diff --git "a/distortion-classification-deeplearning.ipynb" "b/distortion-classification-deeplearning.ipynb"
new file mode 100644--- /dev/null
+++ "b/distortion-classification-deeplearning.ipynb"
@@ -0,0 +1,4283 @@
+{
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+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os, re, warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3fef7e91",
+   "metadata": {
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+     "status": "completed"
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+    "tags": []
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+    {
+     "data": {
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Id_Number</th>\n",
+       "      <th>Patient Question</th>\n",
+       "      <th>Distorted part</th>\n",
+       "      <th>Dominant Distortion</th>\n",
+       "      <th>Secondary Distortion (Optional)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4500</td>\n",
+       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
+       "      <td>The voice are always fimilar (someone she know...</td>\n",
+       "      <td>Personalization</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4501</td>\n",
+       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
+       "      <td>I feel trapped inside my disgusting self and l...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4502</td>\n",
+       "      <td>So I’ve been dating on and off this guy for a...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No Distortion</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4503</td>\n",
+       "      <td>My parents got divorced in 2004. My mother has...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No Distortion</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4504</td>\n",
+       "      <td>I don’t really know how to explain the situati...</td>\n",
+       "      <td>I refused to go because I didn’t know if it wa...</td>\n",
+       "      <td>Fortune-telling</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id_Number                                   Patient Question  \\\n",
+       "0       4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
+       "1       4501  Since I was about 16 years old I’ve had these ...   \n",
+       "2       4502   So I’ve been dating on and off this guy for a...   \n",
+       "3       4503  My parents got divorced in 2004. My mother has...   \n",
+       "4       4504  I don’t really know how to explain the situati...   \n",
+       "\n",
+       "                                      Distorted part Dominant Distortion  \\\n",
+       "0  The voice are always fimilar (someone she know...     Personalization   \n",
+       "1  I feel trapped inside my disgusting self and l...            Labeling   \n",
+       "2                                                NaN       No Distortion   \n",
+       "3                                                NaN       No Distortion   \n",
+       "4  I refused to go because I didn’t know if it wa...     Fortune-telling   \n",
+       "\n",
+       "  Secondary Distortion (Optional)  \n",
+       "0                             NaN  \n",
+       "1             Emotional Reasoning  \n",
+       "2                             NaN  \n",
+       "3                             NaN  \n",
+       "4             Emotional Reasoning  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('/kaggle/input/dis-full-speech/distortion.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "feae1753",
+   "metadata": {
+    "execution": {
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+    "tags": []
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+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "      <th></th>\n",
+       "      <th>Id_Number</th>\n",
+       "      <th>Patient Question</th>\n",
+       "      <th>Distorted part</th>\n",
+       "      <th>Dominant Distortion</th>\n",
+       "      <th>Secondary Distortion (Optional)</th>\n",
+       "    </tr>\n",
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+       "      <td>4500</td>\n",
+       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
+       "      <td>The voice are always fimilar (someone she know...</td>\n",
+       "      <td>Personalization</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4501</td>\n",
+       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
+       "      <td>I feel trapped inside my disgusting self and l...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4504</td>\n",
+       "      <td>I don’t really know how to explain the situati...</td>\n",
+       "      <td>I refused to go because I didn’t know if it wa...</td>\n",
+       "      <td>Fortune-telling</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4510</td>\n",
+       "      <td>I have been with my fiancé for two years now....</td>\n",
+       "      <td>I felt like the response was totally irrationa...</td>\n",
+       "      <td>Magnification</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4511</td>\n",
+       "      <td>My husband and I have been married for over a ...</td>\n",
+       "      <td>I thought that he displayed traits of honor, l...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>2519</th>\n",
+       "      <td>2553</td>\n",
+       "      <td>I feel overprotective about my mother because ...</td>\n",
+       "      <td>Since then, whenever my mother is out alone, I...</td>\n",
+       "      <td>All-or-nothing thinking</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2522</th>\n",
+       "      <td>2557</td>\n",
+       "      <td>From Lebanon: I am dealing with a big problem!...</td>\n",
+       "      <td>My family hate him but they didn’t met him at ...</td>\n",
+       "      <td>Magnification</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2523</th>\n",
+       "      <td>2559</td>\n",
+       "      <td>From the U.S.: I am a junior in high school, a...</td>\n",
+       "      <td>However, I am not happy, at the least only hal...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>All-or-nothing thinking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2526</th>\n",
+       "      <td>2563</td>\n",
+       "      <td>I am 21 female and have not had any friends fo...</td>\n",
+       "      <td>Now I am at university my peers around me all ...</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2527</th>\n",
+       "      <td>2564</td>\n",
+       "      <td>From the U.S.: My brother is 19 years old and ...</td>\n",
+       "      <td>He claims he’s severely depressed and has outb...</td>\n",
+       "      <td>Mental filter</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1597 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Id_Number                                   Patient Question  \\\n",
+       "0          4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
+       "1          4501  Since I was about 16 years old I’ve had these ...   \n",
+       "4          4504  I don’t really know how to explain the situati...   \n",
+       "9          4510   I have been with my fiancé for two years now....   \n",
+       "10         4511  My husband and I have been married for over a ...   \n",
+       "...         ...                                                ...   \n",
+       "2519       2553  I feel overprotective about my mother because ...   \n",
+       "2522       2557  From Lebanon: I am dealing with a big problem!...   \n",
+       "2523       2559  From the U.S.: I am a junior in high school, a...   \n",
+       "2526       2563  I am 21 female and have not had any friends fo...   \n",
+       "2527       2564  From the U.S.: My brother is 19 years old and ...   \n",
+       "\n",
+       "                                         Distorted part  \\\n",
+       "0     The voice are always fimilar (someone she know...   \n",
+       "1     I feel trapped inside my disgusting self and l...   \n",
+       "4     I refused to go because I didn’t know if it wa...   \n",
+       "9     I felt like the response was totally irrationa...   \n",
+       "10    I thought that he displayed traits of honor, l...   \n",
+       "...                                                 ...   \n",
+       "2519  Since then, whenever my mother is out alone, I...   \n",
+       "2522  My family hate him but they didn’t met him at ...   \n",
+       "2523  However, I am not happy, at the least only hal...   \n",
+       "2526  Now I am at university my peers around me all ...   \n",
+       "2527  He claims he’s severely depressed and has outb...   \n",
+       "\n",
+       "          Dominant Distortion Secondary Distortion (Optional)  \n",
+       "0             Personalization                             NaN  \n",
+       "1                    Labeling             Emotional Reasoning  \n",
+       "4             Fortune-telling             Emotional Reasoning  \n",
+       "9               Magnification                             NaN  \n",
+       "10                   Labeling                             NaN  \n",
+       "...                       ...                             ...  \n",
+       "2519  All-or-nothing thinking              Overgeneralization  \n",
+       "2522            Magnification                             NaN  \n",
+       "2523                 Labeling         All-or-nothing thinking  \n",
+       "2526       Overgeneralization                             NaN  \n",
+       "2527            Mental filter                    Mind Reading  \n",
+       "\n",
+       "[1597 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[df['Dominant Distortion'] != 'No Distortion']\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e14bfea0",
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+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
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+       "      <td>4501</td>\n",
+       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
+       "      <td>I feel trapped inside my disgusting self and l...</td>\n",
+       "      <td>Labeling</td>\n",
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+       "      <td>I don’t really know how to explain the situati...</td>\n",
+       "      <td>I refused to go because I didn’t know if it wa...</td>\n",
+       "      <td>Fortune-telling</td>\n",
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+       "      <td>I have been with my fiancé for two years now....</td>\n",
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+       "      <td>My husband and I have been married for over a ...</td>\n",
+       "      <td>I thought that he displayed traits of honor, l...</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>2519</th>\n",
+       "      <td>2553</td>\n",
+       "      <td>I feel overprotective about my mother because ...</td>\n",
+       "      <td>Since then, whenever my mother is out alone, I...</td>\n",
+       "      <td>All-or-nothing thinking</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2522</th>\n",
+       "      <td>2557</td>\n",
+       "      <td>From Lebanon: I am dealing with a big problem!...</td>\n",
+       "      <td>My family hate him but they didn’t met him at ...</td>\n",
+       "      <td>Magnification</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2523</th>\n",
+       "      <td>2559</td>\n",
+       "      <td>From the U.S.: I am a junior in high school, a...</td>\n",
+       "      <td>However, I am not happy, at the least only hal...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>All-or-nothing thinking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2526</th>\n",
+       "      <td>2563</td>\n",
+       "      <td>I am 21 female and have not had any friends fo...</td>\n",
+       "      <td>Now I am at university my peers around me all ...</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2527</th>\n",
+       "      <td>2564</td>\n",
+       "      <td>From the U.S.: My brother is 19 years old and ...</td>\n",
+       "      <td>He claims he’s severely depressed and has outb...</td>\n",
+       "      <td>Mental filter</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1597 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Id_Number                                   Patient Question  \\\n",
+       "0          4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
+       "1          4501  Since I was about 16 years old I’ve had these ...   \n",
+       "4          4504  I don’t really know how to explain the situati...   \n",
+       "9          4510   I have been with my fiancé for two years now....   \n",
+       "10         4511  My husband and I have been married for over a ...   \n",
+       "...         ...                                                ...   \n",
+       "2519       2553  I feel overprotective about my mother because ...   \n",
+       "2522       2557  From Lebanon: I am dealing with a big problem!...   \n",
+       "2523       2559  From the U.S.: I am a junior in high school, a...   \n",
+       "2526       2563  I am 21 female and have not had any friends fo...   \n",
+       "2527       2564  From the U.S.: My brother is 19 years old and ...   \n",
+       "\n",
+       "                                         Distorted part  \\\n",
+       "0     The voice are always fimilar (someone she know...   \n",
+       "1     I feel trapped inside my disgusting self and l...   \n",
+       "4     I refused to go because I didn’t know if it wa...   \n",
+       "9     I felt like the response was totally irrationa...   \n",
+       "10    I thought that he displayed traits of honor, l...   \n",
+       "...                                                 ...   \n",
+       "2519  Since then, whenever my mother is out alone, I...   \n",
+       "2522  My family hate him but they didn’t met him at ...   \n",
+       "2523  However, I am not happy, at the least only hal...   \n",
+       "2526  Now I am at university my peers around me all ...   \n",
+       "2527  He claims he’s severely depressed and has outb...   \n",
+       "\n",
+       "          Dominant Distortion Secondary Distortion (Optional)  \n",
+       "0             Personalization                             NaN  \n",
+       "1                    Labeling             Emotional Reasoning  \n",
+       "4             Fortune-telling             Emotional Reasoning  \n",
+       "9               Magnification                             NaN  \n",
+       "10                   Labeling                             NaN  \n",
+       "...                       ...                             ...  \n",
+       "2519  All-or-nothing thinking              Overgeneralization  \n",
+       "2522            Magnification                             NaN  \n",
+       "2523                 Labeling         All-or-nothing thinking  \n",
+       "2526       Overgeneralization                             NaN  \n",
+       "2527            Mental filter                    Mind Reading  \n",
+       "\n",
+       "[1597 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "89f5af91",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:30:56.385670Z",
+     "iopub.status.busy": "2025-07-31T07:30:56.385416Z",
+     "iopub.status.idle": "2025-07-31T07:30:57.404255Z",
+     "shell.execute_reply": "2025-07-31T07:30:57.403257Z"
+    },
+    "papermill": {
+     "duration": 1.025376,
+     "end_time": "2025-07-31T07:30:57.405599",
+     "exception": false,
+     "start_time": "2025-07-31T07:30:56.380223",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "i luv programmng u r gr8 i cant believe its 123 times better\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to /usr/share/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# !pip install spellchecker\n",
+    "import re\n",
+    "import string\n",
+    "import nltk\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
+    "# from spellchecker import SpellChecker\n",
+    "\n",
+    "# Tải các tài nguyên cần thiết\n",
+    "nltk.download(\"punkt\")\n",
+    "nltk.download(\"stopwords\")\n",
+    "nltk.download(\"wordnet\")\n",
+    "\n",
+    "# Khởi tạo các công cụ\n",
+    "stemmer = PorterStemmer()\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "# spell = SpellChecker()\n",
+    "stop_words = set(stopwords.words(\"english\"))\n",
+    "\n",
+    "def preprocess_text(text, use_stemming=False, use_lemmatization=True, correct_spelling=False):\n",
+    "    text = text.lower()\n",
+    "    \n",
+    "    text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
+    "    \n",
+    "    # tokens = word_tokenize(text)\n",
+    "    \n",
+    "    # tokens = [word for word in tokens if word not in stop_words]\n",
+    "    \n",
+    "    # if use_stemming:\n",
+    "    #     tokens = [stemmer.stem(word) for word in tokens]\n",
+    "    # elif use_lemmatization:\n",
+    "    #     tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
+    "    \n",
+    "    # 7. Xóa khoảng trắng dư thừa (không cần vì token đã tách sẵn)\n",
+    "    \n",
+    "    # 8. Chuyển đổi số thành \"NUM\"\n",
+    "    # tokens = [\"NUM\" if word.isdigit() else word for word in tokens]\n",
+    "    \n",
+    "    # 9. Xử lý lỗi chính tả\n",
+    "    # if correct_spelling:\n",
+    "    #     tokens = [spell.correction(word) if word not in spell else word for word in tokens]\n",
+    "    \n",
+    "    return text#\" \".join(text)\n",
+    "\n",
+    "# Ví dụ sử dụng\n",
+    "text = \"I luv programmng! U r gr8. I can't believe it's 123 times better!\"\n",
+    "processed_text = preprocess_text(text)\n",
+    "print(processed_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "495f293b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:30:57.414915Z",
+     "iopub.status.busy": "2025-07-31T07:30:57.414710Z",
+     "iopub.status.idle": "2025-07-31T07:30:57.444182Z",
+     "shell.execute_reply": "2025-07-31T07:30:57.443553Z"
+    },
+    "papermill": {
+     "duration": 0.035306,
+     "end_time": "2025-07-31T07:30:57.445277",
+     "exception": false,
+     "start_time": "2025-07-31T07:30:57.409971",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df['cleaned'] = df['Distorted part'].map(lambda text: preprocess_text(text))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "469d9017",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:30:57.454280Z",
+     "iopub.status.busy": "2025-07-31T07:30:57.454049Z",
+     "iopub.status.idle": "2025-07-31T07:30:57.463547Z",
+     "shell.execute_reply": "2025-07-31T07:30:57.462800Z"
+    },
+    "papermill": {
+     "duration": 0.015149,
+     "end_time": "2025-07-31T07:30:57.464573",
+     "exception": false,
+     "start_time": "2025-07-31T07:30:57.449424",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>Id_Number</th>\n",
+       "      <th>Patient Question</th>\n",
+       "      <th>Distorted part</th>\n",
+       "      <th>Dominant Distortion</th>\n",
+       "      <th>Secondary Distortion (Optional)</th>\n",
+       "      <th>cleaned</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4500</td>\n",
+       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
+       "      <td>The voice are always fimilar (someone she know...</td>\n",
+       "      <td>Personalization</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>the voice are always fimilar someone she knows...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4501</td>\n",
+       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
+       "      <td>I feel trapped inside my disgusting self and l...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "      <td>i feel trapped inside my disgusting self and l...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4504</td>\n",
+       "      <td>I don’t really know how to explain the situati...</td>\n",
+       "      <td>I refused to go because I didn’t know if it wa...</td>\n",
+       "      <td>Fortune-telling</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "      <td>i refused to go because i didn’t know if it wa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4510</td>\n",
+       "      <td>I have been with my fiancé for two years now....</td>\n",
+       "      <td>I felt like the response was totally irrationa...</td>\n",
+       "      <td>Magnification</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>i felt like the response was totally irrationa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4511</td>\n",
+       "      <td>My husband and I have been married for over a ...</td>\n",
+       "      <td>I thought that he displayed traits of honor, l...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>i thought that he displayed traits of honor lo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2519</th>\n",
+       "      <td>2553</td>\n",
+       "      <td>I feel overprotective about my mother because ...</td>\n",
+       "      <td>Since then, whenever my mother is out alone, I...</td>\n",
+       "      <td>All-or-nothing thinking</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "      <td>since then whenever my mother is out alone i b...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2522</th>\n",
+       "      <td>2557</td>\n",
+       "      <td>From Lebanon: I am dealing with a big problem!...</td>\n",
+       "      <td>My family hate him but they didn’t met him at ...</td>\n",
+       "      <td>Magnification</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>my family hate him but they didn’t met him at ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2523</th>\n",
+       "      <td>2559</td>\n",
+       "      <td>From the U.S.: I am a junior in high school, a...</td>\n",
+       "      <td>However, I am not happy, at the least only hal...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>All-or-nothing thinking</td>\n",
+       "      <td>however i am not happy at the least only half ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2526</th>\n",
+       "      <td>2563</td>\n",
+       "      <td>I am 21 female and have not had any friends fo...</td>\n",
+       "      <td>Now I am at university my peers around me all ...</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>now i am at university my peers around me all ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2527</th>\n",
+       "      <td>2564</td>\n",
+       "      <td>From the U.S.: My brother is 19 years old and ...</td>\n",
+       "      <td>He claims he’s severely depressed and has outb...</td>\n",
+       "      <td>Mental filter</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "      <td>he claims he’s severely depressed and has outb...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1597 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Id_Number                                   Patient Question  \\\n",
+       "0          4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
+       "1          4501  Since I was about 16 years old I’ve had these ...   \n",
+       "4          4504  I don’t really know how to explain the situati...   \n",
+       "9          4510   I have been with my fiancé for two years now....   \n",
+       "10         4511  My husband and I have been married for over a ...   \n",
+       "...         ...                                                ...   \n",
+       "2519       2553  I feel overprotective about my mother because ...   \n",
+       "2522       2557  From Lebanon: I am dealing with a big problem!...   \n",
+       "2523       2559  From the U.S.: I am a junior in high school, a...   \n",
+       "2526       2563  I am 21 female and have not had any friends fo...   \n",
+       "2527       2564  From the U.S.: My brother is 19 years old and ...   \n",
+       "\n",
+       "                                         Distorted part  \\\n",
+       "0     The voice are always fimilar (someone she know...   \n",
+       "1     I feel trapped inside my disgusting self and l...   \n",
+       "4     I refused to go because I didn’t know if it wa...   \n",
+       "9     I felt like the response was totally irrationa...   \n",
+       "10    I thought that he displayed traits of honor, l...   \n",
+       "...                                                 ...   \n",
+       "2519  Since then, whenever my mother is out alone, I...   \n",
+       "2522  My family hate him but they didn’t met him at ...   \n",
+       "2523  However, I am not happy, at the least only hal...   \n",
+       "2526  Now I am at university my peers around me all ...   \n",
+       "2527  He claims he’s severely depressed and has outb...   \n",
+       "\n",
+       "          Dominant Distortion Secondary Distortion (Optional)  \\\n",
+       "0             Personalization                             NaN   \n",
+       "1                    Labeling             Emotional Reasoning   \n",
+       "4             Fortune-telling             Emotional Reasoning   \n",
+       "9               Magnification                             NaN   \n",
+       "10                   Labeling                             NaN   \n",
+       "...                       ...                             ...   \n",
+       "2519  All-or-nothing thinking              Overgeneralization   \n",
+       "2522            Magnification                             NaN   \n",
+       "2523                 Labeling         All-or-nothing thinking   \n",
+       "2526       Overgeneralization                             NaN   \n",
+       "2527            Mental filter                    Mind Reading   \n",
+       "\n",
+       "                                                cleaned  \n",
+       "0     the voice are always fimilar someone she knows...  \n",
+       "1     i feel trapped inside my disgusting self and l...  \n",
+       "4     i refused to go because i didn’t know if it wa...  \n",
+       "9     i felt like the response was totally irrationa...  \n",
+       "10    i thought that he displayed traits of honor lo...  \n",
+       "...                                                 ...  \n",
+       "2519  since then whenever my mother is out alone i b...  \n",
+       "2522  my family hate him but they didn’t met him at ...  \n",
+       "2523  however i am not happy at the least only half ...  \n",
+       "2526  now i am at university my peers around me all ...  \n",
+       "2527  he claims he’s severely depressed and has outb...  \n",
+       "\n",
+       "[1597 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c00caea9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:30:57.473744Z",
+     "iopub.status.busy": "2025-07-31T07:30:57.473559Z",
+     "iopub.status.idle": "2025-07-31T07:30:57.478796Z",
+     "shell.execute_reply": "2025-07-31T07:30:57.478100Z"
+    },
+    "papermill": {
+     "duration": 0.010926,
+     "end_time": "2025-07-31T07:30:57.479770",
+     "exception": false,
+     "start_time": "2025-07-31T07:30:57.468844",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'All-or-nothing thinking': 0, 'Emotional Reasoning': 1, 'Fortune-telling': 2, 'Labeling': 3, 'Magnification': 4, 'Mental filter': 5, 'Mind Reading': 6, 'Overgeneralization': 7, 'Personalization': 8, 'Should statements': 9}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['labelencode'] = label_encoder.fit_transform(df['Dominant Distortion'])\n",
+    "\n",
+    "label_mapping = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)))\n",
+    "print(label_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "de6f3e92",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:30:57.488928Z",
+     "iopub.status.busy": "2025-07-31T07:30:57.488730Z",
+     "iopub.status.idle": "2025-07-31T07:30:57.493429Z",
+     "shell.execute_reply": "2025-07-31T07:30:57.492815Z"
+    },
+    "papermill": {
+     "duration": 0.010412,
+     "end_time": "2025-07-31T07:30:57.494441",
+     "exception": false,
+     "start_time": "2025-07-31T07:30:57.484029",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df['cleaned'].values,\n",
+    "                                                    df['labelencode'].values,\n",
+    "                                                    test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c6925af3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:30:57.503620Z",
+     "iopub.status.busy": "2025-07-31T07:30:57.503407Z",
+     "iopub.status.idle": "2025-07-31T07:31:22.008709Z",
+     "shell.execute_reply": "2025-07-31T07:31:22.008043Z"
+    },
+    "papermill": {
+     "duration": 24.5114,
+     "end_time": "2025-07-31T07:31:22.010032",
+     "exception": false,
+     "start_time": "2025-07-31T07:30:57.498632",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-31 07:31:11.326196: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1753947071.514409      19 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1753947071.569002      19 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torch.optim import AdamW \n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from transformers import RobertaTokenizer, RobertaForSequenceClassification\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from transformers import AutoTokenizer, AutoModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dcc4e584",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:22.020632Z",
+     "iopub.status.busy": "2025-07-31T07:31:22.020134Z",
+     "iopub.status.idle": "2025-07-31T07:31:22.023685Z",
+     "shell.execute_reply": "2025-07-31T07:31:22.022994Z"
+    },
+    "papermill": {
+     "duration": 0.009946,
+     "end_time": "2025-07-31T07:31:22.024751",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:22.014805",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from transformers import AutoTokenizer\n",
+    "\n",
+    "# tokenizer = AutoTokenizer.from_pretrained('roberta-large')\n",
+    "\n",
+    "\n",
+    "# df[\"body_len\"] = df[\"cleaned\"].apply(lambda x: len(tokenizer.encode(x, truncation=False)))\n",
+    "\n",
+    "# avg_summary_len = df[\"body_len\"].mean()\n",
+    "\n",
+    "# print(f\"Độ dài token trung bình của summary: {avg_summary_len:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "eac3c141",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:22.034353Z",
+     "iopub.status.busy": "2025-07-31T07:31:22.034101Z",
+     "iopub.status.idle": "2025-07-31T07:31:22.037644Z",
+     "shell.execute_reply": "2025-07-31T07:31:22.037025Z"
+    },
+    "papermill": {
+     "duration": 0.009443,
+     "end_time": "2025-07-31T07:31:22.038713",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:22.029270",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
+    "# def calculate_results(y_true, y_pred, y_pred_proba=None):\n",
+    "#     \"\"\"\n",
+    "#     Tính toán các chỉ số đánh giá, bao gồm accuracy, precision, recall, f1 và roc-auc.\n",
+    "#     Nếu `y_pred_proba` được cung cấp, tính thêm ROC-AUC cho từng nhãn trong bài toán đa nhãn.\n",
+    "#     \"\"\"\n",
+    "#     results = {\n",
+    "#         \"accuracy\": accuracy_score(y_true, y_pred) * 100,\n",
+    "#         \"precision\": precision_score(y_true, y_pred, average='weighted'),\n",
+    "#         \"recall\": recall_score(y_true, y_pred, average='weighted'),\n",
+    "#         \"f1\": f1_score(y_true, y_pred, average='weighted')\n",
+    "#     }\n",
+    "    \n",
+    "#     # if y_pred_proba is not None:\n",
+    "#     #     # Chuyển đổi nhãn thành dạng nhị phân\n",
+    "#     #     y_true_bin = label_binarize(y_true, classes=np.unique(y_true))\n",
+    "        \n",
+    "#     #     # Tính ROC-AUC cho từng nhãn (macro-average)\n",
+    "#     #     roc_auc_macro = []\n",
+    "#     #     for i in range(y_true_bin.shape[1]):\n",
+    "#     #         roc_auc = roc_auc_score(y_true_bin[:, i], y_pred_proba[:, i])\n",
+    "#     #         roc_auc_macro.append(roc_auc)\n",
+    "        \n",
+    "#     #     # Tính ROC-AUC macro trung bình\n",
+    "#     #     results[\"roc_auc_macro\"] = np.mean(roc_auc_macro)\n",
+    "        \n",
+    "#     #     # Tính ROC-AUC micro-average\n",
+    "#     #     roc_auc_micro = roc_auc_score(y_true_bin, y_pred_proba, average=\"micro\")\n",
+    "#     #     results[\"roc_auc_micro\"] = roc_auc_micro\n",
+    "    \n",
+    "#     return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a734a14b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:22.047692Z",
+     "iopub.status.busy": "2025-07-31T07:31:22.047504Z",
+     "iopub.status.idle": "2025-07-31T07:31:23.970642Z",
+     "shell.execute_reply": "2025-07-31T07:31:23.969612Z"
+    },
+    "papermill": {
+     "duration": 1.929215,
+     "end_time": "2025-07-31T07:31:23.972033",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:22.042818",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "596be61ac3f34a568ab29218295be87d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "245431e736f348308c699b5a82d14420",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1496dd813200429ba841824970977b40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "795397b977054c118a88104e1cc880a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4b44926c5ca4c1199a332a0f7da4acd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+    "from transformers import RobertaTokenizer, RobertaForSequenceClassification\n",
+    "\n",
+    "tokenizer = RobertaTokenizer.from_pretrained('roberta-large')\n",
+    "# tokenizer = AutoTokenizer.from_pretrained('bert-large-uncased')\n",
+    "\n",
+    "class TextDataset(Dataset):\n",
+    "    def __init__(self, texts, labels, tokenizer, max_len=256):\n",
+    "        self.texts = texts\n",
+    "        self.labels = labels\n",
+    "        self.tokenizer = tokenizer\n",
+    "        self.max_len = max_len\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.texts)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        encoding = self.tokenizer(\n",
+    "            self.texts[idx],\n",
+    "            truncation=True,\n",
+    "            padding='max_length',\n",
+    "            max_length=self.max_len,\n",
+    "            return_tensors='pt'\n",
+    "        )\n",
+    "        item = {key: val.squeeze(0) for key, val in encoding.items()}\n",
+    "        item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n",
+    "        return item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3457677b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:23.983649Z",
+     "iopub.status.busy": "2025-07-31T07:31:23.982906Z",
+     "iopub.status.idle": "2025-07-31T07:31:23.987052Z",
+     "shell.execute_reply": "2025-07-31T07:31:23.986540Z"
+    },
+    "papermill": {
+     "duration": 0.010795,
+     "end_time": "2025-07-31T07:31:23.988166",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:23.977371",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_dataset = TextDataset(X_train, y_train, tokenizer)\n",
+    "test_dataset = TextDataset(X_test, y_test, tokenizer)\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8ecf1b5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004299,
+     "end_time": "2025-07-31T07:31:23.997029",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:23.992730",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Conditional models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ed498594",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:24.007303Z",
+     "iopub.status.busy": "2025-07-31T07:31:24.007036Z",
+     "iopub.status.idle": "2025-07-31T07:31:24.010557Z",
+     "shell.execute_reply": "2025-07-31T07:31:24.009976Z"
+    },
+    "papermill": {
+     "duration": 0.009594,
+     "end_time": "2025-07-31T07:31:24.011611",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:24.002017",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# import torch\n",
+    "# import torch.nn as nn\n",
+    "\n",
+    "# class BiLSTMClassifier(nn.Module):\n",
+    "#     def __init__(self, vocab_size, embedding_dim, hidden_dim, num_labels, padding_idx=0, dropout=0.3):\n",
+    "#         super(BiLSTMClassifier, self).__init__()\n",
+    "#         self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=padding_idx)\n",
+    "#         self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=1,\n",
+    "#                             bidirectional=True, batch_first=True)\n",
+    "#         self.dropout = nn.Dropout(dropout)\n",
+    "#         self.classifier = nn.Linear(hidden_dim * 2, num_labels)  # BiLSTM: hidden_dim * 2\n",
+    "\n",
+    "#     def forward(self, input_ids, attention_mask=None):\n",
+    "#         embedded = self.embedding(input_ids)  # [B, T, E]\n",
+    "#         lstm_out, _ = self.lstm(embedded)     # [B, T, H*2]\n",
+    "#         # Lấy ra vector tại vị trí cuối cùng có attention_mask = 1 (nếu có mask)\n",
+    "#         if attention_mask is not None:\n",
+    "#             lengths = attention_mask.sum(dim=1) - 1  # vị trí cuối có token\n",
+    "#             last_outputs = lstm_out[torch.arange(lstm_out.size(0)), lengths]\n",
+    "#         else:\n",
+    "#             last_outputs = lstm_out[:, -1, :]\n",
+    "#         out = self.dropout(last_outputs)\n",
+    "#         return self.classifier(out)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "eccbce05",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:24.021799Z",
+     "iopub.status.busy": "2025-07-31T07:31:24.021590Z",
+     "iopub.status.idle": "2025-07-31T07:31:24.025159Z",
+     "shell.execute_reply": "2025-07-31T07:31:24.024656Z"
+    },
+    "papermill": {
+     "duration": 0.009823,
+     "end_time": "2025-07-31T07:31:24.026191",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:24.016368",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import classification_report, accuracy_score\n",
+    "# from torch.optim import AdamW\n",
+    "\n",
+    "# # Khởi tạo mô hình\n",
+    "# model = BiLSTMClassifier(\n",
+    "#     vocab_size=tokenizer.vocab_size,\n",
+    "#     embedding_dim=768,\n",
+    "#     hidden_dim=768,\n",
+    "#     num_labels=len(set(y_train))\n",
+    "# ).to(device)\n",
+    "\n",
+    "# optimizer = AdamW(model.parameters(), lr=2e-4)\n",
+    "# criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# # Train + Eval loop\n",
+    "# for epoch in range(20):\n",
+    "#     model.train()\n",
+    "#     total_loss = 0\n",
+    "    \n",
+    "#     for batch in train_loader:\n",
+    "#         input_ids = batch['input_ids'].to(device)\n",
+    "#         labels = batch['labels'].to(device)\n",
+    "#         attention_mask = batch.get('attention_mask', None)\n",
+    "#         if attention_mask is not None:\n",
+    "#             attention_mask = attention_mask.to(device)\n",
+    "\n",
+    "#         optimizer.zero_grad()\n",
+    "#         outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
+    "#         loss = criterion(outputs, labels)\n",
+    "#         loss.backward()\n",
+    "#         optimizer.step()\n",
+    "#         total_loss += loss.item()\n",
+    "    \n",
+    "#     # Evaluation\n",
+    "#     model.eval()\n",
+    "#     all_preds = []\n",
+    "#     all_labels = []\n",
+    "\n",
+    "#     with torch.no_grad():\n",
+    "#         for batch in test_loader:\n",
+    "#             input_ids = batch['input_ids'].to(device)\n",
+    "#             labels = batch['labels'].to(device)\n",
+    "#             attention_mask = batch.get('attention_mask', None)\n",
+    "#             if attention_mask is not None:\n",
+    "#                 attention_mask = attention_mask.to(device)\n",
+    "\n",
+    "#             outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
+    "#             preds = torch.argmax(outputs, dim=1)\n",
+    "#             all_preds.extend(preds.cpu().numpy())\n",
+    "#             all_labels.extend(labels.cpu().numpy())\n",
+    "\n",
+    "#     accuracy = accuracy_score(all_labels, all_preds)\n",
+    "#     report = classification_report(all_labels, all_preds)\n",
+    "#     print(f\"Epoch {epoch+1}\")\n",
+    "#     print(f\"Loss: {total_loss / len(train_loader):.4f}\")\n",
+    "#     print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "#     print(report)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf4d977f",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004252,
+     "end_time": "2025-07-31T07:31:24.035118",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:24.030866",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Attention-based models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e53a3b8f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:24.044794Z",
+     "iopub.status.busy": "2025-07-31T07:31:24.044591Z",
+     "iopub.status.idle": "2025-07-31T07:31:29.955710Z",
+     "shell.execute_reply": "2025-07-31T07:31:29.954490Z"
+    },
+    "papermill": {
+     "duration": 5.917952,
+     "end_time": "2025-07-31T07:31:29.957530",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:24.039578",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "521e10dedafe4b4c9a41931c3a38e9cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "RobertaForSequenceClassification(\n",
+       "  (roberta): RobertaModel(\n",
+       "    (embeddings): RobertaEmbeddings(\n",
+       "      (word_embeddings): Embedding(50265, 1024, padding_idx=1)\n",
+       "      (position_embeddings): Embedding(514, 1024, padding_idx=1)\n",
+       "      (token_type_embeddings): Embedding(1, 1024)\n",
+       "      (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "      (dropout): Dropout(p=0.1, inplace=False)\n",
+       "    )\n",
+       "    (encoder): RobertaEncoder(\n",
+       "      (layer): ModuleList(\n",
+       "        (0-23): 24 x RobertaLayer(\n",
+       "          (attention): RobertaAttention(\n",
+       "            (self): RobertaSdpaSelfAttention(\n",
+       "              (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (dropout): Dropout(p=0.1, inplace=False)\n",
+       "            )\n",
+       "            (output): RobertaSelfOutput(\n",
+       "              (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "              (dropout): Dropout(p=0.1, inplace=False)\n",
+       "            )\n",
+       "          )\n",
+       "          (intermediate): RobertaIntermediate(\n",
+       "            (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
+       "            (intermediate_act_fn): GELUActivation()\n",
+       "          )\n",
+       "          (output): RobertaOutput(\n",
+       "            (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
+       "            (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "            (dropout): Dropout(p=0.1, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (classifier): RobertaClassificationHead(\n",
+       "    (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "    (dropout): Dropout(p=0.1, inplace=False)\n",
+       "    (out_proj): Linear(in_features=1024, out_features=10, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model = RobertaForSequenceClassification.from_pretrained(\n",
+    "    'roberta-large',\n",
+    "    num_labels=len(set(y_train))  # số lượng nhãn\n",
+    ")\n",
+    "# model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "#     'bert-large-uncased',\n",
+    "#     num_labels=len(set(y_train))  # số lượng nhãn\n",
+    "# )\n",
+    "model.to(device)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "05e65036",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-31T07:31:29.972415Z",
+     "iopub.status.busy": "2025-07-31T07:31:29.971960Z",
+     "iopub.status.idle": "2025-07-31T08:07:40.097372Z",
+     "shell.execute_reply": "2025-07-31T08:07:40.096566Z"
+    },
+    "papermill": {
+     "duration": 2170.139101,
+     "end_time": "2025-07-31T08:07:40.105408",
+     "exception": false,
+     "start_time": "2025-07-31T07:31:29.966307",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.1594\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.00      0.00      0.00        15\n",
+      "           1       0.00      0.00      0.00        30\n",
+      "           2       0.00      0.00      0.00        24\n",
+      "           3       0.00      0.00      0.00        40\n",
+      "           4       0.00      0.00      0.00        37\n",
+      "           5       0.00      0.00      0.00        13\n",
+      "           6       1.00      0.02      0.04        54\n",
+      "           7       0.16      1.00      0.27        50\n",
+      "           8       0.00      0.00      0.00        39\n",
+      "           9       0.00      0.00      0.00        18\n",
+      "\n",
+      "    accuracy                           0.16       320\n",
+      "   macro avg       0.12      0.10      0.03       320\n",
+      "weighted avg       0.19      0.16      0.05       320\n",
+      "\n",
+      "Epoch 1, Loss: 2.3119\n",
+      "Accuracy: 0.3063\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.00      0.00      0.00        15\n",
+      "           1       0.00      0.00      0.00        30\n",
+      "           2       0.55      0.25      0.34        24\n",
+      "           3       0.41      0.17      0.25        40\n",
+      "           4       0.21      0.59      0.31        37\n",
+      "           5       0.00      0.00      0.00        13\n",
+      "           6       0.53      0.43      0.47        54\n",
+      "           7       0.28      0.78      0.41        50\n",
+      "           8       0.00      0.00      0.00        39\n",
+      "           9       1.00      0.06      0.11        18\n",
+      "\n",
+      "    accuracy                           0.31       320\n",
+      "   macro avg       0.30      0.23      0.19       320\n",
+      "weighted avg       0.31      0.31      0.24       320\n",
+      "\n",
+      "Epoch 2, Loss: 2.1933\n",
+      "Accuracy: 0.4313\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.00      0.00      0.00        15\n",
+      "           1       0.00      0.00      0.00        30\n",
+      "           2       0.43      0.67      0.52        24\n",
+      "           3       0.51      0.60      0.55        40\n",
+      "           4       0.25      0.73      0.37        37\n",
+      "           5       0.00      0.00      0.00        13\n",
+      "           6       0.76      0.52      0.62        54\n",
+      "           7       0.53      0.34      0.41        50\n",
+      "           8       0.50      0.46      0.48        39\n",
+      "           9       0.42      0.44      0.43        18\n",
+      "\n",
+      "    accuracy                           0.43       320\n",
+      "   macro avg       0.34      0.38      0.34       320\n",
+      "weighted avg       0.42      0.43      0.40       320\n",
+      "\n",
+      "Epoch 3, Loss: 1.7082\n",
+      "Accuracy: 0.4281\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.24      0.27      0.25        15\n",
+      "           1       0.80      0.13      0.23        30\n",
+      "           2       0.41      0.58      0.48        24\n",
+      "           3       0.52      0.57      0.55        40\n",
+      "           4       0.53      0.22      0.31        37\n",
+      "           5       0.24      0.31      0.27        13\n",
+      "           6       0.68      0.56      0.61        54\n",
+      "           7       0.46      0.38      0.42        50\n",
+      "           8       0.26      0.59      0.36        39\n",
+      "           9       0.57      0.44      0.50        18\n",
+      "\n",
+      "    accuracy                           0.43       320\n",
+      "   macro avg       0.47      0.41      0.40       320\n",
+      "weighted avg       0.50      0.43      0.42       320\n",
+      "\n",
+      "Epoch 4, Loss: 0.9923\n",
+      "Accuracy: 0.4594\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.27      0.20      0.23        15\n",
+      "           1       0.61      0.37      0.46        30\n",
+      "           2       0.69      0.38      0.49        24\n",
+      "           3       0.58      0.55      0.56        40\n",
+      "           4       0.39      0.35      0.37        37\n",
+      "           5       0.17      0.46      0.25        13\n",
+      "           6       0.76      0.59      0.67        54\n",
+      "           7       0.35      0.58      0.43        50\n",
+      "           8       0.52      0.33      0.41        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.46       320\n",
+      "   macro avg       0.48      0.43      0.43       320\n",
+      "weighted avg       0.52      0.46      0.47       320\n",
+      "\n",
+      "Epoch 5, Loss: 0.3317\n",
+      "Accuracy: 0.4375\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.12      0.13      0.12        15\n",
+      "           1       0.52      0.47      0.49        30\n",
+      "           2       0.60      0.50      0.55        24\n",
+      "           3       0.83      0.25      0.38        40\n",
+      "           4       0.40      0.32      0.36        37\n",
+      "           5       0.14      0.62      0.23        13\n",
+      "           6       0.63      0.63      0.63        54\n",
+      "           7       0.45      0.44      0.44        50\n",
+      "           8       0.46      0.41      0.43        39\n",
+      "           9       0.50      0.56      0.53        18\n",
+      "\n",
+      "    accuracy                           0.44       320\n",
+      "   macro avg       0.46      0.43      0.42       320\n",
+      "weighted avg       0.52      0.44      0.45       320\n",
+      "\n",
+      "Epoch 6, Loss: 0.0763\n",
+      "Accuracy: 0.4500\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.14      0.13      0.14        15\n",
+      "           1       0.50      0.40      0.44        30\n",
+      "           2       0.41      0.50      0.45        24\n",
+      "           3       0.58      0.47      0.52        40\n",
+      "           4       0.38      0.30      0.33        37\n",
+      "           5       0.14      0.31      0.19        13\n",
+      "           6       0.64      0.72      0.68        54\n",
+      "           7       0.45      0.40      0.43        50\n",
+      "           8       0.50      0.38      0.43        39\n",
+      "           9       0.37      0.56      0.44        18\n",
+      "\n",
+      "    accuracy                           0.45       320\n",
+      "   macro avg       0.41      0.42      0.41       320\n",
+      "weighted avg       0.47      0.45      0.45       320\n",
+      "\n",
+      "Epoch 7, Loss: 0.0185\n",
+      "Accuracy: 0.4594\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.15      0.20      0.17        15\n",
+      "           1       0.40      0.53      0.46        30\n",
+      "           2       0.48      0.50      0.49        24\n",
+      "           3       0.62      0.50      0.56        40\n",
+      "           4       0.33      0.38      0.35        37\n",
+      "           5       0.21      0.38      0.27        13\n",
+      "           6       0.64      0.65      0.64        54\n",
+      "           7       0.53      0.40      0.45        50\n",
+      "           8       0.57      0.33      0.42        39\n",
+      "           9       0.45      0.50      0.47        18\n",
+      "\n",
+      "    accuracy                           0.46       320\n",
+      "   macro avg       0.44      0.44      0.43       320\n",
+      "weighted avg       0.49      0.46      0.47       320\n",
+      "\n",
+      "Epoch 8, Loss: 0.0075\n",
+      "Accuracy: 0.4531\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.18      0.27      0.22        15\n",
+      "           1       0.45      0.43      0.44        30\n",
+      "           2       0.48      0.50      0.49        24\n",
+      "           3       0.59      0.47      0.53        40\n",
+      "           4       0.35      0.38      0.36        37\n",
+      "           5       0.15      0.31      0.21        13\n",
+      "           6       0.64      0.65      0.64        54\n",
+      "           7       0.48      0.44      0.46        50\n",
+      "           8       0.52      0.33      0.41        39\n",
+      "           9       0.45      0.50      0.47        18\n",
+      "\n",
+      "    accuracy                           0.45       320\n",
+      "   macro avg       0.43      0.43      0.42       320\n",
+      "weighted avg       0.48      0.45      0.46       320\n",
+      "\n",
+      "Epoch 9, Loss: 0.0042\n",
+      "Accuracy: 0.4625\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.20      0.27      0.23        15\n",
+      "           1       0.45      0.43      0.44        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.62      0.50      0.56        40\n",
+      "           4       0.34      0.38      0.36        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.64      0.69      0.66        54\n",
+      "           7       0.49      0.44      0.46        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.45      0.50      0.47        18\n",
+      "\n",
+      "    accuracy                           0.46       320\n",
+      "   macro avg       0.44      0.43      0.43       320\n",
+      "weighted avg       0.49      0.46      0.47       320\n",
+      "\n",
+      "Epoch 10, Loss: 0.0031\n",
+      "Accuracy: 0.4656\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.46      0.43      0.45        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.61      0.50      0.55        40\n",
+      "           4       0.35      0.38      0.36        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.64      0.70      0.67        54\n",
+      "           7       0.48      0.44      0.46        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.45      0.50      0.47        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.44      0.44      0.43       320\n",
+      "weighted avg       0.49      0.47      0.47       320\n",
+      "\n",
+      "Epoch 11, Loss: 0.0026\n",
+      "Accuracy: 0.4656\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.46      0.43      0.45        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.61      0.50      0.55        40\n",
+      "           4       0.35      0.38      0.36        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.64      0.70      0.67        54\n",
+      "           7       0.48      0.44      0.46        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.45      0.50      0.47        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.44      0.44      0.43       320\n",
+      "weighted avg       0.49      0.47      0.47       320\n",
+      "\n",
+      "Epoch 12, Loss: 0.0022\n",
+      "Accuracy: 0.4688\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.48      0.43      0.46        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.62      0.53      0.57        40\n",
+      "           4       0.37      0.41      0.38        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.63      0.70      0.67        54\n",
+      "           7       0.48      0.42      0.45        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.45      0.50      0.47        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.44      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.47       320\n",
+      "\n",
+      "Epoch 13, Loss: 0.0019\n",
+      "Accuracy: 0.4719\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.50      0.43      0.46        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.62      0.53      0.57        40\n",
+      "           4       0.38      0.41      0.39        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.63      0.70      0.67        54\n",
+      "           7       0.49      0.44      0.46        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.44      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.48       320\n",
+      "\n",
+      "Epoch 14, Loss: 0.0017\n",
+      "Accuracy: 0.4719\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.50      0.43      0.46        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.62      0.53      0.57        40\n",
+      "           4       0.38      0.41      0.39        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.63      0.70      0.67        54\n",
+      "           7       0.49      0.44      0.46        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.44      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.48       320\n",
+      "\n",
+      "Epoch 15, Loss: 0.0015\n",
+      "Accuracy: 0.4719\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.50      0.43      0.46        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.62      0.53      0.57        40\n",
+      "           4       0.38      0.41      0.39        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.63      0.70      0.67        54\n",
+      "           7       0.48      0.44      0.46        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.44      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.48       320\n",
+      "\n",
+      "Epoch 16, Loss: 0.0014\n",
+      "Accuracy: 0.4719\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.52      0.47      0.49        30\n",
+      "           2       0.55      0.50      0.52        24\n",
+      "           3       0.64      0.53      0.58        40\n",
+      "           4       0.38      0.41      0.39        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.62      0.70      0.66        54\n",
+      "           7       0.47      0.42      0.44        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.45      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.48       320\n",
+      "\n",
+      "Epoch 17, Loss: 0.0012\n",
+      "Accuracy: 0.4719\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.54      0.47      0.50        30\n",
+      "           2       0.52      0.50      0.51        24\n",
+      "           3       0.64      0.53      0.58        40\n",
+      "           4       0.38      0.41      0.39        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.62      0.70      0.66        54\n",
+      "           7       0.47      0.42      0.44        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.45      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.48       320\n",
+      "\n",
+      "Epoch 18, Loss: 0.0011\n",
+      "Accuracy: 0.4719\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.54      0.47      0.50        30\n",
+      "           2       0.52      0.50      0.51        24\n",
+      "           3       0.64      0.53      0.58        40\n",
+      "           4       0.38      0.41      0.39        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.62      0.70      0.66        54\n",
+      "           7       0.47      0.42      0.44        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.45      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.48       320\n",
+      "\n",
+      "Epoch 19, Loss: 0.0010\n",
+      "Accuracy: 0.4719\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.21      0.27      0.24        15\n",
+      "           1       0.54      0.47      0.50        30\n",
+      "           2       0.52      0.50      0.51        24\n",
+      "           3       0.64      0.53      0.58        40\n",
+      "           4       0.38      0.41      0.39        37\n",
+      "           5       0.15      0.31      0.20        13\n",
+      "           6       0.62      0.70      0.66        54\n",
+      "           7       0.47      0.42      0.44        50\n",
+      "           8       0.50      0.33      0.40        39\n",
+      "           9       0.43      0.50      0.46        18\n",
+      "\n",
+      "    accuracy                           0.47       320\n",
+      "   macro avg       0.45      0.44      0.44       320\n",
+      "weighted avg       0.49      0.47      0.48       320\n",
+      "\n",
+      "Epoch 20, Loss: 0.0009\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
+    "\n",
+    "model.train()\n",
+    "for epoch in range(20):\n",
+    "    total_loss = 0\n",
+    "    for batch in train_loader:\n",
+    "        input_ids = batch['input_ids'].to(device)\n",
+    "        attention_mask = batch['attention_mask'].to(device)\n",
+    "        labels = batch['labels'].to(device)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
+    "        loss = outputs.loss\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        total_loss += loss.item()\n",
+    "    model.eval()\n",
+    "    all_preds = []\n",
+    "    all_labels = []\n",
+    "    \n",
+    "    with torch.no_grad():\n",
+    "        for batch in test_loader:\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "            attention_mask = batch['attention_mask'].to(device)\n",
+    "            labels = batch['labels'].to(device)\n",
+    "    \n",
+    "            outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
+    "            preds = torch.argmax(outputs.logits, dim=1)\n",
+    "    \n",
+    "            all_preds.extend(preds.cpu().numpy())\n",
+    "            all_labels.extend(labels.cpu().numpy())\n",
+    "    \n",
+    "    accuracy = accuracy_score(all_labels, all_preds)\n",
+    "    report = classification_report(all_labels, all_preds)\n",
+    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "    print(report)\n",
+    "    print(f\"Epoch {epoch+1}, Loss: {total_loss/len(train_loader):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
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+     "status": "completed"
+    },
+    "tags": []
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+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import classification_report\n",
+    "# # from torch.nn.utils import clip_grad_norm_\n",
+    "\n",
+    "# accumulation_steps = 2\n",
+    "# optimizer = AdamW(model.parameters(), lr=2e-5)\n",
+    "\n",
+    "# model.train()\n",
+    "# for epoch in range(20):\n",
+    "#     total_loss = 0\n",
+    "#     model.train()\n",
+    "    \n",
+    "#     for step, batch in enumerate(train_loader):\n",
+    "#         input_ids = batch['input_ids'].to(device)\n",
+    "#         attention_mask = batch['attention_mask'].to(device)\n",
+    "#         labels = batch['labels'].to(device)\n",
+    "\n",
+    "#         outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
+    "#         loss = outputs.loss / accumulation_steps  # normalize loss\n",
+    "#         loss.backward()\n",
+    "\n",
+    "#         if (step + 1) % accumulation_steps == 0 or (step + 1) == len(train_loader):\n",
+    "#             # Optionally clip gradients\n",
+    "#             # clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "#             optimizer.step()\n",
+    "#             optimizer.zero_grad()\n",
+    "        \n",
+    "#         total_loss += loss.item() * accumulation_steps  # multiply to restore original loss\n",
+    "\n",
+    "#     model.eval()\n",
+    "#     all_preds = []\n",
+    "#     all_labels = []\n",
+    "\n",
+    "#     with torch.no_grad():\n",
+    "#         for batch in test_loader:\n",
+    "#             input_ids = batch['input_ids'].to(device)\n",
+    "#             attention_mask = batch['attention_mask'].to(device)\n",
+    "#             labels = batch['labels'].to(device)\n",
+    "\n",
+    "#             outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
+    "#             preds = torch.argmax(outputs.logits, dim=1)\n",
+    "\n",
+    "#             all_preds.extend(preds.cpu().numpy())\n",
+    "#             all_labels.extend(labels.cpu().numpy())\n",
+    "\n",
+    "#     accuracy = accuracy_score(all_labels, all_preds)\n",
+    "#     report = classification_report(all_labels, all_preds)\n",
+    "#     print(f\"Epoch {epoch+1} | Loss: {total_loss/len(train_loader):.4f} | Accuracy: {accuracy:.4f}\")\n",
+    "#     print(report)\n"
+   ]
+  },
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