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code.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c2ed359a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2d441603",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>textID</th>\n",
+       "      <th>text</th>\n",
+       "      <th>selected_text</th>\n",
+       "      <th>sentiment</th>\n",
+       "      <th>Time of Tweet</th>\n",
+       "      <th>Age of User</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Population -2020</th>\n",
+       "      <th>Land Area (Km²)</th>\n",
+       "      <th>Density (P/Km²)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cb774db0d1</td>\n",
+       "      <td>I`d have responded, if I were going</td>\n",
+       "      <td>I`d have responded, if I were going</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>morning</td>\n",
+       "      <td>0-20</td>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>38928346</td>\n",
+       "      <td>652860.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>549e992a42</td>\n",
+       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
+       "      <td>Sooo SAD</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>noon</td>\n",
+       "      <td>21-30</td>\n",
+       "      <td>Albania</td>\n",
+       "      <td>2877797</td>\n",
+       "      <td>27400.0</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>088c60f138</td>\n",
+       "      <td>my boss is bullying me...</td>\n",
+       "      <td>bullying me</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>night</td>\n",
+       "      <td>31-45</td>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>43851044</td>\n",
+       "      <td>2381740.0</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9642c003ef</td>\n",
+       "      <td>what interview! leave me alone</td>\n",
+       "      <td>leave me alone</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>morning</td>\n",
+       "      <td>46-60</td>\n",
+       "      <td>Andorra</td>\n",
+       "      <td>77265</td>\n",
+       "      <td>470.0</td>\n",
+       "      <td>164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>358bd9e861</td>\n",
+       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
+       "      <td>Sons of ****,</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>noon</td>\n",
+       "      <td>60-70</td>\n",
+       "      <td>Angola</td>\n",
+       "      <td>32866272</td>\n",
+       "      <td>1246700.0</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27476</th>\n",
+       "      <td>4eac33d1c0</td>\n",
+       "      <td>wish we could come see u on Denver  husband l...</td>\n",
+       "      <td>d lost</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>night</td>\n",
+       "      <td>31-45</td>\n",
+       "      <td>Ghana</td>\n",
+       "      <td>31072940</td>\n",
+       "      <td>227540.0</td>\n",
+       "      <td>137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27477</th>\n",
+       "      <td>4f4c4fc327</td>\n",
+       "      <td>I`ve wondered about rake to.  The client has ...</td>\n",
+       "      <td>, don`t force</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>morning</td>\n",
+       "      <td>46-60</td>\n",
+       "      <td>Greece</td>\n",
+       "      <td>10423054</td>\n",
+       "      <td>128900.0</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27478</th>\n",
+       "      <td>f67aae2310</td>\n",
+       "      <td>Yay good for both of you. Enjoy the break - y...</td>\n",
+       "      <td>Yay good for both of you.</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>noon</td>\n",
+       "      <td>60-70</td>\n",
+       "      <td>Grenada</td>\n",
+       "      <td>112523</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27479</th>\n",
+       "      <td>ed167662a5</td>\n",
+       "      <td>But it was worth it  ****.</td>\n",
+       "      <td>But it was worth it  ****.</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>night</td>\n",
+       "      <td>70-100</td>\n",
+       "      <td>Guatemala</td>\n",
+       "      <td>17915568</td>\n",
+       "      <td>107160.0</td>\n",
+       "      <td>167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27480</th>\n",
+       "      <td>6f7127d9d7</td>\n",
+       "      <td>All this flirting going on - The ATG smiles...</td>\n",
+       "      <td>All this flirting going on - The ATG smiles. Y...</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>morning</td>\n",
+       "      <td>0-20</td>\n",
+       "      <td>Guinea</td>\n",
+       "      <td>13132795</td>\n",
+       "      <td>246000.0</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>27481 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           textID                                               text  \\\n",
+       "0      cb774db0d1                I`d have responded, if I were going   \n",
+       "1      549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
+       "2      088c60f138                          my boss is bullying me...   \n",
+       "3      9642c003ef                     what interview! leave me alone   \n",
+       "4      358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
+       "...           ...                                                ...   \n",
+       "27476  4eac33d1c0   wish we could come see u on Denver  husband l...   \n",
+       "27477  4f4c4fc327   I`ve wondered about rake to.  The client has ...   \n",
+       "27478  f67aae2310   Yay good for both of you. Enjoy the break - y...   \n",
+       "27479  ed167662a5                         But it was worth it  ****.   \n",
+       "27480  6f7127d9d7     All this flirting going on - The ATG smiles...   \n",
+       "\n",
+       "                                           selected_text sentiment  \\\n",
+       "0                    I`d have responded, if I were going   neutral   \n",
+       "1                                               Sooo SAD  negative   \n",
+       "2                                            bullying me  negative   \n",
+       "3                                         leave me alone  negative   \n",
+       "4                                          Sons of ****,  negative   \n",
+       "...                                                  ...       ...   \n",
+       "27476                                             d lost  negative   \n",
+       "27477                                      , don`t force  negative   \n",
+       "27478                          Yay good for both of you.  positive   \n",
+       "27479                         But it was worth it  ****.  positive   \n",
+       "27480  All this flirting going on - The ATG smiles. Y...   neutral   \n",
+       "\n",
+       "      Time of Tweet Age of User      Country  Population -2020  \\\n",
+       "0           morning        0-20  Afghanistan          38928346   \n",
+       "1              noon       21-30      Albania           2877797   \n",
+       "2             night       31-45      Algeria          43851044   \n",
+       "3           morning       46-60      Andorra             77265   \n",
+       "4              noon       60-70       Angola          32866272   \n",
+       "...             ...         ...          ...               ...   \n",
+       "27476         night       31-45        Ghana          31072940   \n",
+       "27477       morning       46-60       Greece          10423054   \n",
+       "27478          noon       60-70      Grenada            112523   \n",
+       "27479         night      70-100    Guatemala          17915568   \n",
+       "27480       morning        0-20       Guinea          13132795   \n",
+       "\n",
+       "       Land Area (Km²)  Density (P/Km²)  \n",
+       "0             652860.0               60  \n",
+       "1              27400.0              105  \n",
+       "2            2381740.0               18  \n",
+       "3                470.0              164  \n",
+       "4            1246700.0               26  \n",
+       "...                ...              ...  \n",
+       "27476         227540.0              137  \n",
+       "27477         128900.0               81  \n",
+       "27478            340.0              331  \n",
+       "27479         107160.0              167  \n",
+       "27480         246000.0               53  \n",
+       "\n",
+       "[27481 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pd.read_csv('train.csv',encoding='unicode_escape')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "60b7c4de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\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>text</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>I`d have responded, if I were going</td>\n",
+       "      <td>neutral</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>my boss is bullying me...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>what interview! leave me alone</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27476</th>\n",
+       "      <td>wish we could come see u on Denver  husband l...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27477</th>\n",
+       "      <td>I`ve wondered about rake to.  The client has ...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27478</th>\n",
+       "      <td>Yay good for both of you. Enjoy the break - y...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27479</th>\n",
+       "      <td>But it was worth it  ****.</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27480</th>\n",
+       "      <td>All this flirting going on - The ATG smiles...</td>\n",
+       "      <td>neutral</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>27481 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    text sentiment\n",
+       "0                    I`d have responded, if I were going   neutral\n",
+       "1          Sooo SAD I will miss you here in San Diego!!!  negative\n",
+       "2                              my boss is bullying me...  negative\n",
+       "3                         what interview! leave me alone  negative\n",
+       "4       Sons of ****, why couldn`t they put them on t...  negative\n",
+       "...                                                  ...       ...\n",
+       "27476   wish we could come see u on Denver  husband l...  negative\n",
+       "27477   I`ve wondered about rake to.  The client has ...  negative\n",
+       "27478   Yay good for both of you. Enjoy the break - y...  positive\n",
+       "27479                         But it was worth it  ****.  positive\n",
+       "27480     All this flirting going on - The ATG smiles...   neutral\n",
+       "\n",
+       "[27481 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(df.columns[[0,2,4,5,6,7,8,9]], axis=1, inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "296d66e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels=df.sentiment.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "99085d3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label_dict={}\n",
+    "for id,label in enumerate(labels):\n",
+    "    label_dict[label]=id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fa1e4160",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'neutral': 0, 'negative': 1, 'positive': 2}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "eaa2c872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['label']=df.sentiment.replace(label_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "63ed05e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\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>sentiment</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>I`d have responded, if I were going</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>my boss is bullying me...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>what interview! leave me alone</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27476</th>\n",
+       "      <td>wish we could come see u on Denver  husband l...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27477</th>\n",
+       "      <td>I`ve wondered about rake to.  The client has ...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27478</th>\n",
+       "      <td>Yay good for both of you. Enjoy the break - y...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27479</th>\n",
+       "      <td>But it was worth it  ****.</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27480</th>\n",
+       "      <td>All this flirting going on - The ATG smiles...</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>27481 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    text sentiment  label\n",
+       "0                    I`d have responded, if I were going   neutral      0\n",
+       "1          Sooo SAD I will miss you here in San Diego!!!  negative      1\n",
+       "2                              my boss is bullying me...  negative      1\n",
+       "3                         what interview! leave me alone  negative      1\n",
+       "4       Sons of ****, why couldn`t they put them on t...  negative      1\n",
+       "...                                                  ...       ...    ...\n",
+       "27476   wish we could come see u on Denver  husband l...  negative      1\n",
+       "27477   I`ve wondered about rake to.  The client has ...  negative      1\n",
+       "27478   Yay good for both of you. Enjoy the break - y...  positive      2\n",
+       "27479                         But it was worth it  ****.  positive      2\n",
+       "27480     All this flirting going on - The ATG smiles...   neutral      0\n",
+       "\n",
+       "[27481 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b1dc846d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train,X_val,y_train,y_val=train_test_split(df.index.values,df['label'].values,train_size=0.8,random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "418fb78e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['data_type'] = 'not_set'\n",
+    "df.loc[X_train, 'data_type'] = 'train'\n",
+    "df.loc[X_val, 'data_type'] = 'val'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ceed0315",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['text']=df['text'].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d3c74651",
+   "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>sentiment</th>\n",
+       "      <th>label</th>\n",
+       "      <th>data_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>I`d have responded, if I were going</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>my boss is bullying me...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>what interview! leave me alone</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "      <td>val</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>27476</th>\n",
+       "      <td>wish we could come see u on Denver  husband l...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27477</th>\n",
+       "      <td>I`ve wondered about rake to.  The client has ...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>1</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27478</th>\n",
+       "      <td>Yay good for both of you. Enjoy the break - y...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>2</td>\n",
+       "      <td>val</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27479</th>\n",
+       "      <td>But it was worth it  ****.</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>2</td>\n",
+       "      <td>val</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27480</th>\n",
+       "      <td>All this flirting going on - The ATG smiles...</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0</td>\n",
+       "      <td>val</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>27481 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    text sentiment  label  \\\n",
+       "0                    I`d have responded, if I were going   neutral      0   \n",
+       "1          Sooo SAD I will miss you here in San Diego!!!  negative      1   \n",
+       "2                              my boss is bullying me...  negative      1   \n",
+       "3                         what interview! leave me alone  negative      1   \n",
+       "4       Sons of ****, why couldn`t they put them on t...  negative      1   \n",
+       "...                                                  ...       ...    ...   \n",
+       "27476   wish we could come see u on Denver  husband l...  negative      1   \n",
+       "27477   I`ve wondered about rake to.  The client has ...  negative      1   \n",
+       "27478   Yay good for both of you. Enjoy the break - y...  positive      2   \n",
+       "27479                         But it was worth it  ****.  positive      2   \n",
+       "27480     All this flirting going on - The ATG smiles...   neutral      0   \n",
+       "\n",
+       "      data_type  \n",
+       "0         train  \n",
+       "1         train  \n",
+       "2         train  \n",
+       "3         train  \n",
+       "4           val  \n",
+       "...         ...  \n",
+       "27476     train  \n",
+       "27477     train  \n",
+       "27478       val  \n",
+       "27479       val  \n",
+       "27480       val  \n",
+       "\n",
+       "[27481 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b018cca8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([' I`d have responded, if I were going',\n",
+       "       ' Sooo SAD I will miss you here in San Diego!!!',\n",
+       "       'my boss is bullying me...', ...,\n",
+       "       'So I get up early and I feel good about the day. I walk to work and I`m feeling alright. But guess what... I don`t work today.',\n",
+       "       ' wish we could come see u on Denver  husband lost his job and can`t afford it',\n",
+       "       ' I`ve wondered about rake to.  The client has made it clear .NET only, don`t force devs to learn a new lang  #agile #ccnet'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.data_type=='train'].text.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0d03c58e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\KARAN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import BertTokenizer\n",
+    "from torch.utils.data import TensorDataset\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1fc7bfd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', \n",
+    "                                          do_lower_case=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ea3521d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_data_train = tokenizer.batch_encode_plus(\n",
+    "    df[df.data_type=='train'].text.values.tolist(), \n",
+    "    add_special_tokens=True, \n",
+    "    return_attention_mask=True, \n",
+    "    max_length=256,\n",
+    "    padding='max_length',\n",
+    "    truncation=True,\n",
+    "    return_tensors='pt',\n",
+    ")\n",
+    "\n",
+    "encoded_data_val = tokenizer.batch_encode_plus(\n",
+    "    df[df.data_type=='val'].text.values.tolist(), \n",
+    "    add_special_tokens=True, \n",
+    "    return_attention_mask=True, \n",
+    "    max_length=256,\n",
+    "    truncation=True,\n",
+    "    padding='max_length', \n",
+    "    return_tensors='pt'\n",
+    ")\n",
+    "\n",
+    "\n",
+    "input_ids_train = encoded_data_train['input_ids']\n",
+    "attention_masks_train = encoded_data_train['attention_mask']\n",
+    "labels_train = torch.tensor(df[df.data_type=='train'].label.values)\n",
+    "\n",
+    "input_ids_val = encoded_data_val['input_ids']\n",
+    "attention_masks_val = encoded_data_val['attention_mask']\n",
+    "labels_val = torch.tensor(df[df.data_type=='val'].label.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d56c3636",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data=TensorDataset(input_ids_train,attention_masks_train,labels_train)\n",
+    "val_data=TensorDataset(input_ids_val,attention_masks_val,labels_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c1e6192b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import BertForSequenceClassification\n",
+    "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n",
+    "                                                      num_labels=len(label_dict),\n",
+    "                                                      output_attentions=False,\n",
+    "                                                      output_hidden_states=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "18b4fca0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader,RandomSampler,SequentialSampler\n",
+    "train_loader=DataLoader(\n",
+    "    train_data,\n",
+    "    sampler=RandomSampler(train_data),\n",
+    "    batch_size=4\n",
+    ")\n",
+    "\n",
+    "val_loader=DataLoader(\n",
+    "    val_data,\n",
+    "    sampler=SequentialSampler(val_data),\n",
+    "    batch_size=4\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b3f37358",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import get_linear_schedule_with_warmup\n",
+    "from torch.optim import AdamW\n",
+    "\n",
+    "optimizer=AdamW(\n",
+    "    model.parameters(),\n",
+    "    lr=1e-5,\n",
+    "    eps=1e-8\n",
+    ")\n",
+    "\n",
+    "epochs=5\n",
+    "\n",
+    "scheduler=get_linear_schedule_with_warmup(\n",
+    "    optimizer,\n",
+    "    num_warmup_steps=0,\n",
+    "    num_training_steps=len(train_data)*epochs\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a5ccc6d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device('cuda')\n",
+    "model.to(device)\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1ad2f635",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def eval(val_loader,model):\n",
+    "    model.eval()\n",
+    "    loss_val_total=0\n",
+    "    preds,true=[],[]\n",
+    "    \n",
+    "    for batch in val_loader:\n",
+    "        batch=tuple(b.to(device) for b in batch)\n",
+    "        inputs = {'input_ids':      batch[0],\n",
+    "                  'attention_mask': batch[1],\n",
+    "                  'labels':         batch[2],\n",
+    "                 }\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            outputs=model(**inputs)\n",
+    "            \n",
+    "        loss=outputs[0]\n",
+    "        logits=outputs[1]\n",
+    "        loss_val_total+=loss.item()\n",
+    "        logits=logits.detach().cpu().numpy()\n",
+    "        labels=inputs['labels'].cpu().numpy()\n",
+    "        preds.append(logits)\n",
+    "        true.append(labels)\n",
+    "        \n",
+    "    loss_val_avg=loss_val_total/len(val_loader)\n",
+    "    predictions=np.concatenate(preds,axis=0)\n",
+    "    true_vals=np.concatenate(true,axis=0)\n",
+    "    \n",
+    "    return loss_val_avg,predictions,true_vals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "05f1146d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                 \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 1\n",
+      "Training loss: 1.1087568206265244\n",
+      "Validation loss: 1.1073771828738126\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                 \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 2\n",
+      "Training loss: 1.1035335373561803\n",
+      "Validation loss: 1.0943231875246222\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                 \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 3\n",
+      "Training loss: 1.0946122174852106\n",
+      "Validation loss: 1.0898548677617854\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                 \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 4\n",
+      "Training loss: 1.0907055499164993\n",
+      "Validation loss: 1.0901057242480192\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                 \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 5\n",
+      "Training loss: 1.0898382831825786\n",
+      "Validation loss: 1.0943078843030063\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "for epoch in range(1,epochs+1):\n",
+    "    model.train()\n",
+    "    loss_train_total=0\n",
+    "    progress_bar = tqdm(train_loader,desc='Epoch {:1d}'.format(epoch), leave=False, disable=False)\n",
+    "    for batch in progress_bar:\n",
+    "\n",
+    "        model.zero_grad()\n",
+    "        \n",
+    "        batch = tuple(b.to(device) for b in batch)\n",
+    "        \n",
+    "        inputs = {'input_ids':      batch[0],\n",
+    "                  'attention_mask': batch[1],\n",
+    "                  'labels':         batch[2],\n",
+    "                 }       \n",
+    "\n",
+    "        outputs = model(**inputs)\n",
+    "        \n",
+    "        loss = outputs[0]\n",
+    "        loss_train_total += loss.item()\n",
+    "        loss.backward()\n",
+    "\n",
+    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
+    "\n",
+    "        optimizer.step()\n",
+    "        scheduler.step()\n",
+    "        \n",
+    "        progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item()/len(batch))})\n",
+    "    torch.save(model.state_dict(), f'finetuned_BERT_epoch_{epoch}.model')\n",
+    "        \n",
+    "    tqdm.write(f'\\nEpoch {epoch}')\n",
+    "    \n",
+    "    loss_train_avg = loss_train_total/len(train_loader)             \n",
+    "    tqdm.write(f'Training loss: {loss_train_avg}')\n",
+    "    \n",
+    "    val_loss, predictions, true_vals = eval(val_loader)\n",
+    "    tqdm.write(f'Validation loss: {val_loss}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e9f7735a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\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": [
+       "BertForSequenceClassification(\n",
+       "  (bert): BertModel(\n",
+       "    (embeddings): BertEmbeddings(\n",
+       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
+       "      (position_embeddings): Embedding(512, 768)\n",
+       "      (token_type_embeddings): Embedding(2, 768)\n",
+       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "      (dropout): Dropout(p=0.1, inplace=False)\n",
+       "    )\n",
+       "    (encoder): BertEncoder(\n",
+       "      (layer): ModuleList(\n",
+       "        (0-11): 12 x BertLayer(\n",
+       "          (attention): BertAttention(\n",
+       "            (self): BertSelfAttention(\n",
+       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (dropout): Dropout(p=0.1, inplace=False)\n",
+       "            )\n",
+       "            (output): BertSelfOutput(\n",
+       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "              (dropout): Dropout(p=0.1, inplace=False)\n",
+       "            )\n",
+       "          )\n",
+       "          (intermediate): BertIntermediate(\n",
+       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "            (intermediate_act_fn): GELUActivation()\n",
+       "          )\n",
+       "          (output): BertOutput(\n",
+       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "            (dropout): Dropout(p=0.1, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (pooler): BertPooler(\n",
+       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "      (activation): Tanh()\n",
+       "    )\n",
+       "  )\n",
+       "  (dropout): Dropout(p=0.1, inplace=False)\n",
+       "  (classifier): Linear(in_features=768, out_features=3, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model=BertForSequenceClassification.from_pretrained(\n",
+    "    'bert-base-uncased',\n",
+    "    num_labels=len(label_dict),\n",
+    "    output_attentions=False,\n",
+    "    output_hidden_states=False\n",
+    ")\n",
+    " \n",
+    "model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "2cb2cb43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.load_state_dict(torch.load('finetuned_BERT_epoch_2.model',map_location=torch.device('cpu')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "86053301",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss,predictions,true_vals=eval(val_loader,model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26089e26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.94487095,  2.4501007 , -2.4328873 ],\n",
+       "       [ 3.0208707 , -1.7925887 ,  0.409608  ],\n",
+       "       [-1.245395  ,  2.8607914 , -2.7080884 ],\n",
+       "       ...,\n",
+       "       [-0.13207848, -2.0695374 ,  3.5249124 ],\n",
+       "       [-1.0361273 , -2.475614  ,  3.9253955 ],\n",
+       "       [-0.3563956 , -2.541143  ,  3.703467  ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf20a5ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 1, ..., 2, 2, 2], dtype=int64)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds_flat = np.argmax(predictions, axis=1).flatten()\n",
+    "preds_flat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70d73cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 1, ..., 2, 2, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_vals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4d78070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy_per_class(preds, labels):\n",
+    "    label_dict_inverse = {v: k for k, v in label_dict.items()}\n",
+    "    \n",
+    "    preds_flat = np.argmax(preds, axis=1).flatten()\n",
+    "    labels_flat = labels.flatten()\n",
+    "\n",
+    "    for label in np.unique(labels_flat):\n",
+    "        y_preds = preds_flat[labels_flat==label]\n",
+    "        y_true = labels_flat[labels_flat==label]\n",
+    "        print(f'Class: {label_dict_inverse[label]}')\n",
+    "        print(f'Accuracy: {len(y_preds[y_preds==label])}/{len(y_true)}\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46eb06a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class: neutral\n",
+      "Accuracy: 1571/2195\n",
+      "\n",
+      "Class: negative\n",
+      "Accuracy: 1230/1563\n",
+      "\n",
+      "Class: positive\n",
+      "Accuracy: 1501/1739\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_per_class(predictions, true_vals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "284950e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.76      0.72      0.74      2195\n",
+      "           1       0.80      0.79      0.79      1563\n",
+      "           2       0.80      0.86      0.83      1739\n",
+      "\n",
+      "    accuracy                           0.78      5497\n",
+      "   macro avg       0.78      0.79      0.79      5497\n",
+      "weighted avg       0.78      0.78      0.78      5497\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(true_vals,preds_flat))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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