{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup Working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if os.path.basename(os.getcwd()) == \"notebooks\":\n",
    "    os.chdir(\"../\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:03:41.565238Z",
     "iopub.status.busy": "2025-04-05T16:03:41.564958Z",
     "iopub.status.idle": "2025-04-05T16:03:47.190450Z",
     "shell.execute_reply": "2025-04-05T16:03:47.189468Z",
     "shell.execute_reply.started": "2025-04-05T16:03:41.565218Z"
    },
    "id": "UozqNhQ2ZShu",
    "outputId": "aa839538-e501-412f-b82b-9c3194b1a25c",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.9/20.9 MB\u001b[0m \u001b[31m84.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m657.8/657.8 kB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m55.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install -q transformers datasets pandas underthesea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:03:47.191688Z",
     "iopub.status.busy": "2025-04-05T16:03:47.191469Z",
     "iopub.status.idle": "2025-04-05T16:03:48.394694Z",
     "shell.execute_reply": "2025-04-05T16:03:48.393593Z",
     "shell.execute_reply.started": "2025-04-05T16:03:47.191668Z"
    },
    "id": "sk0GBYgTZlsT",
    "outputId": "ec8ca678-6661-49f9-b7a3-42907d611bba",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'UIT-ViCoV19QA'...\n",
      "remote: Enumerating objects: 81, done.\u001b[K\n",
      "remote: Counting objects: 100% (81/81), done.\u001b[K\n",
      "remote: Compressing objects: 100% (62/62), done.\u001b[K\n",
      "remote: Total 81 (delta 26), reused 54 (delta 15), pack-reused 0 (from 0)\u001b[K\n",
      "Receiving objects: 100% (81/81), 1.75 MiB | 12.87 MiB/s, done.\n",
      "Resolving deltas: 100% (26/26), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/triet2397/UIT-ViCoV19QA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:03:48.396764Z",
     "iopub.status.busy": "2025-04-05T16:03:48.396522Z",
     "iopub.status.idle": "2025-04-05T16:03:48.426409Z",
     "shell.execute_reply": "2025-04-05T16:03:48.425594Z",
     "shell.execute_reply.started": "2025-04-05T16:03:48.396743Z"
    },
    "id": "oixfxyF2Zmmd",
    "outputId": "c5ec91f2-6f85-430c-d879-e0785537952e",
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dataset'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "os.makedirs(\"dataset\", exist_ok=True)\n",
    "shutil.copytree(\"UIT-ViCoV19QA/dataset\", \"dataset\", dirs_exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:03:48.428114Z",
     "iopub.status.busy": "2025-04-05T16:03:48.427837Z",
     "iopub.status.idle": "2025-04-05T16:04:21.264724Z",
     "shell.execute_reply": "2025-04-05T16:04:21.264077Z",
     "shell.execute_reply.started": "2025-04-05T16:03:48.428093Z"
    },
    "id": "hHpnEDGcZn5b",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "from sklearn.model_selection import train_test_split\n",
    "import re, string\n",
    "from gensim.models import KeyedVectors\n",
    "from collections import Counter\n",
    "from underthesea import word_tokenize\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import preprocessing, utils, activations\n",
    "# from keras.callbacks import ModelCheckpoint\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:04:21.266294Z",
     "iopub.status.busy": "2025-04-05T16:04:21.265617Z",
     "iopub.status.idle": "2025-04-05T16:04:21.271190Z",
     "shell.execute_reply": "2025-04-05T16:04:21.270296Z",
     "shell.execute_reply.started": "2025-04-05T16:04:21.266270Z"
    },
    "id": "bempA-zQZpBP",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "def melt_dataframe_and_drop_na(_df):\n",
    "  # Melt the DataFrame\n",
    "  melted_df = pd.melt(_df, id_vars=['id', 'Question'], value_vars=['Answer_1', 'Answer_2', 'Answer_3', 'Answer_4'],\n",
    "                      var_name='Answer_Number', value_name='Answer')\n",
    "  # Remove the 'Answer_' prefix from the Answer_Number column\n",
    "  melted_df['Answer_Number'] = melted_df['Answer_Number'].str.replace('Answer_', '')\n",
    "  # Reorder the columns to match the desired format\n",
    "  melted_df = melted_df[['id', 'Question', 'Answer']]\n",
    "  # Replace columns name\n",
    "  melted_df = melted_df.rename(columns={'Question': 'question', 'Answer': 'answer'})\n",
    "  melted_df = melted_df.drop(columns=['id'])\n",
    "  # Drop NaN values\n",
    "  melted_df = melted_df.dropna()\n",
    "  # Drop duplicate rows\n",
    "  melted_df = melted_df.drop_duplicates()\n",
    "\n",
    "  return melted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:04:21.272459Z",
     "iopub.status.busy": "2025-04-05T16:04:21.272169Z",
     "iopub.status.idle": "2025-04-05T16:04:21.483844Z",
     "shell.execute_reply": "2025-04-05T16:04:21.483120Z",
     "shell.execute_reply.started": "2025-04-05T16:04:21.272430Z"
    },
    "id": "4RSmYXAdZp97",
    "outputId": "c0a5a235-842d-4f67-80bb-c1cc4d1f4d1e",
    "trusted": true
   },
   "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>question</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tại sao bệnh này lại được gọi là bệnh vi-rút c...</td>\n",
       "      <td>Vào ngày 11 tháng 2 năm 2020, Tổ Chức Y Tế Thế...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vi-rút lây lan như thế nào?</td>\n",
       "      <td>COVID-19 được cho là lây lan chủ yếu thông qua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lây lan trong cộng đồng là gì?</td>\n",
       "      <td>Lây lan trong cộng đồng nghĩa là mọi người bị ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Muỗi hay ve có thể truyền vi-rút gây bệnh COVI...</td>\n",
       "      <td>Tại thời điểm này, CDC không có dữ liệu nào ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Làm thế nào tôi có thể tự bảo vệ bản thân khỏi...</td>\n",
       "      <td>Truy cập trang Cách Bảo Vệ Bản Thân Và Người K...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question  \\\n",
       "0  Tại sao bệnh này lại được gọi là bệnh vi-rút c...   \n",
       "1                        Vi-rút lây lan như thế nào?   \n",
       "2                     Lây lan trong cộng đồng là gì?   \n",
       "3  Muỗi hay ve có thể truyền vi-rút gây bệnh COVI...   \n",
       "4  Làm thế nào tôi có thể tự bảo vệ bản thân khỏi...   \n",
       "\n",
       "                                              answer  \n",
       "0  Vào ngày 11 tháng 2 năm 2020, Tổ Chức Y Tế Thế...  \n",
       "1  COVID-19 được cho là lây lan chủ yếu thông qua...  \n",
       "2  Lây lan trong cộng đồng nghĩa là mọi người bị ...  \n",
       "3  Tại thời điểm này, CDC không có dữ liệu nào ch...  \n",
       "4  Truy cập trang Cách Bảo Vệ Bản Thân Và Người K...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset/UIT-ViCoV19QA.csv', sep='|')\n",
    "df = melt_dataframe_and_drop_na(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:04:21.484847Z",
     "iopub.status.busy": "2025-04-05T16:04:21.484622Z",
     "iopub.status.idle": "2025-04-05T16:04:21.488431Z",
     "shell.execute_reply": "2025-04-05T16:04:21.487757Z",
     "shell.execute_reply.started": "2025-04-05T16:04:21.484828Z"
    },
    "id": "2wHg3bZyZsLj",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "data = df.values\n",
    "questions = data[:, 0]\n",
    "answers = data[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:04:21.490936Z",
     "iopub.status.busy": "2025-04-05T16:04:21.490702Z",
     "iopub.status.idle": "2025-04-05T16:04:23.042518Z",
     "shell.execute_reply": "2025-04-05T16:04:23.041712Z",
     "shell.execute_reply.started": "2025-04-05T16:04:21.490917Z"
    },
    "id": "ohD4t62wZuHV",
    "outputId": "af7ab050-792a-404e-a96b-89c5dd95b774",
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1150x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to count words in a list of strings\n",
    "def count_words(lst):\n",
    "    return [len(sent.split()) for sent in lst]\n",
    "\n",
    "# Count the number of words in questions and answers\n",
    "question_word_counts = count_words(questions)\n",
    "answer_word_counts = count_words(answers)\n",
    "\n",
    "# Create a DataFrame for better visualization\n",
    "count_df = pd.DataFrame({\n",
    "    'Question Word Count': question_word_counts,\n",
    "    'Answer Word Count': answer_word_counts\n",
    "})\n",
    "\n",
    "# Calculate mean and standard deviation\n",
    "question_mean = np.mean(question_word_counts)\n",
    "question_std = np.std(question_word_counts)\n",
    "answer_mean = np.mean(answer_word_counts)\n",
    "answer_std = np.std(answer_word_counts)\n",
    "\n",
    "# Plot the distribution of word counts\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.5))\n",
    "\n",
    "# Plot the distribution of question word counts\n",
    "count_df['Question Word Count'].plot(kind='hist', ax=axes[0], bins=200, alpha=0.7, color='blue')\n",
    "axes[0].set_title('Distribution of Question Word Counts')\n",
    "axes[0].set_xlabel('Number of Words')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "# Set x-axis ticks to every 50th value\n",
    "axes[0].xaxis.set_major_locator(mticker.MultipleLocator(50))\n",
    "\n",
    "# Rotate x-axis labels by 75 degrees\n",
    "for label in axes[0].get_xticklabels():\n",
    "    label.set_rotation(75)\n",
    "\n",
    "# Add mean and sigma lines for questions\n",
    "axes[0].axvline(question_mean, color='red', linestyle='dashed', linewidth=2, label='Mean')\n",
    "axes[0].axvline(question_mean + question_std, color='orange', linestyle='dashed', linewidth=2, label='+1 Sigma')\n",
    "axes[0].axvline(question_mean + 2 * question_std, color='gold', linestyle='dashed', linewidth=2, label='+2 Sigma')\n",
    "axes[0].axvline(question_mean + 3 * question_std, color='yellow', linestyle='dashed', linewidth=2, label='+3 Sigma')\n",
    "axes[0].axvline(question_mean + 4 * question_std, color='green', linestyle='dashed', linewidth=2, label='+4 Sigma')\n",
    "axes[0].axvline(question_mean + 5 * question_std, color='blue', linestyle='dashed', linewidth=2, label='+5 Sigma')\n",
    "axes[0].axvline(question_mean + 6 * question_std, color='purple', linestyle='dashed', linewidth=2, label='+6 Sigma')\n",
    "axes[0].legend()\n",
    "\n",
    "# Plot the distribution of answer word counts\n",
    "count_df['Answer Word Count'].plot(kind='hist', ax=axes[1], bins=200, alpha=0.7, color='green')\n",
    "axes[1].set_title('Distribution of Answer Word Counts')\n",
    "axes[1].set_xlabel('Number of Words')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "# Set x-axis ticks to every 50th value\n",
    "axes[1].xaxis.set_major_locator(mticker.MultipleLocator(50))\n",
    "\n",
    "# Rotate x-axis labels by 75 degrees\n",
    "for label in axes[1].get_xticklabels():\n",
    "    label.set_rotation(75)\n",
    "\n",
    "# Add mean and sigma lines for answers\n",
    "axes[1].axvline(answer_mean, color='red', linestyle='dashed', linewidth=2, label='Mean')\n",
    "axes[1].axvline(answer_mean + answer_std, color='orange', linestyle='dashed', linewidth=2, label='+1 Sigma')\n",
    "axes[1].axvline(answer_mean + 2 * answer_std, color='gold', linestyle='dashed', linewidth=2, label='+2 Sigma')\n",
    "axes[1].axvline(answer_mean + 3 * answer_std, color='yellow', linestyle='dashed', linewidth=2, label='+3 Sigma')\n",
    "axes[1].axvline(answer_mean + 4 * answer_std, color='green', linestyle='dashed', linewidth=2, label='+4 Sigma')\n",
    "axes[1].axvline(answer_mean + 5 * answer_std, color='blue', linestyle='dashed', linewidth=2, label='+5 Sigma')\n",
    "axes[1].axvline(answer_mean + 6 * answer_std, color='purple', linestyle='dashed', linewidth=2, label='+6 Sigma')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:04:23.044645Z",
     "iopub.status.busy": "2025-04-05T16:04:23.044418Z",
     "iopub.status.idle": "2025-04-05T16:05:16.210701Z",
     "shell.execute_reply": "2025-04-05T16:05:16.209889Z",
     "shell.execute_reply.started": "2025-04-05T16:04:23.044626Z"
    },
    "id": "vo5WYfjuZvk0",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# Tokenization questions and answers\n",
    "questions = [word_tokenize(ques, format='text') for ques in questions]\n",
    "answers = [word_tokenize(ans, format='text') for ans in answers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:05:16.211850Z",
     "iopub.status.busy": "2025-04-05T16:05:16.211601Z",
     "iopub.status.idle": "2025-04-05T16:05:16.221098Z",
     "shell.execute_reply": "2025-04-05T16:05:16.220295Z",
     "shell.execute_reply.started": "2025-04-05T16:05:16.211830Z"
    },
    "id": "izz94RzyafH1",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "my_df = pd.DataFrame()\n",
    "my_df['question'] = questions\n",
    "my_df['answer'] = answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:05:16.222191Z",
     "iopub.status.busy": "2025-04-05T16:05:16.221897Z",
     "iopub.status.idle": "2025-04-05T16:05:26.698706Z",
     "shell.execute_reply": "2025-04-05T16:05:26.697517Z",
     "shell.execute_reply.started": "2025-04-05T16:05:16.222163Z"
    },
    "id": "P2pHATnJZZeu",
    "outputId": "804756ed-383e-4a70-8969-a72d295ef76a",
    "trusted": true
   },
   "outputs": [
    {
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       "model_id": "a1ce8d22b60a4a51a840a851d5d1a843",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/82.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "6c6becc544fb4aafba543065047ce4c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/553 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
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      },
      "text/plain": [
       "spiece.model:   0%|          | 0.00/4.31M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7760b49efeb349e4bb45e838ff113839",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "608f6011768e4a64a374b71b44b4c4df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/1.20G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8971369dfeb54ce0b21f4344e2dc723b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
    "\n",
    "base_model = \"google/mt5-small\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(base_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:05:26.700718Z",
     "iopub.status.busy": "2025-04-05T16:05:26.700427Z",
     "iopub.status.idle": "2025-04-05T16:05:31.317203Z",
     "shell.execute_reply": "2025-04-05T16:05:31.316463Z",
     "shell.execute_reply.started": "2025-04-05T16:05:26.700684Z"
    },
    "id": "w3bXK6YVZcA1",
    "outputId": "d5f7c8e7-109a-4741-bb49-264e0ac10ca9",
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa6989bbc2694ad1aa12e330c2c3e7a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/7339 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import Dataset\n",
    "\n",
    "# Function to preprocess the data\n",
    "def preprocess_data(examples):\n",
    "    inputs = [f\"question: {q}\" for q in examples['question']]\n",
    "    targets = [f\"{a}\" for a in examples['answer']]  # Targets are just the answers\n",
    "\n",
    "    model_inputs = tokenizer(inputs, max_length=192, truncation=True, padding='max_length')\n",
    "    # Setup the tokenizer for targets\n",
    "    with tokenizer.as_target_tokenizer():\n",
    "        labels = tokenizer(targets, max_length=768, truncation=True, padding='max_length')\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]  # Labels are the decoder_input_ids\n",
    "    return model_inputs\n",
    "\n",
    "# Convert DataFrame to Dataset\n",
    "dataset = Dataset.from_pandas(my_df)\n",
    "\n",
    "# Preprocess the dataset\n",
    "tokenized_dataset = dataset.map(preprocess_data, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:05:31.319178Z",
     "iopub.status.busy": "2025-04-05T16:05:31.318768Z",
     "iopub.status.idle": "2025-04-05T16:05:31.340188Z",
     "shell.execute_reply": "2025-04-05T16:05:31.339182Z",
     "shell.execute_reply.started": "2025-04-05T16:05:31.319118Z"
    },
    "id": "55H6CrHHcaO_",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# Split the dataset into training and validation sets\n",
    "train_dataset, val_dataset = tokenized_dataset.train_test_split(test_size=0.1, seed=42).values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:05:31.341675Z",
     "iopub.status.busy": "2025-04-05T16:05:31.341344Z",
     "iopub.status.idle": "2025-04-05T16:05:31.359936Z",
     "shell.execute_reply": "2025-04-05T16:05:31.358412Z",
     "shell.execute_reply.started": "2025-04-05T16:05:31.341631Z"
    },
    "id": "Dh0q68yrbRYT",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T16:05:31.361849Z",
     "iopub.status.busy": "2025-04-05T16:05:31.361451Z",
     "iopub.status.idle": "2025-04-05T16:05:31.626121Z",
     "shell.execute_reply": "2025-04-05T16:05:31.624950Z",
     "shell.execute_reply.started": "2025-04-05T16:05:31.361785Z"
    },
    "id": "7GdUQ18Ud_hc",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "from transformers import TrainerCallback\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "class EarlyStoppingCallback(TrainerCallback):\n",
    "    def __init__(self, patience=5):\n",
    "        self.patience = patience\n",
    "        self.best_metric = None\n",
    "        self.counter = 0\n",
    "\n",
    "    def on_evaluate(self, args, state, control, metrics, **kwargs):\n",
    "        if self.best_metric is None or metrics['eval_loss'] < self.best_metric:\n",
    "            self.best_metric = metrics['eval_loss']\n",
    "            self.counter = 0\n",
    "        else:\n",
    "            self.counter += 1\n",
    "            if self.counter >= self.patience:\n",
    "                control.should_training_stop = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# Save model  and tokenizer to \"vi-medical-transformer-fintune\"\n",
    "os.makedirs(\"models\", exist_ok=True)\n",
    "out_model_name = \"vi-medical-mt5-finetune-qa\"\n",
    "out_model_path = f\"models/{out_model_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T17:29:12.087285Z",
     "iopub.status.busy": "2025-04-05T17:29:12.086983Z",
     "iopub.status.idle": "2025-04-05T17:29:12.128730Z",
     "shell.execute_reply": "2025-04-05T17:29:12.127895Z",
     "shell.execute_reply.started": "2025-04-05T17:29:12.087263Z"
    },
    "id": "MvrLuxXssmCZ",
    "outputId": "61cb24fa-b232-4afd-b752-2386a3717628",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=out_model_path,\n",
    "    evaluation_strategy='epoch',\n",
    "    learning_rate=1e-4,\n",
    "    per_device_train_batch_size=2,\n",
    "    per_device_eval_batch_size=2,\n",
    "    num_train_epochs=26,\n",
    "    weight_decay=0.01,\n",
    "    save_total_limit=1,\n",
    "    load_best_model_at_end=True,  # Load the best model at the end of training\n",
    "    metric_for_best_model='eval_loss',  # Use evaluation loss as the metric\n",
    "    greater_is_better=False,  # Lower evaluation loss is better\n",
    "    save_strategy='epoch'  # Ensure save strategy matches evaluation strategy\n",
    ")\n",
    "\n",
    "early_stopping_callback = EarlyStoppingCallback(patience=10)\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=val_dataset,\n",
    "    callbacks=[early_stopping_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-05T17:29:14.852024Z",
     "iopub.status.busy": "2025-04-05T17:29:14.851599Z",
     "iopub.status.idle": "2025-04-06T01:44:27.669176Z",
     "shell.execute_reply": "2025-04-06T01:44:27.668455Z",
     "shell.execute_reply.started": "2025-04-05T17:29:14.851986Z"
    },
    "id": "7hm4r5GUZeaT",
    "outputId": "6d638243-f051-4065-d04b-88bf55f95dd0",
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='85878' max='85878' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [85878/85878 8:15:11, Epoch 26/26]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.395538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.406000</td>\n",
       "      <td>0.382062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.398900</td>\n",
       "      <td>0.377744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.367524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.405700</td>\n",
       "      <td>0.360998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.389800</td>\n",
       "      <td>0.355099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.393000</td>\n",
       "      <td>0.349508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.401700</td>\n",
       "      <td>0.345235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.360900</td>\n",
       "      <td>0.343793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.343600</td>\n",
       "      <td>0.338723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.363700</td>\n",
       "      <td>0.336523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.346400</td>\n",
       "      <td>0.333973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.327300</td>\n",
       "      <td>0.332415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.340600</td>\n",
       "      <td>0.331806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.344900</td>\n",
       "      <td>0.330227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.335900</td>\n",
       "      <td>0.328180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.332100</td>\n",
       "      <td>0.326864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.314700</td>\n",
       "      <td>0.325996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.325700</td>\n",
       "      <td>0.326483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.324400</td>\n",
       "      <td>0.325702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.302400</td>\n",
       "      <td>0.323864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.300700</td>\n",
       "      <td>0.323292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.307700</td>\n",
       "      <td>0.322439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.294700</td>\n",
       "      <td>0.322978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.299600</td>\n",
       "      <td>0.322619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.306100</td>\n",
       "      <td>0.322764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "There were missing keys in the checkpoint model loaded: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight'].\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=85878, training_loss=0.3532296384634921, metrics={'train_runtime': 29708.0106, 'train_samples_per_second': 5.781, 'train_steps_per_second': 2.891, 'total_flos': 3.40508191555584e+16, 'train_loss': 0.3532296384634921, 'epoch': 26.0})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-06T01:47:59.247039Z",
     "iopub.status.busy": "2025-04-06T01:47:59.246699Z",
     "iopub.status.idle": "2025-04-06T01:48:34.369746Z",
     "shell.execute_reply": "2025-04-06T01:48:34.368888Z",
     "shell.execute_reply.started": "2025-04-06T01:47:59.247015Z"
    },
    "id": "wK3bwK-tZgB0",
    "outputId": "c4840453-ec94-40b6-8171-d18caabe75f8",
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='367' max='367' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [367/367 00:35]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation results: {'eval_loss': 0.3224388360977173, 'eval_runtime': 35.113, 'eval_samples_per_second': 20.904, 'eval_steps_per_second': 10.452, 'epoch': 26.0}\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "eval_results = trainer.evaluate()\n",
    "print(f\"Evaluation results: {eval_results}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-06T01:48:43.265543Z",
     "iopub.status.busy": "2025-04-06T01:48:43.265261Z",
     "iopub.status.idle": "2025-04-06T01:48:47.061790Z",
     "shell.execute_reply": "2025-04-06T01:48:47.060801Z",
     "shell.execute_reply.started": "2025-04-06T01:48:43.265522Z"
    },
    "id": "7BKAn1TykqfE",
    "outputId": "83759824-0d2f-42ba-8b9d-d7867f414147",
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('models/vi-medical-mt5-finetune/tokenizer_config.json',\n",
       " 'models/vi-medical-mt5-finetune/special_tokens_map.json',\n",
       " 'models/vi-medical-mt5-finetune/spiece.model',\n",
       " 'models/vi-medical-mt5-finetune/added_tokens.json',\n",
       " 'models/vi-medical-mt5-finetune/tokenizer.json')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save_pretrained(out_model_path)\n",
    "tokenizer.save_pretrained(out_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-06T01:48:47.063500Z",
     "iopub.status.busy": "2025-04-06T01:48:47.063177Z",
     "iopub.status.idle": "2025-04-06T01:48:52.764627Z",
     "shell.execute_reply": "2025-04-06T01:48:52.763708Z",
     "shell.execute_reply.started": "2025-04-06T01:48:47.063463Z"
    },
    "id": "-t0b9wHTjdPt",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import torch\n",
    "# from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
    "# T5Tokenizer, T5ForConditionalGeneration\n",
    "def load_model_and_tokenizer(output_dir):\n",
    "    # Load the trained tokenizer\n",
    "    tokenizer = AutoTokenizer.from_pretrained(output_dir)\n",
    "\n",
    "    # Load the trained model\n",
    "    model = AutoModelForSeq2SeqLM.from_pretrained(output_dir)\n",
    "\n",
    "    # Move the model to the GPU if available\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "\n",
    "    return tokenizer, model, device\n",
    "\n",
    "def generate_text(tokenizer, model, device, prompt, max_length=100,\n",
    "                  num_return_sequences=1, top_p=0.95, temperature=0.7):\n",
    "    # Tokenize the input prompt\n",
    "    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)\n",
    "\n",
    "    # Generate text\n",
    "    output = model.generate(\n",
    "        input_ids,\n",
    "        max_length=max_length,\n",
    "        num_return_sequences=num_return_sequences,\n",
    "        no_repeat_ngram_size=2,\n",
    "        top_k=50,\n",
    "        top_p=top_p,\n",
    "        temperature=temperature,\n",
    "        do_sample=True\n",
    "    )\n",
    "\n",
    "    # Convert the generated text back to a string\n",
    "    generated_text = [tokenizer.decode(ids, skip_special_tokens=True) for ids in output]\n",
    "\n",
    "    return generated_text\n",
    "\n",
    "# Load the trained model and tokenizer\n",
    "output_dir = \"models/vi-medical-mt5-finetune-qa\"\n",
    "tokenizer, model, device = load_model_and_tokenizer(output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
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    "trusted": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated Text:\n",
      "\n",
      "Chưa có chắc chắn nào cho thấy Covid-19 được phát hiện từ người bị muỗi đốt. Tuy nhiên, nếu bạn bệnh muổi ốm và muốn đổt thì sẽ không xảy ra các triệu chứng COVID-19 gây covid-19\n"
     ]
    }
   ],
   "source": [
    "# Define the prompt for text generation\n",
    "prompt = \"bị muỗi đốt có mắc covid-19 không?\"\n",
    "# Generate text\n",
    "generated_text = generate_text(tokenizer, model, device, prompt,\n",
    "                               max_length=768,\n",
    "                               num_return_sequences=1, top_p=0.95, temperature=0.7)\n",
    "\n",
    "# Print the generated text\n",
    "print(\"Generated Text:\\n\")\n",
    "result = generated_text[0].replace(\"_\", \" \").replace(\" ,\", \",\").replace(\" .\", \".\").replace(\" -\", \"\\n-\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated Text:\n",
      "\n",
      "Để chắc chắn, hiện nay chưa có vấn đề nào cho thấy covid-19 thể gây bệnh nặng. Tuy nhiên trong các nghiên cứu hơn từ một số trường hợp xảy ra là sự lây lan của COVID-19 ở mức ộ là chỉ được đánh giá lần đầu tiên như sau : Đau nhức và ớn lạnh ; đau bụng dữ dội\n",
      "- rối loạn ý nghĩa / tụt huyết áp ≥ 60 % Người mắc bất kỳ tác dụng khác nhau vì cái mới gần đây đã ổn lên ) Trong ít trọng mọi lúc bên ngoài nên vẫn có thai ? Các loại coronavirus đều có dạng cảm cúm Những người Trung Quốc năm 2003   2019 ngày tháng 11/2020 nói chung chẳng may nhiễm COVID--19 khác ( WHO ] tên vaccine Sputnik V công bố cũng không có cơ quan yếu mắc âm gồm cách uống chín chung chịu cung ứng thông tin ăn món ức đề xuất hoang mang i khuẩn hoặc áo rượu mũi hoạt hại cấp phát nguồn virus kém Xem giám sát tòa cộng Đồng Nó ảnh ưởng đến hiệu quả chính mình áp tâm để tìm nguyên tác phòng vệ cụ theo \n"
     ]
    }
   ],
   "source": [
    "# Define the prompt for text generation\n",
    "prompt = \"covid-19 là gì?\"\n",
    "# Generate text\n",
    "generated_text = generate_text(tokenizer, model, device, prompt,\n",
    "                               max_length=768,\n",
    "                               num_return_sequences=1, top_p=0.95, temperature=0.7)\n",
    "\n",
    "# Print the generated text\n",
    "print(\"Generated Text:\\n\")\n",
    "result = generated_text[0].replace(\"_\", \" \").replace(\" ,\", \",\").replace(\" .\", \".\").replace(\" -\", \"\\n-\")\n",
    "print(result)"
   ]
  },
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   "execution_count": 34,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated Text:\n",
      "\n",
      "Người có nguy cơ mắc COVID-19 cao nhất có thể mới có triệu cuộc nhiễm COVID 19 từ các trung tâm y tế. Những người có được một số trẻ em có mức độ ít gặp phải những trên địa bàn, thành phố đã có bệnh lý rất hiếm như sốc phản vệ và lớn trong mọi người vì liệu về hệ miễn dịch ở bất kỳ tác nhân nào ? Các trường hợp bị nhiêm trọng bao gồm cả Trung Quốc ;\n",
      "- Trong giai pháp xét nghiệm SARS-CoV-2 : Đối xử ứng dụng PPE / PCR nhanh chóng lên đến 89 % người trở nên ≥ 12 tuổi ≤ 65 tuần ) Tuy nuôi kém hơn nhau hãy thực hành thăm dò kết quả của cơ quan kinh doanh vaccine Covid-19 ngày nay công tác phòng nhiễu năm 2007 áp ứu 5 tháng sau khi ăn uống uắt khoa mẹ Hà Nội Tiến Định Giám sát Sở Y Tế Thế giới ( WHO ] Công dân Mỹ ( Bộ Y Tế Bản Thành Phố ” Ứng Viện Vệ Sinh Hồi Sức Ăn Hòa Huyết Thanh Lãnh TAND TP.HCM Ưu Chỉ Mục Quân Anh Âu Đồng Giới CDC Ông Tạo Tổ Chức Yên Bục Advisory Group = AKI ARV ' Nicholas Louis !\n"
     ]
    }
   ],
   "source": [
    "# Define the prompt for text generation\n",
    "prompt = \"ai có nguy cơ mắc covid-19 nhiều nhất?\"\n",
    "# Generate text\n",
    "generated_text = generate_text(tokenizer, model, device, prompt,\n",
    "                               max_length=768,\n",
    "                               num_return_sequences=1, top_p=0.95, temperature=0.7)\n",
    "\n",
    "# Print the generated text\n",
    "print(\"Generated Text:\\n\")\n",
    "result = generated_text[0].replace(\"_\", \" \").replace(\" ,\", \",\").replace(\" .\", \".\").replace(\" -\", \"\\n-\")\n",
    "print(result)"
   ]
  }
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