59"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"df_confidence\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Cong nghe\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.296507828868926e-05\n        ],\n        \"semantic_type\": 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\"float32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9983345866203308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kinh te\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.00016388282529078424\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Nha dat\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0002617032441776246\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Phap luat\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6.219554779818282e-05\n        ],\n        \"semantic_type\": 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<thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Cong nghe</th>\n","      <th>Doi song</th>\n","      <th>Giai tri</th>\n","      <th>Giao duc</th>\n","      <th>Khoa hoc</th>\n","      <th>Kinh te</th>\n","      <th>Nha dat</th>\n","      <th>Phap luat</th>\n","      <th>The gioi</th>\n","      <th>The thao</th>\n","      <th>Van hoa</th>\n","      <th>Xa hoi</th>\n","      <th>Xe co</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.000073</td>\n","      <td>0.000441</td>\n","      <td>0.000041</td>\n","      <td>0.000231</td>\n","      <td>0.998335</td>\n","      <td>0.000164</td>\n","      <td>0.000262</td>\n","      <td>0.000062</td>\n","      <td>0.000075</td>\n","      <td>0.00003</td>\n","      <td>0.000083</td>\n","      <td>0.000145</td>\n","      <td>0.000059</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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