lstm.ipynb

{
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   "metadata": {},
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>subtask_a</th>\n",
       "      <th>subtask_b</th>\n",
       "      <th>subtask_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86426</td>\n",
       "      <td>@USER She should ask a few native Americans wh...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>UNT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90194</td>\n",
       "      <td>@USER @USER Go home youâre drunk!!! @USER #M...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>TIN</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16820</td>\n",
       "      <td>Amazon is investigating Chinese employees who ...</td>\n",
       "      <td>NOT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62688</td>\n",
       "      <td>@USER Someone should'veTaken\" this piece of sh...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>UNT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43605</td>\n",
       "      <td>@USER @USER Obama wanted liberals &amp;amp; illega...</td>\n",
       "      <td>NOT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              tweet subtask_a  \\\n",
       "0  86426  @USER She should ask a few native Americans wh...       OFF   \n",
       "1  90194  @USER @USER Go home youâre drunk!!! @USER #M...       OFF   \n",
       "2  16820  Amazon is investigating Chinese employees who ...       NOT   \n",
       "3  62688  @USER Someone should'veTaken\" this piece of sh...       OFF   \n",
       "4  43605  @USER @USER Obama wanted liberals &amp; illega...       NOT   \n",
       "\n",
       "  subtask_b subtask_c  \n",
       "0       UNT       NaN  \n",
       "1       TIN       IND  \n",
       "2       NaN       NaN  \n",
       "3       UNT       NaN  \n",
       "4       NaN       NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import timeit\n",
    "\n",
    "df = pd.read_csv('{Csv file directory}/texts.csv', encoding='latin-1',header=0)\n",
    "df.head(5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>subtask_a</th>\n",
       "      <th>subtask_b</th>\n",
       "      <th>subtask_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@USER She should ask a few native Americans wh...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>UNT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@USER @USER Go home youâre drunk!!! @USER #M...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>TIN</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazon is investigating Chinese employees who ...</td>\n",
       "      <td>NOT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@USER Someone should'veTaken\" this piece of sh...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>UNT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@USER @USER Obama wanted liberals &amp;amp; illega...</td>\n",
       "      <td>NOT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet subtask_a subtask_b  \\\n",
       "0  @USER She should ask a few native Americans wh...       OFF       UNT   \n",
       "1  @USER @USER Go home youâre drunk!!! @USER #M...       OFF       TIN   \n",
       "2  Amazon is investigating Chinese employees who ...       NOT       NaN   \n",
       "3  @USER Someone should'veTaken\" this piece of sh...       OFF       UNT   \n",
       "4  @USER @USER Obama wanted liberals &amp; illega...       NOT       NaN   \n",
       "\n",
       "  subtask_c  \n",
       "0       NaN  \n",
       "1       IND  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.columns[0], axis=1,inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>subtask_a</th>\n",
       "      <th>subtask_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@USER She should ask a few native Americans wh...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@USER @USER Go home youâre drunk!!! @USER #M...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazon is investigating Chinese employees who ...</td>\n",
       "      <td>NOT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@USER Someone should'veTaken\" this piece of sh...</td>\n",
       "      <td>OFF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@USER @USER Obama wanted liberals &amp;amp; illega...</td>\n",
       "      <td>NOT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet subtask_a subtask_c\n",
       "0  @USER She should ask a few native Americans wh...       OFF       NaN\n",
       "1  @USER @USER Go home youâre drunk!!! @USER #M...       OFF       IND\n",
       "2  Amazon is investigating Chinese employees who ...       NOT       NaN\n",
       "3  @USER Someone should'veTaken\" this piece of sh...       OFF       NaN\n",
       "4  @USER @USER Obama wanted liberals &amp; illega...       NOT       NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.columns[2], axis=1,inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>subtask_a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@USER She should ask a few native Americans wh...</td>\n",
       "      <td>OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@USER @USER Go home youâre drunk!!! @USER #M...</td>\n",
       "      <td>OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazon is investigating Chinese employees who ...</td>\n",
       "      <td>NOT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@USER Someone should'veTaken\" this piece of sh...</td>\n",
       "      <td>OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@USER @USER Obama wanted liberals &amp;amp; illega...</td>\n",
       "      <td>NOT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet subtask_a\n",
       "0  @USER She should ask a few native Americans wh...       OFF\n",
       "1  @USER @USER Go home youâre drunk!!! @USER #M...       OFF\n",
       "2  Amazon is investigating Chinese employees who ...       NOT\n",
       "3  @USER Someone should'veTaken\" this piece of sh...       OFF\n",
       "4  @USER @USER Obama wanted liberals &amp; illega...       NOT"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.columns[2], axis=1,inplace=True)\n",
    "df.head(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>subtask_a_NOT</th>\n",
       "      <th>subtask_a_OFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@USER She should ask a few native Americans wh...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@USER @USER Go home youâre drunk!!! @USER #M...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazon is investigating Chinese employees who ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@USER Someone should'veTaken\" this piece of sh...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@USER @USER Obama wanted liberals &amp;amp; illega...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  subtask_a_NOT  \\\n",
       "0  @USER She should ask a few native Americans wh...              0   \n",
       "1  @USER @USER Go home youâre drunk!!! @USER #M...              0   \n",
       "2  Amazon is investigating Chinese employees who ...              1   \n",
       "3  @USER Someone should'veTaken\" this piece of sh...              0   \n",
       "4  @USER @USER Obama wanted liberals &amp; illega...              1   \n",
       "\n",
       "   subtask_a_OFF  \n",
       "0              1  \n",
       "1              1  \n",
       "2              0  \n",
       "3              1  \n",
       "4              0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.get_dummies(df,columns=[\"subtask_a\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>subtask_a_OFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@USER She should ask a few native Americans wh...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@USER @USER Go home youâre drunk!!! @USER #M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazon is investigating Chinese employees who ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@USER Someone should'veTaken\" this piece of sh...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@USER @USER Obama wanted liberals &amp;amp; illega...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  subtask_a_OFF\n",
       "0  @USER She should ask a few native Americans wh...              1\n",
       "1  @USER @USER Go home youâre drunk!!! @USER #M...              1\n",
       "2  Amazon is investigating Chinese employees who ...              0\n",
       "3  @USER Someone should'veTaken\" this piece of sh...              1\n",
       "4  @USER @USER Obama wanted liberals &amp; illega...              0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.columns[1], axis=1,inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def text_cleaning(text):\n",
    "  text = re.sub(r'@[A-Za-z0-9]+', '', text)     # removing @mentions\n",
    "  text = re.sub(r'@[A-Za-zA-Z0-9]+', '', text)  # removing @mentions \n",
    "  text = re.sub(r'@[A-Za-z]+', '', text)        # removing @mentions\n",
    "  text = re.sub(r'@[-)]+', '', text)            # removing @mentions\n",
    "  text = re.sub(r'#', '', text )                # removing '#' sign\n",
    "  text = re.sub(r'RT[\\s]+', '', text)           # removing RT\n",
    "  text = re.sub(r'https?\\/\\/\\S+', '', text)     # removing the hyper link\n",
    "  text = re.sub(r'&[a-z;]+', '', text)          # removing '&gt;'\n",
    "\n",
    "  return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "\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>tweet</th>\n",
       "      <th>subtask_a_OFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>She should ask a few native Americans what th...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Go home youâre drunk!!!  MAGA Trump2020 ð...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazon is investigating Chinese employees who ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Someone should'veTaken\" this piece of shit to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Obama wanted liberals  illegals to move into...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  subtask_a_OFF\n",
       "0   She should ask a few native Americans what th...              1\n",
       "1    Go home youâre drunk!!!  MAGA Trump2020 ð...              1\n",
       "2  Amazon is investigating Chinese employees who ...              0\n",
       "3   Someone should'veTaken\" this piece of shit to...              1\n",
       "4    Obama wanted liberals  illegals to move into...              0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0] = df.iloc[:,0].apply(text_cleaning)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text:     Planned Parenthood provides millions with CONTRACEPTION - thereby eliminating an unplanned pregnancy for most; an unplanned pregnancy that doesnât potentially result in an abortion.  Conservatives possess no critical thinking skills whatsoever!\n",
      "Offensive:  0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df.iloc[:,0].values, df.iloc[:,1].values, test_size=0.30)\n",
    "\n",
    "print('Text: ', x_train[0])\n",
    "print('Offensive: ', y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of datatset vocab is:  17394\n"
     ]
    }
   ],
   "source": [
    "# converting the strings into integers using Tokenizer \n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences \n",
    "\n",
    "max_vocab = 10000\n",
    "tokenizer = Tokenizer(num_words=max_vocab)\n",
    "tokenizer.fit_on_texts(x_train)\n",
    "\n",
    "wordidx = tokenizer.word_index\n",
    "V = len(wordidx)\n",
    "print('The size of datatset vocab is: ', V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of training sequence is:  62\n",
      "The length of testing sequence is:  62\n"
     ]
    }
   ],
   "source": [
    "train_seq = tokenizer.texts_to_sequences(x_train)\n",
    "test_seq = tokenizer.texts_to_sequences(x_test)\n",
    "\n",
    "# padding the traing sequence\n",
    "pad_train = pad_sequences(train_seq)\n",
    "T = pad_train.shape[1]\n",
    "print('The length of training sequence is: ', T)\n",
    "\n",
    "# padding the test sequence\n",
    "pad_test = pad_sequences(test_seq, maxlen=T)\n",
    "print('The length of testing sequence is: ', pad_test.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "290/290 [==============================] - 8s 21ms/step - loss: 0.6407 - accuracy: 0.6634 - val_loss: 0.6259 - val_accuracy: 0.6772\n",
      "Epoch 2/5\n",
      "290/290 [==============================] - 6s 20ms/step - loss: 0.6356 - accuracy: 0.6636 - val_loss: 0.6219 - val_accuracy: 0.6772\n",
      "Epoch 3/5\n",
      "290/290 [==============================] - 6s 20ms/step - loss: 0.5814 - accuracy: 0.6972 - val_loss: 0.5176 - val_accuracy: 0.7528\n",
      "Epoch 4/5\n",
      "290/290 [==============================] - 6s 20ms/step - loss: 0.4022 - accuracy: 0.8235 - val_loss: 0.5192 - val_accuracy: 0.7654\n",
      "Epoch 5/5\n",
      "290/290 [==============================] - 6s 21ms/step - loss: 0.2875 - accuracy: 0.8864 - val_loss: 0.5907 - val_accuracy: 0.7487\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Input, Dense, Embedding, LSTM, GlobalMaxPooling1D\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "D = 20 \n",
    "M = 15\n",
    "\n",
    "i = Input (shape=(T, ))   \n",
    "x = Embedding(V+1, 20)(i)   \n",
    "x = LSTM(15, return_sequences=True)(x)\n",
    "x = GlobalMaxPooling1D()(x)\n",
    "x = Dense(32, activation='relu')(x)\n",
    "x = Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "model = Model(i,x)\n",
    "\n",
    "# compiling the model\n",
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# training the model\n",
    "r = model.fit(pad_train, y_train, validation_data=(pad_test, y_test), epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22ad1e91610>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r.history['accuracy'], label= 'accuracy')\n",
    "plt.plot(r.history['val_accuracy'], label='val_accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.87%\n"
     ]
    }
   ],
   "source": [
    "#model.save('offensive language.h5')\n",
    "scores = model.evaluate(pad_test, y_test, verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Offensive\n",
      "Time required for Execution ->  0.2807350000000497 seconds\n"
     ]
    }
   ],
   "source": [
    "def predict_sentiment(text):\n",
    "  start = timeit.default_timer()\n",
    "  text_seq = tokenizer.texts_to_sequences(text)\n",
    "  text_pad = pad_sequences(text_seq, maxlen=T)\n",
    "\n",
    "  predicted = model.predict(text_pad).round()\n",
    "\n",
    "  if predicted == 1.0:\n",
    "    print('Offensive')\n",
    "  else:\n",
    "    print('Not Offensive')\n",
    "  stop = timeit.default_timer()\n",
    "  execution_time = stop - start\n",
    "  print(\"Time required for Execution -> \", execution_time, \"seconds\")\n",
    "text = ['I am a student']\n",
    "predict_sentiment(text)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "63fd5069d213b44bf678585dea6b12cceca9941eaf7f819626cde1f2670de90d"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "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": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}