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TextClassification-PhoBERT

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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OIZejmI9s81t","outputId":"6aa64326-493f-4512-f100-e149fc4fc044"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","path =  \"/content/drive/My Drive/\""]},{"cell_type":"code","source":["!pip install underthesea"],"metadata":{"collapsed":true,"id":"5_PuAi_DEpot"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install phonlp"],"metadata":{"id":"rBDxyx1OE0hL"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Import Packages"],"metadata":{"id":"ZcxcP1wLtMOp"}},{"cell_type":"code","source":["import tensorflow as tf\n","from tensorflow.compat.v1 import ConfigProto\n","from tensorflow.compat.v1 import InteractiveSession\n","from tensorflow.keras.models import Sequential, load_model\n","from tensorflow.keras.layers import Input, Bidirectional, LSTM, Dropout, Dense\n","from tensorflow.keras.optimizers import Adam\n","from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n","from transformers import AutoModel, AutoTokenizer\n","import pandas as pd\n","import pickle\n","import numpy as np\n","import time\n","import json\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import underthesea\n","import re\n","import phonlp\n","import torch"],"metadata":{"id":"yk_WVsSbtPDX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Function to load models based on model_type\n","def load_models(model_type):\n","\n","    if model_type == 'bilstm_phobertbase':\n","      # Load features and tokenizer\n","        with open(path + 'features_162k_phobertbase.pkl', 'rb') as f:\n","              data_dict = pickle.load(f)\n","        tokenizer = AutoTokenizer.from_pretrained(\"vinai/phobert-base-v2\")\n","        phobert = phobert = AutoModel.from_pretrained(\"vinai/phobert-base-v2\")\n","        max_len = 256\n","        # Load hyperparameters\n","        with open(path + 'hyperparameters/BiLSTM_phobertbase.json', 'r') as json_file:\n","              hyperparameters = json.load(json_file)\n","\n","    else:\n","        raise ValueError(\"Invalid model type specified.\")\n","\n","    return tokenizer, data_dict, hyperparameters, max_len, phobert"],"metadata":{"id":"IEJQFF3jt7iA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Load data"],"metadata":{"id":"UTghkub8ujX0"}},{"cell_type":"code","source":["# Load model-specific data and configurations\n","tokenizer, data_dict, hyperparameters, max_len, phobert = load_models(\"bilstm_phobertbase\")"],"metadata":{"id":"1RaTIxKYukVk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load training, validation, and testing data\n","X_train = np.array(data_dict['X_train'])\n","X_val = np.array(data_dict['X_val'])\n","X_test = np.array(data_dict['X_test'])\n","y_train = data_dict['y_train'].values.astype(int)\n","y_val = data_dict['y_val'].values.astype(int)\n","y_test = data_dict['y_test'].values.astype(int)"],"metadata":{"id":"HvFkwujFu4QH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(X_train.shape, X_test.shape, X_val.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0lW1HiXUu634","outputId":"7e60f92d-823b-4050-e595-88970836877a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(113932, 1, 768) (24291, 1, 768) (24126, 1, 768)\n"]}]},{"cell_type":"markdown","source":["## Build Model"],"metadata":{"id":"1jHUq1kOu7_R"}},{"cell_type":"code","source":["# Function to build the BiLSTM model\n","def build_bilstm_model(X_train, y_train, lstm_units_1, lstm_units_2, dense_units, dropout_rate, learning_rate):\n","    model = Sequential()\n","    # Input layer with the shape based on X_train\n","    model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))\n","\n","    # First BiLSTM layer with dropout\n","    model.add(Bidirectional(LSTM(lstm_units_1, return_sequences=True)))\n","    model.add(Dropout(dropout_rate))\n","\n","    # Second BiLSTM layer with dropout\n","    model.add(Bidirectional(LSTM(lstm_units_2, return_sequences=False)))\n","    model.add(Dropout(dropout_rate))\n","\n","    # Dense layer with ReLU activation and dropout\n","    model.add(Dense(dense_units, activation='relu'))\n","    model.add(Dropout(dropout_rate))\n","\n","    # Final Dense layer with softmax activation\n","    model.add(Dense(y_train.shape[1], activation='softmax'))\n","\n","    # Adam optimizer with the specified learning rate\n","    optimizer = Adam(learning_rate=learning_rate)\n","\n","    # Compile the model with categorical crossentropy loss and accuracy metric\n","    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n","\n","    return model\n","\n","# Use the hyperparameters to build the model\n","lstm_units_1 = hyperparameters['lstm_units_1']\n","lstm_units_2 = hyperparameters['lstm_units_2']\n","dense_units = hyperparameters['dense_units']\n","dropout_rate = hyperparameters['dropout_rate']\n","learning_rate = hyperparameters['learning_rate']\n","epochs = hyperparameters['epochs']\n","batch_size = hyperparameters['batch_size']"],"metadata":{"id":"YnnBzosiu9lq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Build the BiLSTM model\n","model = build_bilstm_model(X_train, y_train, lstm_units_1, lstm_units_2, dense_units, dropout_rate, learning_rate)\n","model.summary()\n","\n","# Print model summary and save model architecture diagram\n","tf.keras.utils.plot_model(model=model, show_shapes=True, dpi=76, to_file=path + 'bilstm_phobertbase_summary.png')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"UW101bI5vhOo","outputId":"3f97f11c-62a1-4fe8-b302-58dc9040cba7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," bidirectional (Bidirection  (None, 1, 448)            1779456   \n"," al)                                                             \n","                                                                 \n"," dropout (Dropout)           (None, 1, 448)            0         \n","                                                                 \n"," bidirectional_1 (Bidirecti  (None, 288)               683136    \n"," onal)                                                           \n","                                                                 \n"," dropout_1 (Dropout)         (None, 288)               0         \n","                                                                 \n"," dense (Dense)               (None, 160)               46240     \n","                                                                 \n"," dropout_2 (Dropout)         (None, 160)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 13)                2093      \n","                                                                 \n","=================================================================\n","Total params: 2510925 (9.58 MB)\n","Trainable params: 2510925 (9.58 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","_________________________________________________________________\n"]},{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# Start training time measurement\n","start_time = time.time()\n","\n","# Train the model\n","history = model.fit(\n","    X_train, y_train,\n","    epochs=epochs,\n","    batch_size=batch_size,\n","    validation_data=(X_val, y_val)\n",")\n","\n","# End training time measurement\n","end_time = time.time()\n","\n","# Calculate training time\n","training_time = end_time - start_time\n","print(f'Training time: {training_time:.2f} seconds')\n","\n","# Save training time to JSON file\n","training_time_data = {\n","    'training_time_seconds': training_time\n","}"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VkEMoJclvrP5","outputId":"9d3fb2bb-f212-42ae-8129-83d8e1a099e5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","594/594 [==============================] - 17s 13ms/step - loss: 0.6482 - accuracy: 0.7974 - val_loss: 0.4288 - val_accuracy: 0.8540\n","Epoch 2/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.4604 - accuracy: 0.8516 - val_loss: 0.4039 - val_accuracy: 0.8618\n","Epoch 3/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.4353 - accuracy: 0.8582 - val_loss: 0.3959 - val_accuracy: 0.8655\n","Epoch 4/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.4166 - accuracy: 0.8632 - val_loss: 0.3809 - val_accuracy: 0.8694\n","Epoch 5/30\n","594/594 [==============================] - 7s 11ms/step - loss: 0.4020 - accuracy: 0.8670 - val_loss: 0.3759 - val_accuracy: 0.8702\n","Epoch 6/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.3850 - accuracy: 0.8706 - val_loss: 0.3698 - val_accuracy: 0.8723\n","Epoch 7/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.3773 - accuracy: 0.8741 - val_loss: 0.3666 - val_accuracy: 0.8742\n","Epoch 8/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.3642 - accuracy: 0.8777 - val_loss: 0.3549 - val_accuracy: 0.8756\n","Epoch 9/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.3564 - accuracy: 0.8795 - val_loss: 0.3592 - val_accuracy: 0.8761\n","Epoch 10/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.3444 - accuracy: 0.8827 - val_loss: 0.3551 - val_accuracy: 0.8774\n","Epoch 11/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.3372 - accuracy: 0.8845 - val_loss: 0.3508 - val_accuracy: 0.8776\n","Epoch 12/30\n","594/594 [==============================] - 7s 11ms/step - loss: 0.3289 - accuracy: 0.8878 - val_loss: 0.3463 - val_accuracy: 0.8778\n","Epoch 13/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.3206 - accuracy: 0.8906 - val_loss: 0.3471 - val_accuracy: 0.8788\n","Epoch 14/30\n","594/594 [==============================] - 7s 12ms/step - loss: 0.3141 - accuracy: 0.8918 - val_loss: 0.3451 - val_accuracy: 0.8791\n","Epoch 15/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.3039 - accuracy: 0.8941 - val_loss: 0.3420 - val_accuracy: 0.8816\n","Epoch 16/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.2972 - accuracy: 0.8975 - val_loss: 0.3488 - val_accuracy: 0.8813\n","Epoch 17/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.2899 - accuracy: 0.8992 - val_loss: 0.3481 - val_accuracy: 0.8803\n","Epoch 18/30\n","594/594 [==============================] - 7s 11ms/step - loss: 0.2828 - accuracy: 0.9010 - val_loss: 0.3451 - val_accuracy: 0.8839\n","Epoch 19/30\n","594/594 [==============================] - 6s 11ms/step - loss: 0.2743 - accuracy: 0.9031 - val_loss: 0.3482 - val_accuracy: 0.8819\n","Epoch 20/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.2669 - accuracy: 0.9060 - val_loss: 0.3463 - val_accuracy: 0.8826\n","Epoch 21/30\n","594/594 [==============================] - 6s 11ms/step - loss: 0.2580 - accuracy: 0.9085 - val_loss: 0.3468 - val_accuracy: 0.8829\n","Epoch 22/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.2522 - accuracy: 0.9107 - val_loss: 0.3546 - val_accuracy: 0.8826\n","Epoch 23/30\n","594/594 [==============================] - 7s 11ms/step - loss: 0.2446 - accuracy: 0.9131 - val_loss: 0.3601 - val_accuracy: 0.8839\n","Epoch 24/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.2375 - accuracy: 0.9148 - val_loss: 0.3545 - val_accuracy: 0.8849\n","Epoch 25/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.2307 - accuracy: 0.9166 - val_loss: 0.3692 - val_accuracy: 0.8837\n","Epoch 26/30\n","594/594 [==============================] - 6s 10ms/step - loss: 0.2218 - accuracy: 0.9200 - val_loss: 0.3768 - val_accuracy: 0.8847\n","Epoch 27/30\n","594/594 [==============================] - 6s 9ms/step - loss: 0.2201 - accuracy: 0.9207 - val_loss: 0.3743 - val_accuracy: 0.8812\n","Epoch 28/30\n","594/594 [==============================] - 7s 11ms/step - loss: 0.2106 - accuracy: 0.9235 - val_loss: 0.3814 - val_accuracy: 0.8819\n","Epoch 29/30\n","594/594 [==============================] - 5s 9ms/step - loss: 0.2063 - accuracy: 0.9250 - val_loss: 0.3861 - val_accuracy: 0.8837\n","Epoch 30/30\n","594/594 [==============================] - 6s 11ms/step - loss: 0.1998 - accuracy: 0.9276 - val_loss: 0.3860 - val_accuracy: 0.8854\n","Training time: 192.62 seconds\n"]}]},{"cell_type":"code","source":["model.save(path + 'bilstm_phobertbase.h5')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wuNbWI-Iwry9","outputId":"f1a1e509-d31f-4b4f-d820-a24224e76e24"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n"]}]},{"cell_type":"markdown","source":["## Evaluate"],"metadata":{"id":"KJRho_FBw0II"}},{"cell_type":"code","source":["# Define class names\n","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","# Define evaluation function\n","def evaluate_model(model, X_test, y_test, class_names):\n","    y_pred = model.predict(X_test)\n","    y_pred_classes = np.argmax(y_pred, axis=1)\n","    y_true = np.argmax(y_test, axis=1)\n","\n","    accuracy = accuracy_score(y_true, y_pred_classes)\n","    conf_matrix = confusion_matrix(y_true, y_pred_classes)\n","    class_report = classification_report(y_true, y_pred_classes, target_names=class_names)\n","\n","    # Convert classification report to DataFrame\n","    report_dict = classification_report(y_true, y_pred_classes, target_names=class_names, output_dict=True)\n","    report_df = pd.DataFrame(report_dict).transpose()\n","\n","    return conf_matrix, report_df"],"metadata":{"id":"pgwbKsEzwwb8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Evaluate the model on test data\n","conf_matrix, report_df = evaluate_model(model, X_test, y_test, class_names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T6v60aS6w4Lb","outputId":"5209e5de-4693-4f25-e5a3-58fc9e3e3ba4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["760/760 [==============================] - 9s 8ms/step\n"]}]},{"cell_type":"code","source":["report_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"id":"aRd0N0CJw7Cr","outputId":"72b7ec4a-c6e8-40e4-d09f-49be3db2ab1c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              precision    recall  f1-score       support\n","Cong nghe      0.916376  0.924971  0.920653   1706.000000\n","Doi song       0.809365  0.868941  0.838095   1671.000000\n","Giai tri       0.905063  0.913514  0.909269   2035.000000\n","Giao duc       0.915953  0.916935  0.916443   1866.000000\n","Khoa hoc       0.894380  0.864232  0.879048   2136.000000\n","Kinh te        0.868528  0.842442  0.855286   2031.000000\n","Nha dat        0.860921  0.891828  0.876102   2117.000000\n","Phap luat      0.878977  0.843208  0.860721   1671.000000\n","The gioi       0.910120  0.902310  0.906198   1515.000000\n","The thao       0.964247  0.970556  0.967391   1834.000000\n","Van hoa        0.818491  0.796797  0.807499   1811.000000\n","Xa hoi         0.808928  0.802809  0.805857   1851.000000\n","Xe co          0.943614  0.956522  0.950024   2047.000000\n","accuracy       0.884607  0.884607  0.884607      0.884607\n","macro avg      0.884228  0.884236  0.884045  24291.000000\n","weighted avg   0.884728  0.884607  0.884486  24291.000000"],"text/html":["\n","  <div id=\"df-2559f341-03b8-4637-8c1a-404c125da91d\" 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>0.916376</td>\n","      <td>0.924971</td>\n","      <td>0.920653</td>\n","      <td>1706.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Doi song</th>\n","      <td>0.809365</td>\n","      <td>0.868941</td>\n","      <td>0.838095</td>\n","      <td>1671.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Giai tri</th>\n","      <td>0.905063</td>\n","      <td>0.913514</td>\n","      <td>0.909269</td>\n","      <td>2035.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Giao duc</th>\n","      <td>0.915953</td>\n","      <td>0.916935</td>\n","      <td>0.916443</td>\n","      <td>1866.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Khoa hoc</th>\n","      <td>0.894380</td>\n","      <td>0.864232</td>\n","      <td>0.879048</td>\n","      <td>2136.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Kinh te</th>\n","      <td>0.868528</td>\n","      <td>0.842442</td>\n","      <td>0.855286</td>\n","      <td>2031.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Nha dat</th>\n","      <td>0.860921</td>\n","      <td>0.891828</td>\n","      <td>0.876102</td>\n","      <td>2117.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Phap luat</th>\n","      <td>0.878977</td>\n","      <td>0.843208</td>\n","      <td>0.860721</td>\n","      <td>1671.000000</td>\n","    </tr>\n","    <tr>\n","      <th>The gioi</th>\n","      <td>0.910120</td>\n","      <td>0.902310</td>\n","      <td>0.906198</td>\n","      <td>1515.000000</td>\n","    </tr>\n","    <tr>\n","      <th>The thao</th>\n","      <td>0.964247</td>\n","      <td>0.970556</td>\n","      <td>0.967391</td>\n","      <td>1834.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Van hoa</th>\n","      <td>0.818491</td>\n","      <td>0.796797</td>\n","      <td>0.807499</td>\n","      <td>1811.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Xa hoi</th>\n","      <td>0.808928</td>\n","      <td>0.802809</td>\n","      <td>0.805857</td>\n","      <td>1851.000000</td>\n","    </tr>\n","    <tr>\n","      <th>Xe co</th>\n","      <td>0.943614</td>\n","      <td>0.956522</td>\n","      <td>0.950024</td>\n","      <td>2047.000000</td>\n","    </tr>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.884607</td>\n","      <td>0.884607</td>\n","      <td>0.884607</td>\n","      <td>0.884607</td>\n","    </tr>\n","    <tr>\n","      <th>macro avg</th>\n","      <td>0.884228</td>\n","      <td>0.884236</td>\n","      <td>0.884045</td>\n","      <td>24291.000000</td>\n","    </tr>\n","    <tr>\n","      <th>weighted avg</th>\n","      <td>0.884728</td>\n","      <td>0.884607</td>\n","      <td>0.884486</td>\n","      <td>24291.000000</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-2559f341-03b8-4637-8c1a-404c125da91d')\"\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 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\"num_unique_values\": 14,\n        \"samples\": [\n          1811.0,\n          2047.0,\n          1706.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["plt.figure(figsize=(10, 8))\n","sns.heatmap(conf_matrix, annot=True, fmt='d', xticklabels=class_names, yticklabels=class_names)\n","\n","# Đặt tiêu đề và nhãn cho trục\n","plt.title('Confusion Matrix for BiLSTM_PhoBert')\n","plt.xlabel('Predicted Labels')\n","plt.ylabel('True Labels')\n","\n","plt.savefig(path + 'confusion_matrix_bilstm_phobertbase.png')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":612},"id":"4HoiQQMrw9-H","outputId":"8e46b636-420e-4897-bb44-bb51cc9cfa27"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["report_df.to_csv(path + 'classification_report_bilstm_phobertbase.csv', index=True)"],"metadata":{"id":"36f_jnqlxGtk"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Predict"],"metadata":{"id":"xWHClMYoxVyb"}},{"cell_type":"code","source":["nlp_model = phonlp.load(save_dir=path + \"phonlp\")\n","\n","# Function to preprocess text\n","def preprocess_text(text):\n","    text = re.sub(r'[^\\w\\s.]', '', text)\n","    sentences = underthesea.sent_tokenize(text)\n","    preprocessed_words = []\n","    for sentence in sentences:\n","        try:\n","            word_tokens = underthesea.word_tokenize(sentence, format=\"text\")\n","            tags = nlp_model.annotate(word_tokens, batch_size=64)\n","            filtered_words = [word.lower() for word, tag in zip(tags[0][0], tags[1][0]) if tag[0] not in ['M', 'X', 'CH']\n","                              and word not in [\"'\", \",\"]]\n","            preprocessed_words.extend(filtered_words)\n","        except Exception as e:\n","            pass\n","    return ' '.join(preprocessed_words)\n","\n","# Function to create BERT features\n","def make_bert_features(v_text, max_len):\n","    v_tokenized = []\n","    for i_text in v_text:\n","        line = tokenizer.encode(i_text, truncation=True)\n","        v_tokenized.append(line)\n","    padded = []\n","    for i in v_tokenized:\n","        if len(i) < max_len:\n","            padded.append(i + [1] * (max_len - len(i)))\n","        else:\n","            padded.append(i[:max_len])\n","    padded = np.array(padded)\n","    attention_mask = np.where(padded == 1, 0, 1)\n","    padded = torch.tensor(padded).to(torch.long)\n","    attention_mask = torch.tensor(attention_mask)\n","    with torch.no_grad():\n","        last_hidden_states = phobert(input_ids=padded, attention_mask=attention_mask)\n","    v_features = last_hidden_states[0][:, 0, :].numpy()\n","    return v_features\n","\n","def predict_label(text, tokenizer, phobert, model, class_names, max_len):\n","    text = preprocess_text(text)\n","    # Encode text using BERT tokenizer and create BERT features\n","    encoded_text = make_bert_features([text], max_len)\n","    encoded_text = np.expand_dims(encoded_text, axis=1)  # Add a new dimension\n","\n","    # Predict probabilities\n","    prediction = model.predict(encoded_text)\n","\n","    # Get predicted label\n","    predicted_label_index = np.argmax(prediction, axis=1)[0]\n","    predicted_label = class_names[predicted_label_index]\n","\n","    # Create confidence DataFrame\n","    confidences = {class_names[i]: float(prediction[0][i]) for i in range(len(prediction[0]))}\n","    confidence_df = pd.DataFrame(confidences, index=[0])\n","\n","    return predicted_label, confidence_df"],"metadata":{"id":"pHuVn8qBxNbU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["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","\n","predicted_label, confidences = predict_label(text, tokenizer, phobert, model, class_names, max_len)\n","print(f\"The predicted label for the new text is: {predicted_label}\")\n","print(\"Confidence scores for each label:\")\n","confidences"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":413},"id":"FGmVwMeax29y","outputId":"fbac57ec-86eb-46d7-e964-fb2403530cfb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 1/1 [00:01<00:00,  1.90s/it]\n","100%|██████████| 1/1 [00:00<00:00, 21.88it/s]\n","100%|██████████| 1/1 [00:00<00:00, 32.12it/s]\n","100%|██████████| 1/1 [00:00<00:00, 29.64it/s]\n","100%|██████████| 1/1 [00:00<00:00, 22.67it/s]\n","100%|██████████| 1/1 [00:00<00:00, 25.53it/s]\n","100%|██████████| 1/1 [00:00<00:00, 27.02it/s]\n","100%|██████████| 1/1 [00:00<00:00, 25.41it/s]\n","100%|██████████| 1/1 [00:00<00:00, 21.28it/s]\n","100%|██████████| 1/1 [00:00<00:00, 28.80it/s]\n","100%|██████████| 1/1 [00:00<00:00, 24.91it/s]\n","100%|██████████| 1/1 [00:00<00:00, 27.44it/s]\n","100%|██████████| 1/1 [00:00<00:00, 23.32it/s]\n","Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 63ms/step\n","The predicted label for the new text is: Khoa hoc\n","Confidence scores for each label:\n"]},{"output_type":"execute_result","data":{"text/plain":["   Cong nghe  Doi song  Giai tri  Giao duc  Khoa hoc   Kinh te   Nha dat  \\\n","0   0.000723  0.025548  0.000074   0.00598  0.956909  0.000189  0.001694   \n","\n","   Phap luat  The gioi  The thao   Van hoa    Xa hoi     Xe co  \n","0   0.000049   0.00043  0.000061  0.000091  0.001248  0.007005  "],"text/html":["\n","  <div id=\"df-884817c4-8881-4c91-95cd-fc0f2982595c\" 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>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>0.000723</td>\n","      <td>0.025548</td>\n","      <td>0.000074</td>\n","      <td>0.00598</td>\n","      <td>0.956909</td>\n","      <td>0.000189</td>\n","      <td>0.001694</td>\n","      <td>0.000049</td>\n","      <td>0.00043</td>\n","      <td>0.000061</td>\n","      <td>0.000091</td>\n","      <td>0.001248</td>\n","      <td>0.007005</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-884817c4-8881-4c91-95cd-fc0f2982595c')\"\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","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-884817c4-8881-4c91-95cd-fc0f2982595c button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\"num_unique_values\": 1,\n        \"samples\": [\n          0.0255478136241436\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Giai tri\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 7.399600144708529e-05,\n        \"max\": 7.399600144708529e-05,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.399600144708529e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Giao duc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.00598022760823369,\n        \"max\": 0.00598022760823369,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.00598022760823369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Khoa hoc\",\n      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