diff --git a/Next_Word_Prediction.ipynb b/Next_Word_Prediction.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yt8X6as3vrSN"
+      },
+      "source": [
+        "### Import the required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wCwrGkBONxCx",
+        "outputId": "aa91faf6-8259-4ced-9777-56d9115cb462"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -q datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "KUv8N5MCmPMq"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import re\n",
+        "\n",
+        "# datasets is a library that contains a lot of datasets that are useful and easy to use.\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "# Tokenizer from tensorflow is used to convert the text into tokens.\n",
+        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+        "# pad_sequences is used to make the length of all the sentences equal.\n",
+        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+        "# to_categorical is used to convert the labels into one-hot encoding.\n",
+        "from tensorflow.keras.utils import to_categorical\n",
+        "\n",
+        "# Required libraries for the model.\n",
+        "from tensorflow.keras.models import Sequential, load_model\n",
+        "from tensorflow.keras.layers import Embedding, Dense, LSTM\n",
+        "from tensorflow.keras.callbacks import EarlyStopping"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VcQaJuanv_kX"
+      },
+      "source": [
+        "### Load Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269,
+          "referenced_widgets": [
+            "ce79aefa8d2249e0860993c04bac6c0d",
+            "86eafc1ab818461c8f6cfcbf38f492ab",
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+            "27074ac17529468fa6eb08feb11649ad",
+            "10b6b319673540068a8e60b70e1448b7",
+            "23c56df304f74b39b4a054ac0f9f25e1",
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+            "9a0a0e8ecbe24b9b905d5eea8669b169",
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+            "4cad34139e6e46b8b09b0a54e1a58776"
+          ]
+        },
+        "id": "Uz41WgekmPMs",
+        "outputId": "21d8d4b8-77bb-4467-a11c-89ef8bff7bc7"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ce79aefa8d2249e0860993c04bac6c0d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/327 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4ec54523dc9c48fab9975debc52f139d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/38.0M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "50a9c2e2e2ee4e46b7d0a6b5e78b044d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/8.91M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d15a57c81ac0438f9e211d82182afa39",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Load the dataset from huggingface. https://huggingface.co/datasets/512duncanl/wh40k_novels\n",
+        "dataset = load_dataset(\"512duncanl/wh40k_novels\")\n",
+        "data = dataset['train']['text']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GJYpKi6awFaO"
+      },
+      "source": [
+        "### Pre-Process The Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "MZt7jjlEmPMu"
+      },
+      "outputs": [],
+      "source": [
+        "def preprocess_data(sentence):\n",
+        "    \"\"\"\n",
+        "    # Preprocess the data by removing special characters and converting to lowercase.\n",
+        "    Args:\n",
+        "        sentence (str): The sentence to be preprocessed.\n",
+        "    Returns:\n",
+        "        str: The preprocessed sentence.\n",
+        "    \"\"\"\n",
+        "    sentence = sentence.lower()\n",
+        "    sentence = re.sub(r'[^a-z0-9]', ' ', sentence)\n",
+        "    sentence = re.sub(' +', ' ', sentence)\n",
+        "\n",
+        "    return sentence\n",
+        "\n",
+        "def tokenize_data(total_words):\n",
+        "    '''\n",
+        "    # Tokenize the data\n",
+        "    Args:\n",
+        "        total_words (int): The total number of words to be used.\n",
+        "    Returns:\n",
+        "        tokenized_data (list): The tokenized data.\n",
+        "        index_to_word (dict): The index to word mapping.\n",
+        "        tokenizer (Tokenizer): The tokenizer object.\n",
+        "    '''\n",
+        "    cleaned_sentences = list(map(preprocess_data, data))\n",
+        "    cleaned_sentences = ' '.join(cleaned_sentences)[:total_words]\n",
+        "\n",
+        "    tokenizer = Tokenizer()\n",
+        "    tokenizer.fit_on_texts([cleaned_sentences])\n",
+        "    tokenized_data = tokenizer.texts_to_sequences([cleaned_sentences])[0]\n",
+        "    index_to_word = dict(zip(tokenizer.word_index.values(), tokenizer.word_index.keys()))\n",
+        "\n",
+        "    return tokenized_data, index_to_word, tokenizer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "8OwAyL7UmPMu"
+      },
+      "outputs": [],
+      "source": [
+        "tokenized_data, index_to_word, tokenizer = tokenize_data(500000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P2ty5jf4yl-X"
+      },
+      "source": [
+        "### Generate Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "Oo07BW1wyoL3"
+      },
+      "outputs": [],
+      "source": [
+        "def generate_data():\n",
+        "    '''\n",
+        "    # Generate the data\n",
+        "    Returns:\n",
+        "        input_data (numpy array): The input data.\n",
+        "        output_data (numpy array): The output data.\n",
+        "\n",
+        "    # Formula for generating the data\n",
+        "\n",
+        "    sample_data = [2, 5, 6, 1, 9, 11, 2, 3, 43]\n",
+        "\n",
+        "    sentence_length = 3\n",
+        "    num_words = 10\n",
+        "\n",
+        "    | input_data  | output_data |\n",
+        "    | ----------  | ----------- |\n",
+        "    | 2, 5, 6     |      1      |\n",
+        "    | 5, 6, 1     |      9      |\n",
+        "    | 6, 1, 9     |      11     |\n",
+        "    | 1, 9, 11    |      2      |\n",
+        "    | 9, 11, 2    |      3      |\n",
+        "    | 11, 2, 3    |      43     |\n",
+        "    '''\n",
+        "    input_data = []\n",
+        "    output_data = []\n",
+        "\n",
+        "    for i in range(0, len(tokenized_data) - sentence_length):\n",
+        "        input_data.append(tokenized_data[i:i+sentence_length])\n",
+        "        output_data.append(tokenized_data[i+sentence_length])\n",
+        "\n",
+        "    output_data = to_categorical(output_data, num_classes=num_words)\n",
+        "\n",
+        "    input_data = np.array(input_data)\n",
+        "    output_data = np.array(output_data)\n",
+        "\n",
+        "    return input_data, output_data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "4Xfrt1iAmPMv"
+      },
+      "outputs": [],
+      "source": [
+        "# num_words is the total number of unique words in the dataset.\n",
+        "num_words = len(tokenizer.word_index) + 1\n",
+        "\n",
+        "# sentence_length is the length of the input data.\n",
+        "sentence_length = 5\n",
+        "\n",
+        "input_data, output_data = generate_data()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4JyCJudWmPMv",
+        "outputId": "02161dae-c426-4340-8261-b02162a4f550"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "input_data shape : (92057, 5)\n",
+            "output_data shape : (92057, 9431)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"input_data shape : {input_data.shape}\")\n",
+        "print(f\"output_data shape : {output_data.shape}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train the Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "eOj0wKDKmPMv"
+      },
+      "outputs": [],
+      "source": [
+        "def rnn_model(optimizer, epochs):\n",
+        "    '''\n",
+        "    # Build the RNN model\n",
+        "    Args:\n",
+        "        optimizer (str): The optimizer to be used.\n",
+        "        epochs (int): The number of epochs to train the model.\n",
+        "    Returns:\n",
+        "        model (Sequential): The RNN model.\n",
+        "    '''\n",
+        "    # Initialize the model\n",
+        "    model = Sequential()\n",
+        "\n",
+        "    # Add the Embedding layer\n",
+        "    model.add(Embedding(input_dim=num_words, output_dim=300, input_length=sentence_length))\n",
+        "\n",
+        "    # Add LSTM layers\n",
+        "    model.add(LSTM(256, return_sequences=True))\n",
+        "    model.add(LSTM(128, return_sequences=True))\n",
+        "    model.add(LSTM(128))\n",
+        "\n",
+        "    # Add the Dense layer for output\n",
+        "    model.add(Dense(num_words, activation='softmax'))\n",
+        "\n",
+        "    # Compile the model\n",
+        "    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "    # Early Stopping with\n",
+        "    es = EarlyStopping(monitor='loss', patience=5)\n",
+        "    model.fit(input_data, output_data, epochs=epochs, callbacks=[es])\n",
+        "\n",
+        "    return model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "INDj7nEhmPMw",
+        "outputId": "d7504cbd-ce2a-4529-9107-bf73b0b7019b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/100\n",
+            "2877/2877 [==============================] - 361s 123ms/step - loss: 6.8955 - accuracy: 0.0711\n",
+            "Epoch 2/100\n",
+            "2877/2877 [==============================] - 353s 123ms/step - loss: 6.2301 - accuracy: 0.0936\n",
+            "Epoch 3/100\n",
+            "2877/2877 [==============================] - 347s 121ms/step - loss: 5.8023 - accuracy: 0.1190\n",
+            "Epoch 4/100\n",
+            "2877/2877 [==============================] - 343s 119ms/step - loss: 5.4664 - accuracy: 0.1334\n",
+            "Epoch 5/100\n",
+            "2877/2877 [==============================] - 345s 120ms/step - loss: 5.1482 - accuracy: 0.1500\n",
+            "Epoch 6/100\n",
+            "2877/2877 [==============================] - 340s 118ms/step - loss: 4.8358 - accuracy: 0.1652\n",
+            "Epoch 7/100\n",
+            "2877/2877 [==============================] - 344s 120ms/step - loss: 4.5168 - accuracy: 0.1861\n",
+            "Epoch 8/100\n",
+            "2877/2877 [==============================] - 354s 123ms/step - loss: 4.2030 - accuracy: 0.2136\n",
+            "Epoch 9/100\n",
+            "2877/2877 [==============================] - 364s 127ms/step - loss: 3.8964 - accuracy: 0.2457\n",
+            "Epoch 10/100\n",
+            "2877/2877 [==============================] - 353s 123ms/step - loss: 3.5888 - accuracy: 0.2847\n",
+            "Epoch 11/100\n",
+            "2877/2877 [==============================] - 349s 121ms/step - loss: 3.2933 - accuracy: 0.3278\n",
+            "Epoch 12/100\n",
+            "2877/2877 [==============================] - 355s 124ms/step - loss: 3.0079 - accuracy: 0.3735\n",
+            "Epoch 13/100\n",
+            "2877/2877 [==============================] - 354s 123ms/step - loss: 2.7404 - accuracy: 0.4219\n",
+            "Epoch 14/100\n",
+            "2877/2877 [==============================] - 358s 124ms/step - loss: 2.4856 - accuracy: 0.4718\n",
+            "Epoch 15/100\n",
+            "2877/2877 [==============================] - 358s 124ms/step - loss: 2.2506 - accuracy: 0.5172\n",
+            "Epoch 16/100\n",
+            "2877/2877 [==============================] - 367s 128ms/step - loss: 2.0310 - accuracy: 0.5632\n",
+            "Epoch 17/100\n",
+            "2877/2877 [==============================] - 355s 123ms/step - loss: 1.8253 - accuracy: 0.6048\n",
+            "Epoch 18/100\n",
+            "2877/2877 [==============================] - 359s 125ms/step - loss: 1.6458 - accuracy: 0.6433\n",
+            "Epoch 19/100\n",
+            "2877/2877 [==============================] - 362s 126ms/step - loss: 1.4803 - accuracy: 0.6795\n",
+            "Epoch 20/100\n",
+            "2877/2877 [==============================] - 365s 127ms/step - loss: 1.3292 - accuracy: 0.7131\n",
+            "Epoch 21/100\n",
+            "2877/2877 [==============================] - 369s 128ms/step - loss: 1.1919 - accuracy: 0.7416\n",
+            "Epoch 22/100\n",
+            "2877/2877 [==============================] - 350s 122ms/step - loss: 1.0723 - accuracy: 0.7672\n",
+            "Epoch 23/100\n",
+            "2877/2877 [==============================] - 354s 123ms/step - loss: 0.9675 - accuracy: 0.7899\n",
+            "Epoch 24/100\n",
+            "2877/2877 [==============================] - 361s 125ms/step - loss: 0.8720 - accuracy: 0.8126\n",
+            "Epoch 25/100\n",
+            "2877/2877 [==============================] - 355s 124ms/step - loss: 0.7902 - accuracy: 0.8297\n",
+            "Epoch 26/100\n",
+            "2877/2877 [==============================] - 352s 123ms/step - loss: 0.7132 - accuracy: 0.8477\n",
+            "Epoch 27/100\n",
+            "2877/2877 [==============================] - 357s 124ms/step - loss: 0.6545 - accuracy: 0.8593\n",
+            "Epoch 28/100\n",
+            "2877/2877 [==============================] - 353s 123ms/step - loss: 0.6009 - accuracy: 0.8704\n",
+            "Epoch 29/100\n",
+            "2877/2877 [==============================] - 358s 125ms/step - loss: 0.5537 - accuracy: 0.8809\n",
+            "Epoch 30/100\n",
+            "2877/2877 [==============================] - 355s 124ms/step - loss: 0.5166 - accuracy: 0.8878\n",
+            "Epoch 31/100\n",
+            "2877/2877 [==============================] - 355s 123ms/step - loss: 0.4791 - accuracy: 0.8956\n",
+            "Epoch 32/100\n",
+            "2877/2877 [==============================] - 358s 124ms/step - loss: 0.4491 - accuracy: 0.9027\n",
+            "Epoch 33/100\n",
+            "2877/2877 [==============================] - 367s 128ms/step - loss: 0.4302 - accuracy: 0.9060\n",
+            "Epoch 34/100\n",
+            "2877/2877 [==============================] - 360s 125ms/step - loss: 0.4036 - accuracy: 0.9103\n",
+            "Epoch 35/100\n",
+            "2877/2877 [==============================] - 360s 125ms/step - loss: 0.3875 - accuracy: 0.9140\n",
+            "Epoch 36/100\n",
+            "2877/2877 [==============================] - 363s 126ms/step - loss: 0.3725 - accuracy: 0.9163\n",
+            "Epoch 37/100\n",
+            "2877/2877 [==============================] - 346s 120ms/step - loss: 0.3562 - accuracy: 0.9195\n",
+            "Epoch 38/100\n",
+            "2877/2877 [==============================] - 359s 125ms/step - loss: 0.3426 - accuracy: 0.9217\n",
+            "Epoch 39/100\n",
+            "2877/2877 [==============================] - 359s 125ms/step - loss: 0.3305 - accuracy: 0.9239\n",
+            "Epoch 40/100\n",
+            "2877/2877 [==============================] - 366s 127ms/step - loss: 0.3234 - accuracy: 0.9256\n",
+            "Epoch 41/100\n",
+            "2877/2877 [==============================] - 370s 129ms/step - loss: 0.3216 - accuracy: 0.9242\n",
+            "Epoch 42/100\n",
+            "2877/2877 [==============================] - 362s 126ms/step - loss: 0.2991 - accuracy: 0.9307\n",
+            "Epoch 43/100\n",
+            "2877/2877 [==============================] - 363s 126ms/step - loss: 0.3052 - accuracy: 0.9264\n",
+            "Epoch 44/100\n",
+            "2877/2877 [==============================] - 372s 129ms/step - loss: 0.2992 - accuracy: 0.9286\n",
+            "Epoch 45/100\n",
+            "2877/2877 [==============================] - 366s 127ms/step - loss: 0.2885 - accuracy: 0.9300\n",
+            "Epoch 46/100\n",
+            "2877/2877 [==============================] - 359s 125ms/step - loss: 0.2909 - accuracy: 0.9286\n",
+            "Epoch 47/100\n",
+            "2877/2877 [==============================] - 364s 126ms/step - loss: 0.2797 - accuracy: 0.9326\n",
+            "Epoch 48/100\n",
+            "2877/2877 [==============================] - 362s 126ms/step - loss: 0.2852 - accuracy: 0.9289\n",
+            "Epoch 49/100\n",
+            "2877/2877 [==============================] - 354s 123ms/step - loss: 0.2809 - accuracy: 0.9292\n",
+            "Epoch 50/100\n",
+            "2877/2877 [==============================] - 354s 123ms/step - loss: 0.2701 - accuracy: 0.9330\n",
+            "Epoch 51/100\n",
+            "2877/2877 [==============================] - 356s 124ms/step - loss: 0.2800 - accuracy: 0.9299\n",
+            "Epoch 52/100\n",
+            "2877/2877 [==============================] - 354s 123ms/step - loss: 0.2709 - accuracy: 0.9313\n",
+            "Epoch 53/100\n",
+            "2877/2877 [==============================] - 356s 124ms/step - loss: 0.2715 - accuracy: 0.9308\n",
+            "Epoch 54/100\n",
+            "2877/2877 [==============================] - 366s 127ms/step - loss: 0.2617 - accuracy: 0.9336\n",
+            "Epoch 55/100\n",
+            "2877/2877 [==============================] - 361s 126ms/step - loss: 0.2606 - accuracy: 0.9345\n",
+            "Epoch 56/100\n",
+            "2877/2877 [==============================] - 361s 126ms/step - loss: 0.2595 - accuracy: 0.9326\n",
+            "Epoch 57/100\n",
+            "2877/2877 [==============================] - 355s 123ms/step - loss: 0.2612 - accuracy: 0.9324\n",
+            "Epoch 58/100\n",
+            "2877/2877 [==============================] - 357s 124ms/step - loss: 0.2585 - accuracy: 0.9331\n",
+            "Epoch 59/100\n",
+            "2877/2877 [==============================] - 354s 123ms/step - loss: 0.2672 - accuracy: 0.9304\n",
+            "Epoch 60/100\n",
+            "2877/2877 [==============================] - 355s 123ms/step - loss: 0.2681 - accuracy: 0.9302\n",
+            "Epoch 61/100\n",
+            "2877/2877 [==============================] - 355s 123ms/step - loss: 0.2592 - accuracy: 0.9325\n",
+            "Epoch 62/100\n",
+            "2877/2877 [==============================] - 348s 121ms/step - loss: 0.2574 - accuracy: 0.9339\n",
+            "Epoch 63/100\n",
+            "2877/2877 [==============================] - 352s 122ms/step - loss: 0.2571 - accuracy: 0.9326\n",
+            "Epoch 64/100\n",
+            "2877/2877 [==============================] - 355s 123ms/step - loss: 0.2544 - accuracy: 0.9326\n",
+            "Epoch 65/100\n",
+            "2877/2877 [==============================] - 350s 121ms/step - loss: 0.2545 - accuracy: 0.9329\n",
+            "Epoch 66/100\n",
+            "2877/2877 [==============================] - 349s 121ms/step - loss: 0.2523 - accuracy: 0.9336\n",
+            "Epoch 67/100\n",
+            "2877/2877 [==============================] - 346s 120ms/step - loss: 0.2512 - accuracy: 0.9336\n",
+            "Epoch 68/100\n",
+            "2877/2877 [==============================] - 340s 118ms/step - loss: 0.2575 - accuracy: 0.9312\n",
+            "Epoch 69/100\n",
+            "2877/2877 [==============================] - 364s 126ms/step - loss: 0.2541 - accuracy: 0.9326\n",
+            "Epoch 70/100\n",
+            "2877/2877 [==============================] - 355s 124ms/step - loss: 0.2538 - accuracy: 0.9328\n",
+            "Epoch 71/100\n",
+            "2877/2877 [==============================] - 357s 124ms/step - loss: 0.2535 - accuracy: 0.9323\n",
+            "Epoch 72/100\n",
+            "2877/2877 [==============================] - 352s 122ms/step - loss: 0.2526 - accuracy: 0.9318\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = rnn_model('adam', 100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uAZm5zCV0g-A",
+        "outputId": "0a077f38-5e35-4af5-d52c-665c1c9d91d2"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
+            "  saving_api.save_model(\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Save the model.\n",
+        "# model.save('./model/best_model.h5')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "YOvkQLDAYDTZ"
+      },
+      "outputs": [],
+      "source": [
+        "# Load the model.\n",
+        "# model = load_model('./model/best_model.h5')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "YJOU2hEpmPMw"
+      },
+      "outputs": [],
+      "source": [
+        "def text_generation(input_text, len_text):\n",
+        "    word = ''\n",
+        "    generated_text = input_text.lower()\n",
+        "    \n",
+        "    # Generate the text.\n",
+        "    for _ in range(len_text):\n",
+        "        # Tokenize the text.\n",
+        "        text = tokenizer.texts_to_sequences([generated_text])[0][-sentence_length:]\n",
+        "\n",
+        "        # Pad the text.\n",
+        "        text = pad_sequences([text], maxlen=5, padding='post')\n",
+        "\n",
+        "        # Predict the next word.\n",
+        "        predict = adam_model.predict(text)\n",
+        "\n",
+        "        # Get the prediction with the highest probability and convert it to a word.\n",
+        "        word = tokenizer.index_word[np.argmax(predict)]\n",
+        "\n",
+        "        # Append the word to the generated text.\n",
+        "        generated_text += ' ' + word\n",
+        "\n",
+        "        # Remove extra spaces.\n",
+        "        generated_text = re.sub(' +', ' ', generated_text)\n",
+        "\n",
+        "    return generated_text"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZK8TDh0VmPMw"
+      },
+      "outputs": [],
+      "source": [
+        "samples = [\n",
+        "    'The ships of the speartip',\n",
+        "    'Surface batteries smashed them out',\n",
+        "    'Just a week or two',\n",
+        "    \"But there's something I must\",\n",
+        "    'With a sound like the',\n",
+        "    'I really like to eat'\n",
+        "    ]\n",
+        "\n",
+        "output_texts = []\n",
+        "\n",
+        "for sample in samples:\n",
+        "    output_texts.append(text_generation(sample, 10))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3Ruiox1xmPMx",
+        "outputId": "872b44b4-e0d1-4709-ea04-31be82ec1192"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['the ships of the speartip slipped forward running under obscurement on board stormbirds were hauled',\n",
+              " 'surface batteries smashed them out of the heavens as the burning scads of debris from',\n",
+              " 'just a week or two before a sozzled second engineer had explained to karkasy that',\n",
+              " \"but there's something i must the deal of all or still worn deck his face\",\n",
+              " 'with a sound like the legion i believe that if he does even old he',\n",
+              " 'i really like to eat the time they knew he had switched out as a']"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "output_texts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Og9o6ZrbmPMx"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
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+      "display_name": "Python 3",
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