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cognitive-distortion-detection-deeplearning.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5bba7abb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:15.252393Z",
+     "iopub.status.busy": "2025-09-29T03:52:15.252092Z",
+     "iopub.status.idle": "2025-09-29T03:52:17.151894Z",
+     "shell.execute_reply": "2025-09-29T03:52:17.150967Z"
+    },
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+     "exception": false,
+     "start_time": "2025-09-29T03:52:15.246266",
+     "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": "9f73e599",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:17.163059Z",
+     "iopub.status.busy": "2025-09-29T03:52:17.162673Z",
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+    },
+    "papermill": {
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+     "end_time": "2025-09-29T03:52:17.252914",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:17.158292",
+     "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",
+       "    </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/cognitive-distortion-detetction-dataset/Annotated_data.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4bdec7e0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:17.262119Z",
+     "iopub.status.busy": "2025-09-29T03:52:17.261822Z",
+     "iopub.status.idle": "2025-09-29T03:52:17.264808Z",
+     "shell.execute_reply": "2025-09-29T03:52:17.264189Z"
+    },
+    "papermill": {
+     "duration": 0.00864,
+     "end_time": "2025-09-29T03:52:17.265921",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:17.257281",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# df=df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "49316246",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:17.274826Z",
+     "iopub.status.busy": "2025-09-29T03:52:17.274598Z",
+     "iopub.status.idle": "2025-09-29T03:52:17.277625Z",
+     "shell.execute_reply": "2025-09-29T03:52:17.276818Z"
+    },
+    "papermill": {
+     "duration": 0.009205,
+     "end_time": "2025-09-29T03:52:17.279273",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:17.270068",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# lengths = df['Distorted part'].apply(lambda x: len(x.split()))\n",
+    "\n",
+    "# plt.figure(figsize=(10, 5))\n",
+    "# plt.hist(lengths, bins=30, edgecolor='k', alpha=0.7)\n",
+    "# plt.title('Distribution of sentence lengths')\n",
+    "# plt.xlabel('Sentence Length')\n",
+    "# plt.ylabel('Number of Sentences')\n",
+    "# plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "872f92ef",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:17.287846Z",
+     "iopub.status.busy": "2025-09-29T03:52:17.287596Z",
+     "iopub.status.idle": "2025-09-29T03:52:17.404691Z",
+     "shell.execute_reply": "2025-09-29T03:52:17.403977Z"
+    },
+    "papermill": {
+     "duration": 0.123138,
+     "end_time": "2025-09-29T03:52:17.406334",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:17.283196",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Tạo LabelEncoder\n",
+    "encoder = LabelEncoder()\n",
+    "\n",
+    "# Mã hóa cột 'Dominant Distortion', 'No Distortion' thành 0, các loại còn lại thành 1\n",
+    "df['Dominant Distortion Encoded'] = encoder.fit_transform(df['Dominant Distortion'].apply(lambda x: 0 if x == 'No Distortion' else 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d0c55440",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:17.415784Z",
+     "iopub.status.busy": "2025-09-29T03:52:17.415525Z",
+     "iopub.status.idle": "2025-09-29T03:52:18.256425Z",
+     "shell.execute_reply": "2025-09-29T03:52:18.255403Z"
+    },
+    "papermill": {
+     "duration": 0.84688,
+     "end_time": "2025-09-29T03:52:18.257776",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:17.410896",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "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]   Unzipping corpora/stopwords.zip.\n",
+      "[nltk_data] Downloading package wordnet to /usr/share/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "i luv programmng u r gr8 i cant believe its 123 times better\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)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d232a9ba",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:18.267764Z",
+     "iopub.status.busy": "2025-09-29T03:52:18.267488Z",
+     "iopub.status.idle": "2025-09-29T03:52:18.451213Z",
+     "shell.execute_reply": "2025-09-29T03:52:18.450248Z"
+    },
+    "papermill": {
+     "duration": 0.190457,
+     "end_time": "2025-09-29T03:52:18.452821",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:18.262364",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df['cleaned'] = df['Patient Question'].map(lambda text: preprocess_text(text))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0e75019a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:18.463419Z",
+     "iopub.status.busy": "2025-09-29T03:52:18.463120Z",
+     "iopub.status.idle": "2025-09-29T03:52:18.466631Z",
+     "shell.execute_reply": "2025-09-29T03:52:18.465966Z"
+    },
+    "papermill": {
+     "duration": 0.009927,
+     "end_time": "2025-09-29T03:52:18.467830",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:18.457903",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from collections import Counter\n",
+    "\n",
+    "# def create_corpus():\n",
+    "#     corpus = []\n",
+    "#     for x in df['cleaned'].str.split():\n",
+    "#         for i in x:\n",
+    "#             corpus.append(i)\n",
+    "#     return corpus\n",
+    "\n",
+    "# corpus = create_corpus()\n",
+    "\n",
+    "# counter = Counter(corpus)\n",
+    "# most_common_words = counter.most_common(40)\n",
+    "\n",
+    "# x = []\n",
+    "# y = []\n",
+    "# for word, count in most_common_words:\n",
+    "#     x.append(word)\n",
+    "#     y.append(count)\n",
+    "\n",
+    "# plt.figure(figsize=(10, 8))\n",
+    "# sns.barplot(x=y, y=x, palette='viridis')\n",
+    "# plt.xlabel('Count')\n",
+    "# plt.ylabel('Words')\n",
+    "# plt.title('Top 40 Most Common Words in review')\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a6d0e1ec",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:18.476634Z",
+     "iopub.status.busy": "2025-09-29T03:52:18.476371Z",
+     "iopub.status.idle": "2025-09-29T03:52:18.479532Z",
+     "shell.execute_reply": "2025-09-29T03:52:18.478900Z"
+    },
+    "papermill": {
+     "duration": 0.008762,
+     "end_time": "2025-09-29T03:52:18.480677",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:18.471915",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from wordcloud import WordCloud\n",
+    "# import matplotlib.pyplot as plt\n",
+    "# import numpy as np\n",
+    "# from PIL import Image, ImageOps\n",
+    "\n",
+    "# text = ' '.join(df['cleaned'])\n",
+    "\n",
+    "# mask_img = Image.open('/kaggle/input/butter-mask/c26289174f9d2ee80a9bc60455257030.png').convert(\"L\")  \n",
+    "# mask_img_inverted = ImageOps.invert(mask_img)  \n",
+    "\n",
+    "# mask = np.array(mask_img_inverted)\n",
+    "# mask[mask == 255] = 0\n",
+    "# mask[mask == 158] = 255\n",
+    "\n",
+    "# wordcloud = WordCloud(background_color='white',\n",
+    "#                       max_words=2000,\n",
+    "#                       mask=mask,\n",
+    "#                       contour_width=3,\n",
+    "#                       contour_color='white',\n",
+    "#                       colormap='viridis').generate(text)\n",
+    "\n",
+    "# # Hiển thị\n",
+    "# plt.figure(figsize=(10, 10))\n",
+    "# plt.imshow(wordcloud, interpolation='bilinear')\n",
+    "# plt.axis('off')\n",
+    "# plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cd48ecdc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:18.489624Z",
+     "iopub.status.busy": "2025-09-29T03:52:18.489357Z",
+     "iopub.status.idle": "2025-09-29T03:52:18.494641Z",
+     "shell.execute_reply": "2025-09-29T03:52:18.493650Z"
+    },
+    "papermill": {
+     "duration": 0.011092,
+     "end_time": "2025-09-29T03:52:18.495952",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:18.484860",
+     "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['Patient Question'].values,\n",
+    "                                                    df['Dominant Distortion Encoded'].values,\n",
+    "                                                    test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a5c6c9cd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:18.504805Z",
+     "iopub.status.busy": "2025-09-29T03:52:18.504562Z",
+     "iopub.status.idle": "2025-09-29T03:52:18.509148Z",
+     "shell.execute_reply": "2025-09-29T03:52:18.508343Z"
+    },
+    "papermill": {
+     "duration": 0.010425,
+     "end_time": "2025-09-29T03:52:18.510499",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:18.500074",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, ..., 1, 0, 0])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ca64a24f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:18.519582Z",
+     "iopub.status.busy": "2025-09-29T03:52:18.519345Z",
+     "iopub.status.idle": "2025-09-29T03:52:38.984259Z",
+     "shell.execute_reply": "2025-09-29T03:52:38.983492Z"
+    },
+    "papermill": {
+     "duration": 20.471052,
+     "end_time": "2025-09-29T03:52:38.985827",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:18.514775",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.optim import AdamW  # ✅ Sửa tại đây\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": 13,
+   "id": "7623d594",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:38.995659Z",
+     "iopub.status.busy": "2025-09-29T03:52:38.995122Z",
+     "iopub.status.idle": "2025-09-29T03:52:38.999547Z",
+     "shell.execute_reply": "2025-09-29T03:52:38.998744Z"
+    },
+    "papermill": {
+     "duration": 0.010439,
+     "end_time": "2025-09-29T03:52:39.000804",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:38.990365",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.47.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import transformers\n",
+    "\n",
+    "print(transformers.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c06d0ef3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:39.010246Z",
+     "iopub.status.busy": "2025-09-29T03:52:39.009943Z",
+     "iopub.status.idle": "2025-09-29T03:52:40.957564Z",
+     "shell.execute_reply": "2025-09-29T03:52:40.956570Z"
+    },
+    "papermill": {
+     "duration": 1.954124,
+     "end_time": "2025-09-29T03:52:40.959317",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:39.005193",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81d6d531b8bf46208b1dbf3a07b3da7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95dc73ea287e4f65b5b9c2184828b60a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/580 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e7dde90a5554fbba23248a6df60976d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "spm.model:   0%|          | 0.00/2.46M [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('microsoft/deberta-v3-large')\n",
+    "\n",
+    "class TextDataset(Dataset):\n",
+    "    def __init__(self, texts, labels, tokenizer, max_len=512):\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": 15,
+   "id": "b3a23bed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:40.969720Z",
+     "iopub.status.busy": "2025-09-29T03:52:40.969452Z",
+     "iopub.status.idle": "2025-09-29T03:52:40.973769Z",
+     "shell.execute_reply": "2025-09-29T03:52:40.972885Z"
+    },
+    "papermill": {
+     "duration": 0.010896,
+     "end_time": "2025-09-29T03:52:40.975148",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:40.964252",
+     "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=2, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5d8efc44",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:40.985643Z",
+     "iopub.status.busy": "2025-09-29T03:52:40.985391Z",
+     "iopub.status.idle": "2025-09-29T03:52:46.716501Z",
+     "shell.execute_reply": "2025-09-29T03:52:46.715103Z"
+    },
+    "papermill": {
+     "duration": 5.738345,
+     "end_time": "2025-09-29T03:52:46.718089",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:40.979744",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fecc138b4f54f5b97d0b04442b6711a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/874M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-v3-large and are newly initialized: ['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.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": [
+       "DebertaV2ForSequenceClassification(\n",
+       "  (deberta): DebertaV2Model(\n",
+       "    (embeddings): DebertaV2Embeddings(\n",
+       "      (word_embeddings): Embedding(128100, 1024, padding_idx=0)\n",
+       "      (LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)\n",
+       "      (dropout): Dropout(p=0.1, inplace=False)\n",
+       "    )\n",
+       "    (encoder): DebertaV2Encoder(\n",
+       "      (layer): ModuleList(\n",
+       "        (0-23): 24 x DebertaV2Layer(\n",
+       "          (attention): DebertaV2Attention(\n",
+       "            (self): DisentangledSelfAttention(\n",
+       "              (query_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (key_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (value_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (pos_dropout): Dropout(p=0.1, inplace=False)\n",
+       "              (dropout): Dropout(p=0.1, inplace=False)\n",
+       "            )\n",
+       "            (output): DebertaV2SelfOutput(\n",
+       "              (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "              (LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)\n",
+       "              (dropout): Dropout(p=0.1, inplace=False)\n",
+       "            )\n",
+       "          )\n",
+       "          (intermediate): DebertaV2Intermediate(\n",
+       "            (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
+       "            (intermediate_act_fn): GELUActivation()\n",
+       "          )\n",
+       "          (output): DebertaV2Output(\n",
+       "            (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
+       "            (LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)\n",
+       "            (dropout): Dropout(p=0.1, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (rel_embeddings): Embedding(512, 1024)\n",
+       "      (LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (pooler): ContextPooler(\n",
+       "    (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "    (dropout): Dropout(p=0, inplace=False)\n",
+       "  )\n",
+       "  (classifier): Linear(in_features=1024, out_features=2, bias=True)\n",
+       "  (dropout): Dropout(p=0.1, inplace=False)\n",
+       ")"
+      ]
+     },
+     "execution_count": 16,
+     "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)) \n",
+    "# )\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    'microsoft/deberta-v3-large',\n",
+    "    num_labels=len(set(y_train)) \n",
+    ")\n",
+    "model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3f6073fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:46.729189Z",
+     "iopub.status.busy": "2025-09-29T03:52:46.728839Z",
+     "iopub.status.idle": "2025-09-29T03:52:46.732796Z",
+     "shell.execute_reply": "2025-09-29T03:52:46.731925Z"
+    },
+    "papermill": {
+     "duration": 0.011498,
+     "end_time": "2025-09-29T03:52:46.734629",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:46.723131",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import classification_report\n",
+    "\n",
+    "# optimizer = AdamW(model.parameters(), lr=2e-5)\n",
+    "\n",
+    "# model.train()\n",
+    "# for epoch in range(10):\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": 18,
+   "id": "6ca40d29",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T03:52:46.754858Z",
+     "iopub.status.busy": "2025-09-29T03:52:46.754566Z",
+     "iopub.status.idle": "2025-09-29T05:30:24.025725Z",
+     "shell.execute_reply": "2025-09-29T05:30:24.024812Z"
+    },
+    "papermill": {
+     "duration": 5857.284843,
+     "end_time": "2025-09-29T05:30:24.032714",
+     "exception": false,
+     "start_time": "2025-09-29T03:52:46.747871",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 0.1061\n",
+      "Accuracy: 0.6542\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.00      0.00      0.00       175\n",
+      "           1       0.65      1.00      0.79       331\n",
+      "\n",
+      "    accuracy                           0.65       506\n",
+      "   macro avg       0.33      0.50      0.40       506\n",
+      "weighted avg       0.43      0.65      0.52       506\n",
+      "\n",
+      "Epoch 2, Loss: 0.0955\n",
+      "Accuracy: 0.7708\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.65      0.72      0.68       175\n",
+      "           1       0.84      0.80      0.82       331\n",
+      "\n",
+      "    accuracy                           0.77       506\n",
+      "   macro avg       0.75      0.76      0.75       506\n",
+      "weighted avg       0.78      0.77      0.77       506\n",
+      "\n",
+      "Epoch 3, Loss: 0.0781\n",
+      "Accuracy: 0.6779\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.52      0.89      0.66       175\n",
+      "           1       0.90      0.57      0.70       331\n",
+      "\n",
+      "    accuracy                           0.68       506\n",
+      "   macro avg       0.71      0.73      0.68       506\n",
+      "weighted avg       0.77      0.68      0.68       506\n",
+      "\n",
+      "Epoch 4, Loss: 0.0506\n",
+      "Accuracy: 0.7727\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.75      0.52      0.61       175\n",
+      "           1       0.78      0.91      0.84       331\n",
+      "\n",
+      "    accuracy                           0.77       506\n",
+      "   macro avg       0.76      0.71      0.73       506\n",
+      "weighted avg       0.77      0.77      0.76       506\n",
+      "\n",
+      "Epoch 5, Loss: 0.0259\n",
+      "Accuracy: 0.7589\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.70      0.53      0.60       175\n",
+      "           1       0.78      0.88      0.83       331\n",
+      "\n",
+      "    accuracy                           0.76       506\n",
+      "   macro avg       0.74      0.70      0.71       506\n",
+      "weighted avg       0.75      0.76      0.75       506\n",
+      "\n",
+      "Epoch 6, Loss: 0.0182\n",
+      "Accuracy: 0.7609\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.73      0.49      0.59       175\n",
+      "           1       0.77      0.90      0.83       331\n",
+      "\n",
+      "    accuracy                           0.76       506\n",
+      "   macro avg       0.75      0.70      0.71       506\n",
+      "weighted avg       0.76      0.76      0.75       506\n",
+      "\n",
+      "Epoch 7, Loss: 0.0079\n",
+      "Accuracy: 0.7253\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.59      0.66      0.63       175\n",
+      "           1       0.81      0.76      0.78       331\n",
+      "\n",
+      "    accuracy                           0.73       506\n",
+      "   macro avg       0.70      0.71      0.70       506\n",
+      "weighted avg       0.73      0.73      0.73       506\n",
+      "\n",
+      "Epoch 8, Loss: 0.0061\n",
+      "Accuracy: 0.7372\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.71      0.40      0.51       175\n",
+      "           1       0.74      0.92      0.82       331\n",
+      "\n",
+      "    accuracy                           0.74       506\n",
+      "   macro avg       0.73      0.66      0.67       506\n",
+      "weighted avg       0.73      0.74      0.71       506\n",
+      "\n",
+      "Epoch 9, Loss: 0.0030\n",
+      "Accuracy: 0.7391\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.60      0.71      0.65       175\n",
+      "           1       0.83      0.76      0.79       331\n",
+      "\n",
+      "    accuracy                           0.74       506\n",
+      "   macro avg       0.72      0.73      0.72       506\n",
+      "weighted avg       0.75      0.74      0.74       506\n",
+      "\n",
+      "Epoch 10, Loss: 0.0059\n",
+      "Accuracy: 0.6917\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.54      0.75      0.63       175\n",
+      "           1       0.84      0.66      0.74       331\n",
+      "\n",
+      "    accuracy                           0.69       506\n",
+      "   macro avg       0.69      0.71      0.68       506\n",
+      "weighted avg       0.73      0.69      0.70       506\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report, accuracy_score\n",
+    "# from transformers import AdamW\n",
+    "\n",
+    "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
+    "\n",
+    "accumulation_steps = 6  \n",
+    "model.train()\n",
+    "for epoch in range(10):\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  \n",
+    "\n",
+    "        loss.backward()\n",
+    "        total_loss += loss.item()\n",
+    "\n",
+    "        if (step + 1) % accumulation_steps == 0:\n",
+    "            optimizer.step()\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "    if (step + 1) % accumulation_steps != 0:\n",
+    "        optimizer.step()\n",
+    "        optimizer.zero_grad()\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}\")\n",
+    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "    print(report)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "07ee95b3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-09-29T05:30:24.045329Z",
+     "iopub.status.busy": "2025-09-29T05:30:24.045071Z",
+     "iopub.status.idle": "2025-09-29T05:30:24.048378Z",
+     "shell.execute_reply": "2025-09-29T05:30:24.047714Z"
+    },
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+     "end_time": "2025-09-29T05:30:24.049538",
+     "exception": false,
+     "start_time": "2025-09-29T05:30:24.038239",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 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",
+    "# print(f\"Accuracy: {accuracy:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bdea7785",
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+     "iopub.status.busy": "2025-09-29T05:30:24.060484Z",
+     "iopub.status.idle": "2025-09-29T05:30:24.063360Z",
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+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import classification_report\n",
+    "\n",
+    "# report = classification_report(all_labels, all_preds)\n",
+    "# print(report)\n"
+   ]
+  },
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distortion-MRC-extraction.ipynb

@@ -0,0 +1 @@
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pd.read_csv('/kaggle/input/d-qa-v3/distortion_processed.csv')\n\ndf['Question']=\"What is the distorted part?\"\n\nimport re\n\ndef find_answer_start(context, answer):\n    match = re.search(re.escape(answer.strip()), context)\n    return match.start() if match else -1\n\n# Chuyển đổi DataFrame thành định dạng cho mô hình QA\nquestions = df[\"Question\"].tolist()\ncontexts = df[\"Patient Question\"].tolist()\nanswer_ = df[\"processed_substrings\"].tolist()\n\nanswers = []\nfor i, row in df.iterrows():\n    if pd.isna(row[\"processed_substrings\"]):  # Không có câu trả lời\n        answer = {\"text\": [\"\"], \"answer_start\": [-1]}  \n    else:\n        start_idx = find_answer_start(row[\"Patient Question\"], answer_[i])\n        answer = {\n            \"text\": [row[\"processed_substrings\"]],\n            \"answer_start\": [start_idx]\n        }\n    answers.append(answer)\n\n\n\n# Tạo dataset theo định dạng của Hugging Face\nqa_data = {\n    \"distortion\": df[\"Dominant Distortion\"].tolist(),\n    \"question\": questions,\n    \"context\": df[\"Patient Question\"].tolist(),\n    \"answers\": answers\n}\n\n#Chuyển sang Dataset của Hugging Face\nfrom datasets import Dataset\ndataset = Dataset.from_dict(qa_data)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T02:49:04.123373Z","iopub.execute_input":"2026-04-09T02:49:04.123801Z","iopub.status.idle":"2026-04-09T02:49:07.702176Z","shell.execute_reply.started":"2026-04-09T02:49:04.123770Z","shell.execute_reply":"2026-04-09T02:49:07.701266Z"}},"outputs":[],"execution_count":1},{"cell_type":"code","source":"# dataset = dataset.train_test_split(test_size=0.2, seed=42)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T02:49:07.703325Z","iopub.execute_input":"2026-04-09T02:49:07.703649Z","iopub.status.idle":"2026-04-09T02:49:07.707440Z","shell.execute_reply.started":"2026-04-09T02:49:07.703626Z","shell.execute_reply":"2026-04-09T02:49:07.706400Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"# dataset","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T02:49:07.709576Z","iopub.execute_input":"2026-04-09T02:49:07.709829Z","iopub.status.idle":"2026-04-09T02:49:07.735105Z","shell.execute_reply.started":"2026-04-09T02:49:07.709809Z","shell.execute_reply":"2026-04-09T02:49:07.734071Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"def predict_answer(question, context):\n    # Tokenize with overflow & offset mapping\n    inputs = tokenizer(\n        question,\n        context,\n        return_tensors=\"pt\",\n        max_length=512,\n        truncation=\"only_second\",\n        stride=256,\n        return_overflowing_tokens=True,\n        return_offsets_mapping=True,\n        padding=\"max_length\",\n    )\n\n    # Pop những thông tin không phải input cho model\n    offset_mapping = inputs.pop(\"offset_mapping\")\n    overflow_mapping = inputs.pop(\"overflow_to_sample_mapping\")\n\n    input_ids = inputs[\"input_ids\"]\n    # print(\"Số đoạn context:\", input_ids.shape[0])\n    inputs = {k: v.to(device) for k, v in inputs.items()}\n\n    with torch.no_grad():\n        outputs = model(**inputs)\n\n    start_logits = outputs.start_logits\n    end_logits = outputs.end_logits\n\n    best_score = float('-inf')\n    best_answer = \"\"\n\n    for i in range(len(start_logits)):\n        start_logit = start_logits[i]\n        end_logit = end_logits[i]\n        offsets = offset_mapping[i]\n\n        start_index = torch.argmax(start_logit).item()\n        end_index = torch.argmax(end_logit).item()\n\n        # Kiểm tra chỉ số có hợp lệ không\n        if (\n            start_index >= len(offsets)\n            or end_index >= len(offsets)\n            or offsets[start_index] is None\n            or offsets[end_index] is None\n        ):\n            continue\n\n        start_char = offsets[start_index][0]\n        end_char = offsets[end_index][1]\n\n        score = start_logit[start_index] + end_logit[end_index]\n        # print(score)\n        if score > best_score and start_char < end_char:\n            best_score = score\n            best_answer = context[start_char:end_char]\n\n    return best_answer","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T02:49:07.736360Z","iopub.execute_input":"2026-04-09T02:49:07.736718Z","iopub.status.idle":"2026-04-09T02:49:07.762185Z","shell.execute_reply.started":"2026-04-09T02:49:07.736682Z","shell.execute_reply":"2026-04-09T02:49:07.761018Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"from collections import Counter\nimport string\n\n# Hàm tính toán Exact Match và F1 (như đã trình bày trước đó)\ndef calculate_exact_match(predictions, ground_truths):\n    exact_match = 0\n    for pred, truth in zip(predictions, ground_truths):\n        if pred.strip().lower() == truth.strip().lower():\n            exact_match += 1\n    return exact_match / len(predictions)\n\ndef calculate_f1_score(predictions, ground_truths):\n    def f1(pred, truth):\n        pred_tokens = normalize_text(pred).split()\n        truth_tokens = normalize_text(truth).split()\n\n        # Trường hợp cả hai chuỗi đều rỗng\n        if not pred_tokens and not truth_tokens:\n            return 1.0  # Có thể cho là F1 score hoàn hảo trong trường hợp này\n\n        common_tokens = Counter(pred_tokens) & Counter(truth_tokens)\n        num_common = sum(common_tokens.values())\n\n        if num_common == 0:\n            return 0.0  # Trả về giá trị 0 nếu không có sự giao nhau\n\n        precision = num_common / len(pred_tokens) if len(pred_tokens) > 0 else 0\n        recall = num_common / len(truth_tokens) if len(truth_tokens) > 0 else 0\n\n        if precision + recall == 0:\n            return 0.0  # Tránh trường hợp chia cho 0\n\n        return 2 * (precision * recall) / (precision + recall)\n\n    f1_scores = [f1(pred, truth) for pred, truth in zip(predictions, ground_truths)]\n    return sum(f1_scores) / len(f1_scores)\n\n\ndef normalize_text(text):\n    return text.translate(str.maketrans(\"\", \"\", string.punctuation)).lower()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T02:49:07.762952Z","iopub.execute_input":"2026-04-09T02:49:07.763265Z","iopub.status.idle":"2026-04-09T02:49:07.821812Z","shell.execute_reply.started":"2026-04-09T02:49:07.763226Z","shell.execute_reply":"2026-04-09T02:49:07.820355Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"import torch\nimport numpy as np\nfrom datasets import Dataset\nfrom transformers import AutoTokenizer, AutoModelForQuestionAnswering\nfrom torch.utils.data import DataLoader\nfrom transformers import default_data_collator\nfrom tqdm.auto import tqdm\n\n# Tokenizer\nmodel_checkpoint = \"bert-base-cased\"\ntokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n\n# Preprocessing function\ndef preprocess_examples(examples):\n    questions = [q.strip() for q in examples[\"question\"]]\n    inputs = tokenizer(\n        questions,\n        examples[\"context\"],\n        max_length=512,\n        truncation=\"only_second\",\n        stride=256,\n        return_overflowing_tokens=True,\n        return_offsets_mapping=True,\n        padding=\"max_length\",\n    )\n    \n    offset_mapping = inputs.pop(\"offset_mapping\")\n    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n    answers = examples[\"answers\"]\n    start_positions, end_positions = [], []\n    \n    for i, offsets in enumerate(offset_mapping):\n        sample_idx = sample_map[i]\n        answer = answers[sample_idx]\n        if len(answer[\"text\"]) == 0:\n            start_positions.append(0)\n            end_positions.append(0)\n        else:\n            start_char = answer[\"answer_start\"][0]\n            end_char = start_char + len(answer[\"text\"][0])\n            sequence_ids = inputs.sequence_ids(i)\n            \n            idx = 0\n            while sequence_ids[idx] != 1:\n                idx += 1\n            context_start = idx\n            while idx < len(sequence_ids) and sequence_ids[idx] == 1:\n                idx += 1\n            context_end = idx - 1\n            \n            if offsets[context_start][0] > start_char or offsets[context_end][1] < end_char:\n                start_positions.append(0)\n                end_positions.append(0)\n            else:\n                idx = context_start\n                while idx <= context_end and offsets[idx][0] <= start_char:\n                    idx += 1\n                start_positions.append(idx - 1)\n                \n                idx = context_end\n                while idx >= context_start and offsets[idx][1] >= end_char:\n                    idx -= 1\n                end_positions.append(idx + 1)\n    \n    inputs[\"start_positions\"] = start_positions\n    inputs[\"end_positions\"] = end_positions\n    return inputs\n\n# Apply preprocessing\ndataset_processed = dataset.map(preprocess_examples, batched=True, remove_columns=dataset.column_names)\ndataset_splited = dataset_processed.train_test_split(test_size=0.2, seed=42)\n\ndataset.set_format(\"torch\")\ntrain_dataloader = DataLoader(dataset_splited['train'], batch_size=3, shuffle=True, collate_fn=default_data_collator)\ntest_dataloader = DataLoader(dataset_splited['test'], batch_size=3, shuffle=False, collate_fn=default_data_collator)\n\n# Model and optimizer\nmodel = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel.to(device)\noptimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)\n\n# Training loop\nnum_epochs = 20\nprogress_bar = tqdm(range(len(train_dataloader) * num_epochs))\n\ndataset = Dataset.from_dict(qa_data)\ndataset = dataset.train_test_split(test_size=0.2, seed=42)\n\nfor epoch in range(num_epochs):\n    model.train()\n    for batch in train_dataloader:\n        batch = {k: v.to(device) for k, v in batch.items()}\n        optimizer.zero_grad()\n        outputs = model(**batch)\n        loss = outputs.loss\n        loss.backward()\n        optimizer.step()\n        progress_bar.update(1)\n    predictions = []\n    references = []\n    model.eval()\n    # Lặp qua từng dòng trong tập test\n    for example in dataset['test']:\n        question = example[\"question\"]\n        context = example[\"context\"]\n        ground_truth = example[\"answers\"][\"text\"][0]\n    \n        # Dự đoán câu trả lời\n        result = predict_answer(question, context)\n    \n        # Thêm vào danh sách đánh giá\n        predictions.append(result)\n        references.append(ground_truth)\n    \n    \n    # Tính Exact Match và F1\n    em = calculate_exact_match(predictions, references)\n    f1 = calculate_f1_score(predictions, references)\n    \n    print(f\"Exact Match: {em * 100:.2f}%\")\n    print(f\"F1 Score: {f1:.4f}\")\n    print(f\"Epoch {epoch + 1} completed.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T02:49:08.670359Z","iopub.execute_input":"2026-04-09T02:49:08.670795Z","execution_failed":"2026-04-08T19:52:18.847Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4459cdddee0241a2b16fe972fa08c58e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3e9bcdf9144749fa8259f0eaad45fdad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"01fbb2df8b8a403dbfc30ffc2f5260f3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e857c1b04ebf46ceafb90f3190c7151d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Map:   0%|          | 0/2530 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4a73070a62df42788f1144353d05b292"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fc08de0a23244b0ca9f6e3b1cdc2a7c7"}},"metadata":{}},{"name":"stderr","text":"Some weights of BertForQuestionAnswering were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/13960 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"372a4f27c1104f01985e59672266f38d"}},"metadata":{}},{"name":"stdout","text":"Exact Match: 40.12%\nF1 Score: 0.4749\nEpoch 1 completed.\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# Save model\nmodel.save_pretrained(\"bert-finetuned-distortion\")\ntokenizer.save_pretrained(\"bert-finetuned-distortion\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T04:02:20.639851Z","iopub.status.idle":"2025-09-29T04:02:20.640119Z","shell.execute_reply":"2025-09-29T04:02:20.640009Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n# def predict_answer(question, context):\n#     inputs = tokenizer(question, context, return_tensors=\"pt\", truncation=True, max_length=384)\n#     inputs = {k: v.to(device) for k, v in inputs.items()}\n    \n#     with torch.no_grad():\n#         outputs = model(**inputs)\n    \n#     start_logits = outputs.start_logits\n#     end_logits = outputs.end_logits\n#     start_index = torch.argmax(start_logits, dim=-1).item()\n#     end_index = torch.argmax(end_logits, dim=-1).item()\n    \n#     all_tokens = tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"].squeeze())\n#     answer = tokenizer.convert_tokens_to_string(all_tokens[start_index:end_index+1])\n    \n#     return answer\n\n# def predict_answer(question, context):\n#     # Tokenize with offset mapping to align tokens with original text\n#     inputs = tokenizer(\n#         question,\n#         context,\n#         return_tensors=\"pt\",\n#         max_length=512,\n#         truncation=\"only_second\",\n#         stride=256,\n#         return_offsets_mapping=True,\n#         padding=\"max_length\",\n#     )\n\n#     offset_mapping = inputs.pop(\"offset_mapping\")\n#     input_ids = inputs[\"input_ids\"]\n#     inputs = {k: v.to(device) for k, v in inputs.items()}\n\n#     with torch.no_grad():\n#         outputs = model(**inputs)\n\n#     start_logits = outputs.start_logits\n#     end_logits = outputs.end_logits\n\n#     # Get the most probable start and end token positions\n#     start_index = torch.argmax(start_logits, dim=-1).item()\n#     end_index = torch.argmax(end_logits, dim=-1).item()\n\n#     # Extract character positions from offset mapping\n#     offsets = offset_mapping[0]\n#     start_char = offsets[start_index][0]\n#     end_char = offsets[end_index][1]\n\n#     # Extract the predicted answer from the original context\n#     answer = context[start_char:end_char]\n\n#     return answer\n\n\n\n# Example inference\n# for i in range(15):\n#     question = qa_data[\"question\"][i]\n#     context = qa_data[\"context\"][i]\n#     print(context)\n#     print(qa_data[\"answers\"][i])\n#     predicted_answer = predict_answer(question, context)\n#     print(f\"Question: {question}\")\n#     print(f\"Predicted Answer: {predicted_answer}\\n\")\n    ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-09T02:38:13.063799Z","iopub.execute_input":"2026-04-09T02:38:13.064239Z","iopub.status.idle":"2026-04-09T02:38:13.074009Z","shell.execute_reply.started":"2026-04-09T02:38:13.064205Z","shell.execute_reply":"2026-04-09T02:38:13.072689Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"# # !pip install evaluate\n# import evaluate\n\n# # Load metrics\n# squad_metric = evaluate.load(\"squad\")\n\n# # Chuẩn bị prediction và references\n# predictions = []\n# references = []\n\n# for i in range(len(qa_data[\"question\"])):\n#     question = qa_data[\"question\"][i]\n#     context = qa_data[\"context\"][i]\n#     ground_truth = qa_data[\"answers\"][i][\"text\"][0]\n\n#     result = predict_answer(question, context)\n\n#     # Thêm vào danh sách đánh giá\n#     predictions.append({\n#         \"id\": str(i),\n#         \"prediction_text\": result\n#     })\n#     references.append({\n#         \"id\": str(i),\n#         \"answers\": {\n#             \"answer_start\": [qa_data[\"answers\"][i][\"answer_start\"][0]],\n#             \"text\": [ground_truth]\n#         }\n#     })\n\n# # Tính EM và F1\n# results = squad_metric.compute(predictions=predictions, references=references)\n# print(f\"Exact Match: {results['exact_match']:.2f}\")\n# print(f\"F1 Score: {results['f1']:.2f}\")\nfrom collections import Counter\nimport string\n\n# Hàm tính toán Exact Match và F1 (như đã trình bày trước đó)\ndef calculate_exact_match(predictions, ground_truths):\n    exact_match = 0\n    for pred, truth in zip(predictions, ground_truths):\n        if pred.strip().lower() == truth.strip().lower():\n            exact_match += 1\n    return exact_match / len(predictions)\n\ndef calculate_f1_score(predictions, ground_truths):\n    def f1(pred, truth):\n        pred_tokens = normalize_text(pred).split()\n        truth_tokens = normalize_text(truth).split()\n\n        # Trường hợp cả hai chuỗi đều rỗng\n        if not pred_tokens and not truth_tokens:\n            return 1.0  # Có thể cho là F1 score hoàn hảo trong trường hợp này\n\n        common_tokens = Counter(pred_tokens) & Counter(truth_tokens)\n        num_common = sum(common_tokens.values())\n\n        if num_common == 0:\n            return 0.0  # Trả về giá trị 0 nếu không có sự giao nhau\n\n        precision = num_common / len(pred_tokens) if len(pred_tokens) > 0 else 0\n        recall = num_common / len(truth_tokens) if len(truth_tokens) > 0 else 0\n\n        if precision + recall == 0:\n            return 0.0  # Tránh trường hợp chia cho 0\n\n        return 2 * (precision * recall) / (precision + recall)\n\n    f1_scores = [f1(pred, truth) for pred, truth in zip(predictions, ground_truths)]\n    return sum(f1_scores) / len(f1_scores)\n\n\ndef normalize_text(text):\n    return text.translate(str.maketrans(\"\", \"\", string.punctuation)).lower()\n\n#Chuẩn bị dữ liệu từ qa_data\n# predictions = []\n# references = []\n\n# for i in range(len(qa_data[\"question\"])):\n#     question = qa_data[\"question\"][i]\n#     context = qa_data[\"context\"][i]\n#     ground_truth = qa_data[\"answers\"][i][\"text\"][0]\n\n#     # Dự đoán câu trả lời cho từng câu hỏi\n#     result = predict_answer(question, context)\n#     qa_data[\"predict\"][i]= result\n#     # Thêm vào danh sách đánh giá\n#     predictions.append(result)\n#     references.append(ground_truth)\n\n# predictions = []\n# references = []\n\n# # Lặp qua từng dòng trong tập test\n# for example in dataset['test']:\n#     question = example[\"question\"]\n#     context = example[\"context\"]\n#     ground_truth = example[\"answers\"][\"text\"][0]\n\n#     # Dự đoán câu trả lời\n#     result = predict_answer(question, context)\n\n#     # Thêm vào danh sách đánh giá\n#     predictions.append(result)\n#     references.append(ground_truth)\n\n\n# # Tính Exact Match và F1\n# em = calculate_exact_match(predictions, references)\n# f1 = calculate_f1_score(predictions, references)\n\n# print(f\"Exact Match: {em * 100:.2f}%\")\n# print(f\"F1 Score: {f1:.4f}\")\n\n# qa_data[\"predict\"] = []  # Tạo danh sách rỗng trước\n\n# for i in range(len(qa_data[\"question\"])):\n#     question = qa_data[\"question\"][i]\n#     context = qa_data[\"context\"][i]\n#     ground_truth = qa_data[\"answers\"][i][\"text\"][0]\n\n#     # Dự đoán câu trả lời cho từng câu hỏi\n#     result = predict_answer(question, context)\n\n#     qa_data[\"predict\"].append(result)  # Thêm kết quả dự đoán\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T04:49:23.854050Z","iopub.execute_input":"2025-09-29T04:49:23.854337Z","iopub.status.idle":"2025-09-29T04:49:23.862356Z","shell.execute_reply.started":"2025-09-29T04:49:23.854316Z","shell.execute_reply":"2025-09-29T04:49:23.861378Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"import pandas as pd\n\n# Chuyển dict thành DataFrame\ndf_qa = pd.DataFrame(qa_data)\n","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.991Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_qa.to_csv('distorted.csv', index = False)","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.991Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":" df['Distorted part']=df['Distorted part'].fillna('')","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.991Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"references","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.991Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"qa_data[\"context\"][91]","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.991Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df.head()","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df['Distorted part'][91]","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df['Longest Match'][91]","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"qa_data[\"answers\"][91]","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range(len(references)):\n    print(references[i])\n    print(predictions[i])","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"predictions[2]","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"references","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"predictions[0]","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"references[0]","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load pipeline\nquestion_answerer = pipeline(\"question-answering\", model=model, tokenizer=tokenizer)\n\n# Example inference\nfor i in range(len(qa_data[\"question\"])):\n    question = qa_data[\"question\"][i]\n    context = qa_data[\"context\"][i]\n    result = question_answerer(question=question, context=context)\n    print(\"Answer:\", result[\"answer\"])\n    print(\"Start index:\", result[\"start\"])\n    print(\"End index:\", result[\"end\"])\n    print(\"Confidence score:\", result[\"score\"])\n    print(f\"Question: {question}\")\n    print(f\"Predicted Answer: {result['answer']}\\n\")\n    break","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(\"I don’t really know how to explain the situation. \")","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.992Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"qa_data","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-19T01:36:39.993Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}

methods_output_compairision.ipynb

@@ -0,0 +1,2278 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8cf922fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings \n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c53d9c9",
+   "metadata": {},
+   "source": [
+    "# 2021 methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "043ac410",
+   "metadata": {},
+   "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",
+       "    </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_2021 = pd.read_csv('data/distortion.csv')\n",
+    "df_2021.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7dac60fb",
+   "metadata": {},
+   "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",
+       "    </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>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",
+       "  </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",
+       "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",
+       "                                       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",
+       "4   I refused to go because I didn’t know if it wa...     Fortune-telling   \n",
+       "9   I felt like the response was totally irrationa...       Magnification   \n",
+       "10  I thought that he displayed traits of honor, l...            Labeling   \n",
+       "\n",
+       "   Secondary Distortion (Optional)  \n",
+       "0                              NaN  \n",
+       "1              Emotional Reasoning  \n",
+       "4              Emotional Reasoning  \n",
+       "9                              NaN  \n",
+       "10                             NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2021 = df_2021[df_2021['Dominant Distortion'] != 'No Distortion']\n",
+    "df_2021.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c75ed93d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\Admin\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\Admin\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\Admin\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "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",
+    "\n",
+    "nltk.download(\"punkt\")\n",
+    "nltk.download(\"stopwords\")\n",
+    "nltk.download(\"wordnet\")\n",
+    "\n",
+    "stemmer = PorterStemmer()\n",
+    "lemmatizer = WordNetLemmatizer()\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",
+    "    text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
+    "    return text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c1943d1a",
+   "metadata": {},
+   "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>hello i have a beautifulsmartoutgoing and amaz...</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>since i was about 16 years old i’ve had these ...</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 don’t really know how to explain the situati...</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 have been with my fiancé for two years now ...</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>my husband and i have been married for over a ...</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",
+       "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",
+       "                                       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",
+       "4   I refused to go because I didn’t know if it wa...     Fortune-telling   \n",
+       "9   I felt like the response was totally irrationa...       Magnification   \n",
+       "10  I thought that he displayed traits of honor, l...            Labeling   \n",
+       "\n",
+       "   Secondary Distortion (Optional)  \\\n",
+       "0                              NaN   \n",
+       "1              Emotional Reasoning   \n",
+       "4              Emotional Reasoning   \n",
+       "9                              NaN   \n",
+       "10                             NaN   \n",
+       "\n",
+       "                                              cleaned  \n",
+       "0   hello i have a beautifulsmartoutgoing and amaz...  \n",
+       "1   since i was about 16 years old i’ve had these ...  \n",
+       "4   i don’t really know how to explain the situati...  \n",
+       "9    i have been with my fiancé for two years now ...  \n",
+       "10  my husband and i have been married for over a ...  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2021['cleaned'] = df_2021['Patient Question'].map(lambda text: preprocess_text(text))\n",
+    "df_2021.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0c116e42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df_2021['cleaned'].values,\n",
+    "                                                    df_2021['Dominant Distortion'].values,\n",
+    "                                                    test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c7bf8c80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "tfidf_vectorizer = TfidfVectorizer()\n",
+    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
+    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "59a4d305",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.svm import SVC\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5fdc025f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearSVC()"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearSVC()\n",
+    "model.fit(X_train_tfidf,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6bfc79e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, classification_report\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",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f213c25e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 24.6875, 'precision': 0.23199561875314, 'recall': 0.246875, 'f1': 0.23512261720447172}\n",
+      "                         precision    recall  f1-score   support\n",
+      "\n",
+      "All-or-nothing thinking       0.00      0.00      0.00        15\n",
+      "    Emotional Reasoning       0.27      0.20      0.23        30\n",
+      "        Fortune-telling       0.31      0.33      0.32        24\n",
+      "               Labeling       0.14      0.10      0.12        40\n",
+      "          Magnification       0.18      0.16      0.17        37\n",
+      "          Mental filter       0.15      0.23      0.18        13\n",
+      "           Mind Reading       0.39      0.52      0.45        54\n",
+      "     Overgeneralization       0.25      0.32      0.28        50\n",
+      "        Personalization       0.25      0.18      0.21        39\n",
+      "      Should statements       0.06      0.06      0.06        18\n",
+      "\n",
+      "               accuracy                           0.25       320\n",
+      "              macro avg       0.20      0.21      0.20       320\n",
+      "           weighted avg       0.23      0.25      0.24       320\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test_tfidf)\n",
+    "print(calculate_results(y_test, y_pred))\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7664dae2",
+   "metadata": {},
+   "source": [
+    "# Our methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13ccfe1c",
+   "metadata": {},
+   "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",
+       "    </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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_our = pd.read_csv('data/distortion.csv')\n",
+    "df_our.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6c37e38a",
+   "metadata": {},
+   "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",
+       "    </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>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",
+       "  </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",
+       "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",
+       "                                       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",
+       "4   I refused to go because I didn’t know if it wa...     Fortune-telling   \n",
+       "9   I felt like the response was totally irrationa...       Magnification   \n",
+       "10  I thought that he displayed traits of honor, l...            Labeling   \n",
+       "\n",
+       "   Secondary Distortion (Optional)  \n",
+       "0                              NaN  \n",
+       "1              Emotional Reasoning  \n",
+       "4              Emotional Reasoning  \n",
+       "9                              NaN  \n",
+       "10                             NaN  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_our = df_our[df_our['Dominant Distortion'] != 'No Distortion']\n",
+    "df_our.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1503580c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_our = df_our.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "597bb4ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_our['cleaned'] = df_our['Distorted part'].map(lambda text: preprocess_text(text))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "626adab6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_our['cleaned_full'] = df_our['Patient Question'].map(lambda text: preprocess_text(text))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b573c357",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train_full, test_full, _, _ = train_test_split(df_our['cleaned_full'].values,\n",
+    "                                                    df_our['Dominant Distortion'].values,\n",
+    "                                                    test_size=0.2, random_state=42)\n",
+    "\n",
+    "X_train_our, X_test_our, y_train_our, y_test_our = train_test_split(df_our['cleaned'].values,\n",
+    "                                                    df_our['Dominant Distortion'].values,\n",
+    "                                                    test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2a3b3c97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "tfidf_vectorizer_our = TfidfVectorizer()\n",
+    "X_train_tfidf_our = tfidf_vectorizer_our.fit_transform(X_train_our)\n",
+    "X_test_tfidf_our = tfidf_vectorizer_our.transform(X_test_our)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "894cf240",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearSVC()"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearSVC()\n",
+    "model.fit(X_train_tfidf_our,y_train_our)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "c52c869e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 34.6875, 'precision': 0.36036002269633405, 'recall': 0.346875, 'f1': 0.34796169864321225}\n",
+      "                         precision    recall  f1-score   support\n",
+      "\n",
+      "All-or-nothing thinking       0.15      0.13      0.14        15\n",
+      "    Emotional Reasoning       0.35      0.20      0.26        30\n",
+      "        Fortune-telling       0.54      0.58      0.56        24\n",
+      "               Labeling       0.44      0.35      0.39        40\n",
+      "          Magnification       0.30      0.24      0.27        37\n",
+      "          Mental filter       0.06      0.15      0.09        13\n",
+      "           Mind Reading       0.48      0.57      0.52        54\n",
+      "     Overgeneralization       0.29      0.34      0.31        50\n",
+      "        Personalization       0.33      0.28      0.31        39\n",
+      "      Should statements       0.38      0.28      0.32        18\n",
+      "\n",
+      "               accuracy                           0.35       320\n",
+      "              macro avg       0.33      0.31      0.32       320\n",
+      "           weighted avg       0.36      0.35      0.35       320\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred_our = model.predict(X_test_tfidf_our)\n",
+    "print(calculate_results(y_test_our, y_pred_our))\n",
+    "print(classification_report(y_test_our, y_pred_our))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2f56b5fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_our = pd.DataFrame({\n",
+    "    \"text\": X_test_our,\n",
+    "    \"full\": test_full,\n",
+    "    \"gold_label\": y_test_our,\n",
+    "    \"predicted_label\": y_pred_our\n",
+    "})\n",
+    "# df.to_csv(\"predictions.csv\", index=False, encoding='utf-8-sig')\n",
+    "\n",
+    "# print(\"Đã lưu file predictions.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "79f9a8e6",
+   "metadata": {},
+   "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>text</th>\n",
+       "      <th>full</th>\n",
+       "      <th>gold_label</th>\n",
+       "      <th>predicted_label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>i had an extreme hate towards myself sabotagin...</td>\n",
+       "      <td>from lebanon it has been a long period that i’...</td>\n",
+       "      <td>Mental filter</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>my husband was acting like he was having an af...</td>\n",
+       "      <td>last year my husband said he knew this girl wh...</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "      <td>Labeling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>i feel as if moving on to higher education wil...</td>\n",
+       "      <td>i find myself increasingly disillusioned with ...</td>\n",
+       "      <td>Fortune-telling</td>\n",
+       "      <td>Fortune-telling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>i’m really struggling with understanding how i...</td>\n",
+       "      <td>my husband left almost 3 years ago and lives w...</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>i’m 17 now and should be off to university nex...</td>\n",
+       "      <td>i’m 17 now and should be off to university nex...</td>\n",
+       "      <td>Should statements</td>\n",
+       "      <td>Magnification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>another thing is that my mother loves lecturin...</td>\n",
+       "      <td>my mother and father treat me quite differentl...</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>part of me thinks this is a routine and just d...</td>\n",
+       "      <td>my boyfriend of 2 years is a wonderful person ...</td>\n",
+       "      <td>Personalization</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>all my life i’ve tried to be different people ...</td>\n",
+       "      <td>from a 14 year old girl in the us all my life ...</td>\n",
+       "      <td>Personalization</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>she knows she has memory problems and is not i...</td>\n",
+       "      <td>my wife has been seeing the same psychiatrist ...</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>i just feel like i’ve never been happy</td>\n",
+       "      <td>is lifetime depression a thing is there some o...</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>320 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  text  \\\n",
+       "0    i had an extreme hate towards myself sabotagin...   \n",
+       "1    my husband was acting like he was having an af...   \n",
+       "2    i feel as if moving on to higher education wil...   \n",
+       "3    i’m really struggling with understanding how i...   \n",
+       "4    i’m 17 now and should be off to university nex...   \n",
+       "..                                                 ...   \n",
+       "315  another thing is that my mother loves lecturin...   \n",
+       "316  part of me thinks this is a routine and just d...   \n",
+       "317  all my life i’ve tried to be different people ...   \n",
+       "318  she knows she has memory problems and is not i...   \n",
+       "319             i just feel like i’ve never been happy   \n",
+       "\n",
+       "                                                  full           gold_label  \\\n",
+       "0    from lebanon it has been a long period that i’...        Mental filter   \n",
+       "1    last year my husband said he knew this girl wh...  Emotional Reasoning   \n",
+       "2    i find myself increasingly disillusioned with ...      Fortune-telling   \n",
+       "3    my husband left almost 3 years ago and lives w...         Mind Reading   \n",
+       "4    i’m 17 now and should be off to university nex...    Should statements   \n",
+       "..                                                 ...                  ...   \n",
+       "315  my mother and father treat me quite differentl...         Mind Reading   \n",
+       "316  my boyfriend of 2 years is a wonderful person ...      Personalization   \n",
+       "317  from a 14 year old girl in the us all my life ...      Personalization   \n",
+       "318  my wife has been seeing the same psychiatrist ...         Mind Reading   \n",
+       "319  is lifetime depression a thing is there some o...   Overgeneralization   \n",
+       "\n",
+       "         predicted_label  \n",
+       "0     Overgeneralization  \n",
+       "1               Labeling  \n",
+       "2        Fortune-telling  \n",
+       "3           Mind Reading  \n",
+       "4          Magnification  \n",
+       "..                   ...  \n",
+       "315         Mind Reading  \n",
+       "316         Mind Reading  \n",
+       "317   Overgeneralization  \n",
+       "318         Mind Reading  \n",
+       "319  Emotional Reasoning  \n",
+       "\n",
+       "[320 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_our"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "666e3ea7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = pd.DataFrame({\n",
+    "    \"text\": X_test,\n",
+    "    \"gold_label\": y_test,\n",
+    "    \"predicted_label\": y_pred\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "70529b00",
+   "metadata": {},
+   "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>text</th>\n",
+       "      <th>gold_label</th>\n",
+       "      <th>predicted_label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>from lebanon it has been a long period that i’...</td>\n",
+       "      <td>Mental filter</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>last year my husband said he knew this girl wh...</td>\n",
+       "      <td>Emotional Reasoning</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>i find myself increasingly disillusioned with ...</td>\n",
+       "      <td>Fortune-telling</td>\n",
+       "      <td>Fortune-telling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>my husband left almost 3 years ago and lives w...</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>i’m 17 now and should be off to university nex...</td>\n",
+       "      <td>Should statements</td>\n",
+       "      <td>Mental filter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>my mother and father treat me quite differentl...</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>my boyfriend of 2 years is a wonderful person ...</td>\n",
+       "      <td>Personalization</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>from a 14 year old girl in the us all my life ...</td>\n",
+       "      <td>Personalization</td>\n",
+       "      <td>Magnification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>my wife has been seeing the same psychiatrist ...</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "      <td>Mind Reading</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>is lifetime depression a thing is there some o...</td>\n",
+       "      <td>Overgeneralization</td>\n",
+       "      <td>Should statements</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>320 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  text           gold_label  \\\n",
+       "0    from lebanon it has been a long period that i’...        Mental filter   \n",
+       "1    last year my husband said he knew this girl wh...  Emotional Reasoning   \n",
+       "2    i find myself increasingly disillusioned with ...      Fortune-telling   \n",
+       "3    my husband left almost 3 years ago and lives w...         Mind Reading   \n",
+       "4    i’m 17 now and should be off to university nex...    Should statements   \n",
+       "..                                                 ...                  ...   \n",
+       "315  my mother and father treat me quite differentl...         Mind Reading   \n",
+       "316  my boyfriend of 2 years is a wonderful person ...      Personalization   \n",
+       "317  from a 14 year old girl in the us all my life ...      Personalization   \n",
+       "318  my wife has been seeing the same psychiatrist ...         Mind Reading   \n",
+       "319  is lifetime depression a thing is there some o...   Overgeneralization   \n",
+       "\n",
+       "        predicted_label  \n",
+       "0    Overgeneralization  \n",
+       "1    Overgeneralization  \n",
+       "2       Fortune-telling  \n",
+       "3          Mind Reading  \n",
+       "4         Mental filter  \n",
+       "..                  ...  \n",
+       "315        Mind Reading  \n",
+       "316        Mind Reading  \n",
+       "317       Magnification  \n",
+       "318        Mind Reading  \n",
+       "319   Should statements  \n",
+       "\n",
+       "[320 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ce61b9cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "common_texts_df1 = results[results['text'].isin(results_our['full'])]\n",
+    "\n",
+    "common_texts_df2 = results_our[results_our['full'].isin(results['text'])]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2e2fbb23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "since may i haven’t been sleeping great i had a roommate before i came home and as soon as i slept in my own room i’ve been freaking out at night i see the light flickering by my window someone’s there i hear something under my desk someone’s there every little thing is making me terrified and unless i take benedryl i physically can’t sleep because i’m so afraid since i haven’t been sleeping great i’ve also started having minor hallucinations occasionally mostly auditory the microwave running and dishes breaking but i keep feeling like there’s bugs on me whenever i’m really paranoid how do i cope with thus fear\n",
+      "Magnification\n",
+      "Overgeneralization\n"
+     ]
+    }
+   ],
+   "source": [
+    "i=14\n",
+    "print(common_texts_df1['text'][i])\n",
+    "print(common_texts_df1['gold_label'][i])\n",
+    "print(common_texts_df1['predicted_label'][i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0dded9a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "since may i haven’t been sleeping great i had a roommate before i came home and as soon as i slept in my own room i’ve been freaking out at night i see the light flickering by my window someone’s there i hear something under my desk someone’s there every little thing is making me terrified and unless i take benedryl i physically can’t sleep because i’m so afraid since i haven’t been sleeping great i’ve also started having minor hallucinations occasionally mostly auditory the microwave running and dishes breaking but i keep feeling like there’s bugs on me whenever i’m really paranoid how do i cope with thus fear\n",
+      "i see the light flickering by my window someone’s there i hear something under my desk someone’s there every little thing is making me terrified and unless i take benedryl i physically can’t sleep because i’m so afraid\n",
+      "Magnification\n",
+      "Personalization\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(common_texts_df2['full'][i])\n",
+    "print(common_texts_df2['text'][i])\n",
+    "print(common_texts_df2['gold_label'][i])\n",
+    "print(common_texts_df2['predicted_label'][i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "453506b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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",
+       "      <th>cleaned_full</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2468</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>I can’t get close to anyone, I can’t feel, I c...</td>\n",
+       "      <td>I’m not shy at all, I’m a very confident perso...</td>\n",
+       "      <td>Labeling</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>i’m not shy at all i’m a very confident person...</td>\n",
+       "      <td>i can’t get close to anyone i can’t feel i can...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Id_Number                                   Patient Question  \\\n",
+       "2468       2500  I can’t get close to anyone, I can’t feel, I c...   \n",
+       "\n",
+       "                                         Distorted part Dominant Distortion  \\\n",
+       "2468  I’m not shy at all, I’m a very confident perso...            Labeling   \n",
+       "\n",
+       "     Secondary Distortion (Optional)  \\\n",
+       "2468                             NaN   \n",
+       "\n",
+       "                                                cleaned  \\\n",
+       "2468  i’m not shy at all i’m a very confident person...   \n",
+       "\n",
+       "                                           cleaned_full  \n",
+       "2468  i can’t get close to anyone i can’t feel i can...  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example = df_our[df_our['Id_Number'] == 2500]\n",
+    "example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "3cb9c242",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I can’t get close to anyone, I can’t feel, I can’t make friends or i just don’t want to, i feel the need to be alone. I’m not shy at all, I’m a very confident person I can talk to anyone in fact I don’t even hesitate to talk with strangers at all. and yes I’m a good person at heart i’m not selfish yet I can’t get close to anyone sometimes I feel like there’s an invisible wall around me that I can’t let anyone close. mostly people like me they are inspired by me they want to get close but each time I just push them away even sometimes when I really like them, but I don’t feel. What’s wrong with me?\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(example['Patient Question'][2468])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "879feb79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I’m not shy at all, I’m a very confident person I can talk to anyone in fact I don’t even hesitate to talk with strangers at all.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(example['Distorted part'][2468])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "962aaac3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}